-
-LogoDet-3K[2] dataset is used for experiments. The dataset is fully labeled, with 3000 logo categories, about 200,000 high-quality manually labeled logo objects and 158,652 images.
-
-Since the dataset is original desigined for detection task, only the cropped logo area is used in the logo recognition stage. Therefore, the labeled bbox annotations are used to crop the logo area to form the training set, eliminating the influence of the background in the recognition stage. After cropping preprocessing, the dataset was splited to 155,427 images as training sets, covering 3000 logo categories (also used as the gallery during testing), and 3225 as test sets, which were used as query sets. The cropped dataset is available [download here](https://arxiv.org/abs/2008.05359)
-
-On this data, the single model Recall@1 Acc: 89.8%.
-
-## 3 References
-
-[1] Circle loss: A unified perspective of pair similarity optimization. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2020.
-
-[2] LogoDet-3K: A Large-Scale Image Dataset for Logo Detection[J]. arXiv preprint arXiv:2008.05359, 2020.
diff --git a/docs/en/application/mainbody_detection_en.md b/docs/en/application/mainbody_detection_en.md
deleted file mode 100644
index c4fbed6db..000000000
--- a/docs/en/application/mainbody_detection_en.md
+++ /dev/null
@@ -1,175 +0,0 @@
-# Mainbody Detection
-
-The mainbody detection technology is currently a very widely used detection technology, which refers to the detect one or some mainbody objects in the picture, crop the corresponding area in the image and carry out recognition, thereby completing the entire recognition process. Mainbody detection is the first step of the recognition task, which can effectively improve the recognition accuracy.
-
-
-This tutorial will introduce the dataset and model training for mainbody detection in PaddleClas.
-
-
-## 1. Dataset
-
-The datasets we used for mainbody detection task are shown in the following table.
-
-
-| Dataset | Image number | Image number used in <
- 
-
-
-`ppyolov2_r50vd_dcn_365e_coco.yml` depends on other configuration files, their meanings are as follows.
-
-
-```
-coco_detection.yml：num_class of the model, and train/eval/test dataset.
-
-runtime.yml：public runtime parameters, use_gpu, save_interval, etc.
-
-optimizer_365e.yml：learning rate and optimizer.
-
-ppyolov2_r50vd_dcn.yml：model architecture.
-
-ppyolov2_reader.yml：train/eval/test reader.
-```
-
-In mainbody detection task, you need to modify `num_classes` in `datasets/coco_detection.yml` to 1 (just `foreground` is included). Dataset path should also be updated.
-
-
-### 3.4 Begin the training process
-
-
-PaddleDetection supports many ways of training process.
-
-* Training using single GPU
-
-```bash
-# not needed for windows and Mac
-export CUDA_VISIBLE_DEVICES=0
-python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
-```
-
-* Training using multiple GPU's
-
-```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
-```
-
---eval：eval during training
-
-* (**Recommend**) Model finetune
-If you want to finetune the model on your own dataset, you can run the following command to train the model.
-
-```bash
-export CUDA_VISIBLE_DEVICES=0
-# assign pretrain_weights, load the general mainbody-detection pretrained model
-python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o pretrain_weights=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/ppyolov2_r50vd_dcn_mainbody_v1.0_pretrained.pdparams
-```
-
-
-* Resume training: you can use `-r` to load checkpoints and resume training.
-
-```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
-```
-
-Note:
-If error `out of memory` occured, you can try to decrease `batch_size` in `ppyolov2_reader.yml`.
-
-
-### 3.5 Model prediction
-
-Use the following command to finish the prediction process.
-
-
-
-```bash
-export CUDA_VISIBLE_DEVICES=0
-python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=your_image_path.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final
-```
-
-`--draw_threshold` is an optional parameter.
-
-### 3.6 Export model and inference.
-
-Use the following to export the inference model.
-
-```bash
-python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
-```
-
-The inference model will be saved folder `inference/ppyolov2_r50vd_dcn_365e_coco`, which contains `model.pdiparams`, `model.pdiparams.info`,`model.pdmodel` and `infer_cfg.yml`(optional for mainbody detection).
-
-* Note: Inference model name that `PaddleDetection` exports is `model.xxx`, here if you want to keep it consistent with `PaddleClas`, you can rename `model.xxx` to `inference.xxx` for subsequent inference.
-
-For more model export tutorial, please refer to [EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md).
-
-Now you get the newest model on your own dataset. In the recognition process, you can replace the detection model path with yours. For quick start of recognition process, please refer to the [tutorial](../tutorials/quick_start_recognition_en.md).
diff --git a/docs/en/application/object_detection_en.md b/docs/en/application/object_detection_en.md
deleted file mode 100644
index b99360533..000000000
--- a/docs/en/application/object_detection_en.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# General object detection
-
-## Practical Server-side detection method base on RCNN
-
-### Introduction
-
-
-* In recent years, object detection tasks have attracted widespread attention. [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) open-sourced the ResNet50_vd_SSLD pretrained model based on ImageNet(Top1 Acc 82.4%). And based on the pretrained model, PaddleDetection provided the PSS-DET (Practical Server-side detection) with the help of the rich operators in PaddleDetection. The inference speed can reach 61FPS on single V100 GPU when COCO mAP is 41.6%, and 20FPS when COCO mAP is 47.8%.
-
-* We take the standard `Faster RCNN ResNet50_vd FPN` as an example. The following table shows ablation study of PSS-DET.
-
-| Trick | Train scale | Test scale | COCO mAP | Infer speed/FPS |
-|- |:-: |:-: | :-: | :-: |
-| `baseline` | 640x640 | 640x640 | 36.4% | 43.589 |
-| +`test proposal=pre/post topk 500/300` | 640x640 | 640x640 | 36.2% | 52.512 |
-| +`fpn channel=64` | 640x640 | 640x640 | 35.1% | 67.450 |
-| +`ssld pretrain` | 640x640 | 640x640 | 36.3% | 67.450 |
-| +`ciou loss` | 640x640 | 640x640 | 37.1% | 67.450 |
-| +`DCNv2` | 640x640 | 640x640 | 39.4% | 60.345 |
-| +`3x, multi-scale training` | 640x640 | 640x640 | 41.0% | 60.345 |
-| +`auto augment` | 640x640 | 640x640 | 41.4% | 60.345 |
-| +`libra sampling` | 640x640 | 640x640 | 41.6% | 60.345 |
-
-
-Based on the ablation experiments, Cascade RCNN and larger inference scale(1000x1500) are used for better performance. The final COCO mAP is 47.8%
-and the following figure shows `mAP-Speed` curves for some common detectors.
-
-
-
-
-
-**Note**
-> For fair comparison, inference time for PSS-DET models on V100 GPU is transformed to Titan V GPU by multiplying by 1.2 times.
-
-For more detailed information, you can refer to [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_server_side_det).
-
-
-## Practical Mobile-side detection method base on RCNN
-
-* This part is comming soon!
diff --git a/docs/en/application/product_recognition_en.md b/docs/en/application/product_recognition_en.md
deleted file mode 100644
index fe5e6558c..000000000
--- a/docs/en/application/product_recognition_en.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Product Recognition
-
-Product recogniton is now widely used . The way of shopping by taking a photo has been adopted by many people. And the unmanned settlement platform has entered the major supermarkets, which is also supported by product recognition technology. The technology is about the process of "product detection + product identification". The product detection module is responsible for detecting potential product areas, and the product identification model is responsible for identifying the main body detected by the product detection module. The recognition module uses the retrieval method to get the similarity rank of product in database and the query image . This document mainly introduces the feature extraction part of product pictures.
-
-## 1 Pipeline
-
-See the pipline of [feature learning](./feature_learning_en.md) for details.
-
-The config file: [ResNet50_vd_Aliproduct.yaml](../../../ppcls/configs/Products/ResNet50_vd_Aliproduct.yaml)
-
- The details are as follows.
-
-### 1.1 Data Augmentation
-
-- `RandomCrop`: 224x224
-- `RandomFlip`
-- `Normlize`: normlize images to 0~1
-
-### 1.2 Backbone
-
-Using `ResNet50_vd` as the backbone, whicle is pretrained on ImageNet.
-
-### 1.3 Neck
-
- A 512 dimensional embedding FC layer without batchnorm and activation is used.
-
-### 1.4 Metric Learning Losses
-
- At present, `CELoss` is used. In order to obtain more robust features, other loss will be used for training in the future. Please look forward to it.
-
-## 2 Experiment
-
- This scheme is tested on Aliproduct [1] dataset. This dataset is an open source dataset of Tianchi competition, which is the largest open source product data set at present. It has more than 50000 identification categories and about 2.5 million training pictures.
-
- On this data, the single model Top1 Acc: 85.67%.
-
-## 3 References
-
-[1] Weakly Supervised Learning with Side Information for Noisy Labeled Images. ECCV, 2020.
diff --git a/docs/en/application/transfer_learning_en.md b/docs/en/application/transfer_learning_en.md
deleted file mode 100644
index 2aa0f22c8..000000000
--- a/docs/en/application/transfer_learning_en.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# Transfer learning in image classification
-
-Transfer learning is an important part of machine learning, which is widely used in various fields such as text and images. Here we mainly introduce transfer learning in the field of image classification, which is often called domain transfer, such as migration of the ImageNet classification model to the specified image classification task, such as flower classification.
-
-## Hyperparameter search
-
-ImageNet is the widely used dataset for image classification. A series of empirical hyperparameters have been summarized. High accuracy can be got using the hyperparameters. However, when applied in the specified dataset, the hyperparameters may not be optimal. There are two commonly used hyperparameter search methods that can be used to help us obtain better model hyperparameters.
-
-### Grid search
-
-For grid search, which is also called exhaustive search, the optimal value is determined by finding the best solution from all solutions in the search space. The method is simple and effective, but when the search space is large, it takes huge computing resource.
-
-### Bayesian search
-
-Bayesian search, which is also called Bayesian optimization, is realized by randomly selecting a group of hyperparameters in the search space. Gaussian process is used to update the hyperparameters, compute their expected mean and variance according to the performance of the previous hyperparameters. The larger the expected mean, the greater the probability of being close to the optimal solution. The larger the expected variance, the greater the uncertainty. Usually, the hyperparameter point with large expected mean is called `exporitation`, and the hyperparameter point with large variance is called `exploration`. Acquisition function is defined to balance the expected mean and variance. The currently selected hyperparameter point is viewed as the optimal position with maximum probability.
-
-According to the above two search schemes, we carry out some experiments based on fixed scheme and two search schemes on 8 open source datasets. As the experimental scheme in [1], we search for 4 hyperparameters, the search space and The experimental results are as follows:
-
-a fixed set of parameter experiments and two search schemes on 8 open source data sets. With reference to the experimental scheme of [1], we search for 4 hyperparameters, the search space and the experimental results are as follows:
-
-
-- Fixed scheme.
-
-```
-lr=0.003，l2 decay=1e-4，label smoothing=False，mixup=False
-```
-
-- Search space of the hyperparameters.
-
-```
-lr: [0.1, 0.03, 0.01, 0.003, 0.001, 0.0003, 0.0001]
-
-l2 decay: [1e-3, 3e-4, 1e-4, 3e-5, 1e-5, 3e-6, 1e-6]
-
-label smoothing: [False, True]
-
-mixup: [False, True]
-```
-
-It takes 196 times for grid search, and takes 10 times less for Bayesian search. The baseline is trained by using ImageNet1k pretrained model based on ResNet50_vd and fixed scheme. The follow shows the experiments.
-
-
-| Dataset | Fix scheme | Grid search | Grid search time | Bayesian search | Bayesian search time|
-| ------------------ | -------- | -------- | -------- | -------- | ---------- |
-| Oxford-IIIT-Pets | 93.64% | 94.55% | 196 | 94.04% | 20 |
-| Oxford-102-Flowers | 96.08% | 97.69% | 196 | 97.49% | 20 |
-| Food101 | 87.07% | 87.52% | 196 | 87.33% | 23 |
-| SUN397 | 63.27% | 64.84% | 196 | 64.55% | 20 |
-| Caltech101 | 91.71% | 92.54% | 196 | 92.16% | 14 |
-| DTD | 76.87% | 77.53% | 196 | 77.47% | 13 |
-| Stanford Cars | 85.14% | 92.72% | 196 | 92.72% | 25 |
-| FGVC Aircraft | 80.32% | 88.45% | 196 | 88.36% | 20 |
-
-
-- The above experiments verify that Bayesian search only reduces the accuracy by 0% to 0.4% under the condition of reducing the number of searches by about 10 times compared to grid search.
-- The search space can be expaned easily using Bayesian search.
-
-## Large-scale image classification
-
-In practical applications, due to the lack of training data, the classification model trained on the ImageNet1k data set is often used as the pretrained model for other image classification tasks. In order to further help solve practical problems, based on ResNet50_vd, Baidu open sourced a self-developed large-scale classification pretrained model, in which the training data contains 100,000 categories and 43 million pictures. The pretrained model can be downloaded as follows：[**download link**](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_10w_pretrained.pdparams)
-
-We conducted transfer learning experiments on 6 self-collected datasets,
-
-using a set of fixed parameters and a grid search method, in which the number of training rounds was set to 20epochs, the ResNet50_vd model was selected, and the ImageNet pre-training accuracy was 79.12%. The comparison results of the experimental data set parameters and model accuracy are as follows:
-
-
-Fixed scheme：
-
-```
-lr=0.001，l2 decay=1e-4，label smoothing=False，mixup=False
-```
-
-| Dataset | Statstics | **Pretrained moel on ImageNet
-Top-1(fixed)/Top-1(search)** | **Pretrained moel on large-scale dataset
Top-1(fixed)/Top-1(search)** | -| --------------- | ----------------------------------------- | -------------------------------------------------------- | --------------------------------------------------------- | -| Flowers | class:102
train:5789
valid:2396 | 0.7779/0.9883 | 0.9892/0.9954 | -| Hand-painted stick figures | Class:18
train:1007
valid:432 | 0.8795/0.9196 | 0.9107/0.9219 | -| Leaves | class:6
train:5256
valid:2278 | 0.8212/0.8482 | 0.8385/0.8659 | -| Container vehicle | Class:115
train:4879
valid:2094 | 0.6230/0.9556 | 0.9524/0.9702 | -| Chair | class:5
train:169
valid:78 | 0.8557/0.9688 | 0.9077/0.9792 | -| Geology | class:4
train:671
valid:296 | 0.5719/0.8094 | 0.6781/0.8219 | - -- The above experiments verified that for fixed parameters, compared with the pretrained model on ImageNet, using the large-scale classification model as a pretrained model can help us improve the model performance on a new dataset in most cases. Parameter search can be further helpful to the model performance. - -## Reference - -[1] Kornblith, Simon, Jonathon Shlens, and Quoc V. Le. "Do better imagenet models transfer better?." *Proceedings of the IEEE conference on computer vision and pattern recognition*. 2019. - -[2] Kolesnikov, Alexander, et al. "Large Scale Learning of General Visual Representations for Transfer." *arXiv preprint arXiv:1912.11370* (2019). diff --git a/docs/en/application/vehicle_recognition_en.md b/docs/en/application/vehicle_recognition_en.md deleted file mode 100644 index 0c1e611e5..000000000 --- a/docs/en/application/vehicle_recognition_en.md +++ /dev/null @@ -1,99 +0,0 @@ -# Vehicle Recognition - -This part mainly includes two parts: vehicle fine-grained classification and vehicle Reid. - -The goal of fine-grained classification is to recognize images belonging to multiple subordinate categories of a super-category, e.g., different species of animals/plants, different models of cars, different kinds of retail products. Obviously, fine-grained vehicle classification is to classify different sub categories of vehicles. - -Vehicle ReID aims to re-target vehicle images across non-overlapping camera views given a query image. It has many practical applications, such as for analyzing and managing the traffic flows in Intelligent Transport System. In this process, how to extract robust features is particularly important. - -In this document, the same training scheme is used to try the two application respectively. - -## 1 Pipeline - -See the pipline of [feature learning](./feature_learning_en.md) for details. - -The config file of Vehicle ReID: [ResNet50_ReID.yaml](../../../ppcls/configs/Vehicle/ResNet50_ReID.yaml). - -The config file of Vehicle fine-grained classification：[ResNet50.yaml](../../../ppcls/configs/Vehicle/ResNet50.yaml). - - The details are as follows. - -### 1.1 Data Augmentation - -Different from classification, this part mainly uses the following methods: - -- `Resize` to 224. Especially for ReID, the vehicle image is already croped using bbox by detector. So if `CenterCrop` is used, more vehicle information will be lost. -- [AugMix](https://arxiv.org/abs/1912.02781v1)：Simulation of lighting changes, camera position changes and other real scenes. -- [RandomErasing](https://arxiv.org/pdf/1708.04896v2.pdf)：Simulate occlusion. - -### 1.2 Backbone - - Using `ResNet50` as backbone, and make the following modifications: - -- Last stage stride = 1, keep the size of the final output feature map to 14x14. At the cost of increasing a small amount of calculation, the ability of feature expression is greatly improved. - -code：[ResNet50_last_stage_stride1](../../../ppcls/arch/backbone/variant_models/resnet_variant.py) - -### 1.3 Neck - -In order to reduce the complexity of calculating feature distance in inference, an embedding convolution layer is added, and the feature dimension is set to 512. - -### 1.4 Metric Learning Losses - -In vehicle ReID and vehicle fine-grained classification，[SupConLoss](../../../ppcls/loss/supconloss.py) , [ArcLoss](../../../ppcls/arch/gears/arcmargin.py) are used. The weight ratio of two losses is 1:1. - -## 2 Experiment - -### 2.1 Vehicle ReID - -
-
-This method is used in VERI-Wild dataset. This dataset was captured in a large CCTV monitoring system in an unrestricted scenario for a month (30 * 24 hours). The system consists of 174 cameras, which are distributed in large area of more than 200 square kilometers. The original vehicle image set contains 12 million vehicle images. After data cleaning and labeling, 416314 images and 40671 vehicle ids are collected. [See the paper for details]( https://github.com/PKU-IMRE/VERI-Wild).
-
-| **Methods** | **Small** | | |
-| :--------------------------: | :-------: | :-------: | :-------: |
-| | mAP | Top1 | Top5 |
-| Strong baesline(Resnet50)[1] | 76.61 | 90.83 | 97.29 |
-| HPGN(Resnet50+PGN)[2] | 80.42 | 91.37 | - |
-| GLAMOR(Resnet50+PGN)[3] | 77.15 | 92.13 | 97.43 |
-| PVEN(Resnet50)[4] | 79.8 | 94.01 | 98.06 |
-| SAVER(VAE+Resnet50)[5] | 80.9 | 93.78 | 97.93 |
-| PaddleClas baseline | 80.57 | **93.81** | **98.06** |
-
-### 2.2 Vehicle Fine-grained Classification
-
- In this applications, we use [CompCars](http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html) as train dataset.
-
-
-
-The images in the dataset mainly come from the network and monitoring data. The network data includes 163 automobile manufacturers and 1716 automobile models, which includes **136726** full vehicle images and **27618** partial vehicle images. The network car data includes the information of bounding box, perspective and five attributes (maximum speed, displacement, number of doors, number of seats and car type) for vehicles. The monitoring data includes **50000** front view images.
-
- It is worth noting that this dataset needs to generate labels according to its own needs. For example, in this demo, vehicles of the same model produced in different years are regarded as the same category. Therefore, the total number of categories is 431.
-
-| **Methods** | Top1 Acc |
-| :-----------------------------: | :--------: |
-| ResNet101-swp[6] | 97.6% |
-| Fine-Tuning DARTS[7] | 95.9% |
-| Resnet50 + COOC[8] | 95.6% |
-| A3M[9] | 95.4% |
-| PaddleClas baseline (ResNet50) | **97.37**% |
-
-## 3 References
-
-[1] Bag of Tricks and a Strong Baseline for Deep Person Re-Identification.CVPR workshop 2019.
-
-[2] Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification. In arXiv preprint arXiv:2005.14684
-
-[3] GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention. In arXiv preprint arXiv:2002.02256
-
-[4] Parsing-based view-aware embedding network for vehicle re-identification. CVPR 2020.
-
-[5] The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification. In ECCV 2020.
-
-[6] Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition. IEEE Transactions on Intelligent Transportation Systems, 2017.
-
-[7] Fine-Tuning DARTS for Image Classification. 2020.
-
-[8] Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning. 2018
-
-[9] Attribute-Aware Attention Model for Fine-grained Representation Learning. 2019.
diff --git a/docs/en/extension/paddle_mobile_inference_en.md b/docs/en/extension/paddle_mobile_inference_en.md
index 44beba110..86c8e040d 100644
--- a/docs/en/extension/paddle_mobile_inference_en.md
+++ b/docs/en/extension/paddle_mobile_inference_en.md
@@ -4,7 +4,7 @@
[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) is a set of lightweight inference engine which is fully functional, easy to use and then performs well. Lightweighting is reflected in the use of fewer bits to represent the weight and activation of the neural network, which can greatly reduce the size of the model, solve the problem of limited storage space of the mobile device, and the inference speed is better than other frameworks on the whole.
-In [PaddleClas](https://github.com/PaddlePaddle/PaddleClas), we uses Paddle-Lite to [evaluate the performance on the mobile device](../models/Mobile.md), in this section we uses the `MobileNetV1` model trained on the `ImageNet1k` dataset as an example to introduce how to use `Paddle-Lite` to evaluate the model speed on the mobile terminal (evaluated on SD855)
+In [PaddleClas](https://github.com/PaddlePaddle/PaddleClas), we uses Paddle-Lite to [evaluate the performance on the mobile device](../models/Mobile_en.md), in this section we uses the `MobileNetV1` model trained on the `ImageNet1k` dataset as an example to introduce how to use `Paddle-Lite` to evaluate the model speed on the mobile terminal (evaluated on SD855)
## Evaluation Steps
diff --git a/docs/en/extension/paddle_quantization_en.md b/docs/en/extension/paddle_quantization_en.md
index f5cedcefe..e84e3a820 100644
--- a/docs/en/extension/paddle_quantization_en.md
+++ b/docs/en/extension/paddle_quantization_en.md
@@ -9,4 +9,4 @@ By using this toolkit, [PaddleClas](https://github.com/PaddlePaddle/PaddleClas)
After quantized, the prediction speed is accelerated from 19.308ms to 14.395ms on SD855.
The storage size is reduced from 21M to 10M.
The top1 recognition accuracy rate is 75.9%.
-For specific training methods, please refer to [PaddleSlim quant aware](../../../deploy/slim/quant/README_en.md)。
+For specific training methods, please refer to [PaddleSlim quant aware](../../../deploy/slim/README_en.md)。
diff --git a/docs/en/image_recognition_pipeline/feature_extraction_en.md b/docs/en/image_recognition_pipeline/feature_extraction_en.md
index e5d0f49b7..4ff01f515 100644
--- a/docs/en/image_recognition_pipeline/feature_extraction_en.md
+++ b/docs/en/image_recognition_pipeline/feature_extraction_en.md
@@ -33,7 +33,7 @@ Functions of the above modules :
## 3.General Recognition Models
-In PP-Shitu, we have [PP_LCNet_x2_5](../models/PP-LCNet.md) as the backbone network, Linear Layer for Neck, [ArcMargin](../../../ppcls/arch/gears/arcmargin.py) for Head, and CELoss for Loss. See the details in [General Recognition_configuration files](../.././ppcls/configs/GeneralRecognition/). The involved training data covers the following seven public datasets:
+In PP-Shitu, we have [PP_LCNet_x2_5](../models/PP-LCNet.md) as the backbone network, Linear Layer for Neck, [ArcMargin](../../../ppcls/arch/gears/arcmargin.py) for Head, and CELoss for Loss. See the details in [General Recognition_configuration files](../../../ppcls/configs/GeneralRecognition/). The involved training data covers the following seven public datasets:
| Datasets | Data Size | Class Number | Scenarios | URL |
| ------------ | --------- | ------------ | ------------------ | ------------------------------------------------------------ |
diff --git a/docs/en/image_recognition_pipeline/mainbody_detection_en.md b/docs/en/image_recognition_pipeline/mainbody_detection_en.md
index 2cd10f603..9ff310ee0 100644
--- a/docs/en/image_recognition_pipeline/mainbody_detection_en.md
+++ b/docs/en/image_recognition_pipeline/mainbody_detection_en.md
@@ -227,7 +227,7 @@ The final directory contains `inference/ppyolov2_r50vd_dcn_365e_coco`, `inferen
After exporting the model, the path of the detection model can be changed to the inference model path to complete the prediction task.
-Take product recognition as an example，you can modify the field `Global.det_inference_model_dir` in its config file [inference_product.yaml](../../../deploy/configs/inference_product.yaml) to the directory of exported inference model, and then finish the detection and recognition of the product with reference to [Quick Start for Image Recognition](./quick_start/quick_start_recognition_en.md).
+Take product recognition as an example，you can modify the field `Global.det_inference_model_dir` in its config file [inference_product.yaml](../../../deploy/configs/inference_product.yaml) to the directory of exported inference model, and then finish the detection and recognition of the product with reference to [Quick Start for Image Recognition](../quick_start/quick_start_recognition_en.md).
## FAQ
diff --git a/docs/en/inference_deployment/cpp_deploy_en.md b/docs/en/inference_deployment/cpp_deploy_en.md
index 81470c8a2..1b65ebf62 100644
--- a/docs/en/inference_deployment/cpp_deploy_en.md
+++ b/docs/en/inference_deployment/cpp_deploy_en.md
@@ -26,8 +26,6 @@ This tutorial will introduce the detailed steps of deploying the PaddleClas clas
- Linux, docker is recommended.
- Windows, compilation based on `Visual Studio 2019 Community` is supported. In addition, you can refer to [How to use PaddleDetection to make a complete project](https://zhuanlan.zhihu.com/p/145446681) to compile by generating the `sln solution`.
- This document mainly introduces the compilation and inference of PaddleClas using C++ in Linux environment.
-- If you need to use the Inference Library in Windows environment, please refer to [The compilation tutorial in Windows](./docs/windows_vs2019_build.md) for detailed information.
-
### 1.1 Compile opencv
@@ -254,7 +252,7 @@ After executing the above commands, the dynamic link libraries (`libcls.so` and
### 3.1 Prepare the inference model
-* You can refer to [Model inference](../../tools/export_model.py)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
+* You can refer to [Model inference](../../../tools/export_model.py)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
```
inference/
diff --git a/docs/en/inference_deployment/export_model_en.md b/docs/en/inference_deployment/export_model_en.md
index fb3d8ce31..8fe3c46ad 100644
--- a/docs/en/inference_deployment/export_model_en.md
+++ b/docs/en/inference_deployment/export_model_en.md
@@ -95,9 +95,9 @@ The exporting model command will generate the following three files:
The inference model exported is used to deployment by using prediction engine. You can refer the following docs according to different deployment modes / platforms
-* [Python inference](./python_deploy.md)
-* [C++ inference](./cpp_deploy.md)(Only support classification)
-* [Python Whl inference](./whl_deploy.md)(Only support classification)
-* [PaddleHub Serving inference](./paddle_hub_serving_deploy.md)(Only support classification)
-* [PaddleServing inference](./paddle_serving_deploy.md)
-* [PaddleLite inference](./paddle_lite_deploy.md)(Only support classification)
+* [Python inference](./python_deploy_en.md)
+* [C++ inference](./cpp_deploy_en.md)(Only support classification)
+* [Python Whl inference](./whl_deploy_en.md)(Only support classification)
+* [PaddleHub Serving inference](./paddle_hub_serving_deploy_en.md)(Only support classification)
+* [PaddleServing inference](./paddle_serving_deploy_en.md)
+* [PaddleLite inference](./paddle_lite_deploy_en.md)(Only support classification)
diff --git a/docs/en/inference_deployment/paddle_hub_serving_deploy_en.md b/docs/en/inference_deployment/paddle_hub_serving_deploy_en.md
index f143d314b..56e68faad 100644
--- a/docs/en/inference_deployment/paddle_hub_serving_deploy_en.md
+++ b/docs/en/inference_deployment/paddle_hub_serving_deploy_en.md
@@ -16,6 +16,9 @@ PaddleClas supports rapid service deployment through Paddlehub. At present, it s
- [6. Send prediction requests](#6)
- [7. User defined service module modification](#7)
+
+## 1. Introduction
+
HubServing service pack contains 3 files, the directory is as follows:
```
@@ -45,7 +48,7 @@ Before installing the service module, you need to prepare the inference model an
**Notice**:
* The model file path can be viewed and modified in `PaddleClas/deploy/hubserving/clas/params.py`.
* It should be noted that the prefix of model structure file and model parameters file must be `inference`.
-* More models provided by PaddleClas can be obtained from the [model library](../../docs/en/models/models_intro_en.md). You can also use models trained by yourself.
+* More models provided by PaddleClas can be obtained from the [model library](../models/models_intro_en.md). You can also use models trained by yourself.
## 4. Install Service Module
@@ -230,4 +233,4 @@ Common parameters can be modified in params.py:
'class_id_map_file':
```
-In order to avoid unnecessary delay and be able to predict in batch, the preprocessing (include resize, crop and other) is completed in the client, so modify [test_hubserving.py](../../deploy/hubserving/test_hubserving.py#L35-L52) if necessary.
+In order to avoid unnecessary delay and be able to predict in batch, the preprocessing (include resize, crop and other) is completed in the client, so modify [test_hubserving.py](../../../deploy/hubserving/test_hubserving.py#L35-L52) if necessary.
diff --git a/docs/en/inference_deployment/paddle_lite_deploy_en.md b/docs/en/inference_deployment/paddle_lite_deploy_en.md
index 6bb5616d1..b584aafdc 100644
--- a/docs/en/inference_deployment/paddle_lite_deploy_en.md
+++ b/docs/en/inference_deployment/paddle_lite_deploy_en.md
@@ -4,7 +4,7 @@ This tutorial will introduce how to use [Paddle-Lite](https://github.com/PaddleP
Paddle-Lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for mobile-side deployment issues.
-If you only want to test speed, please refer to [The tutorial of Paddle-Lite mobile-side benchmark test](../../docs/zh_CN/extension/paddle_mobile_inference.md).
+If you only want to test speed, please refer to [The tutorial of Paddle-Lite mobile-side benchmark test](../extension/paddle_mobile_inference_en.md).
---
@@ -127,7 +127,7 @@ git checkout develop
After the compilation is complete, the `opt` file is located under `build.opt/lite/api/`.
-`opt` tool is used in the same way as `paddle_lite_opt` , please refer to [4.1](#4.1).
+`opt` tool is used in the same way as `paddle_lite_opt` , please refer to [2.1.1](#2.1.1).
### 2.1.3 Demo of get the optimized model
@@ -202,7 +202,7 @@ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
The `prepare.sh` take `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` as the test image, and copy it to the `demo/cxx/clas/debug/` directory.
-You should put the model that optimized by `paddle_lite_opt` under the `demo/cxx/clas/debug/` directory. In this example, use `MobileNetV3_large_x1_0.nb` model file generated in [2.1.3](#4.3).
+You should put the model that optimized by `paddle_lite_opt` under the `demo/cxx/clas/debug/` directory. In this example, use `MobileNetV3_large_x1_0.nb` model file generated in [2.1.3](#2.1.3).
The structure of the clas demo is as follows after the above command is completed:
diff --git a/docs/en/inference_deployment/python_deploy_en.md b/docs/en/inference_deployment/python_deploy_en.md
index 8096cc5cb..22dd4e609 100644
--- a/docs/en/inference_deployment/python_deploy_en.md
+++ b/docs/en/inference_deployment/python_deploy_en.md
@@ -139,4 +139,4 @@ python python/predict_rec.py -c configs/inference_rec.yaml -o Global.use_gpu=Fa
## 4. Concatenation of mainbody detection, feature extraction and vector search
- Please refer to [Quick Start of Recognition](./tutorials/quick_start_recognition_en.md)
+ Please refer to [Quick Start of Recognition](../quick_start/quick_start_recognition_en.md)
diff --git a/docs/en/inference_deployment/whl_deploy_en.md b/docs/en/inference_deployment/whl_deploy_en.md
index eea7ef771..e97cbfd7b 100644
--- a/docs/en/inference_deployment/whl_deploy_en.md
+++ b/docs/en/inference_deployment/whl_deploy_en.md
@@ -37,17 +37,17 @@ pip3 install dist/*
## 2. Quick Start
-* Using the `ResNet50` model provided by PaddleClas, the following image(`'docs/images/whl/demo.jpg'`) as an example.
+* Using the `ResNet50` model provided by PaddleClas, the following image(`'docs/images/inference_deployment/whl_demo.jpg'`) as an example.
-
+
* Python
```python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
-infer_imgs='docs/images/whl/demo.jpg'
+infer_imgs='docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))
```
@@ -61,12 +61,12 @@ print(next(result))
* CLI
```bash
-paddleclas --model_name=ResNet50 --infer_imgs="docs/images/whl/demo.jpg"
+paddleclas --model_name=ResNet50 --infer_imgs="docs/images/inference_deployment/whl_demo.jpg"
```
```
>>> result
-filename: docs/images/whl/demo.jpg, top-5, class_ids: [8, 7, 136, 80, 84], scores: [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], label_names: ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']
+filename: docs/images/inference_deployment/whl_demo.jpg, top-5, class_ids: [8, 7, 136, 80, 84], scores: [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], label_names: ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']
Predict complete!
```
@@ -95,7 +95,7 @@ The following parameters can be specified in Command Line or used as parameters
* CLI:
```bash
from paddleclas import PaddleClas, get_default_confg
-paddleclas --model_name=ViT_base_patch16_384 --infer_imgs='docs/images/whl/demo.jpg' --resize_short=384 --crop_size=384
+paddleclas --model_name=ViT_base_patch16_384 --infer_imgs='docs/images/inference_deployment/whl_demo.jpg' --resize_short=384 --crop_size=384
```
* Python:
@@ -127,14 +127,14 @@ You can use the inference model provided by PaddleClas to predict, and only need
```python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
-infer_imgs = 'docs/images/whl/demo.jpg'
+infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))
```
* CLI
```bash
-paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/demo.jpg'
+paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg'
```
@@ -145,14 +145,14 @@ You can use the local model files trained by yourself to predict, and only need
```python
from paddleclas import PaddleClas
clas = PaddleClas(inference_model_dir='./inference/')
-infer_imgs = 'docs/images/whl/demo.jpg'
+infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))
```
* CLI
```bash
-paddleclas --inference_model_dir='./inference/' --infer_imgs='docs/images/whl/demo.jpg'
+paddleclas --inference_model_dir='./inference/' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg'
```
@@ -182,26 +182,26 @@ You can predict the Internet image, only need to specify URL of Internet image b
```python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
-infer_imgs = 'https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/whl/demo.jpg'
+infer_imgs = 'https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))
```
* CLI
```bash
-paddleclas --model_name='ResNet50' --infer_imgs='https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/whl/demo.jpg'
+paddleclas --model_name='ResNet50' --infer_imgs='https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/inference_deployment/whl_demo.jpg'
```
### 4.6 Prediction of NumPy.array format image
-In Python code, you can predict the NumPy.array format image, only need to use the `infer_imgs` to transfer variable of image data. Note that the image data must be 3 channels.
+In Python code, you can predict the `NumPy.array` format image, only need to use the `infer_imgs` to transfer variable of image data. Note that the models in PaddleClas only support to predict 3 channels image data, and channels order is `RGB`.
* python
```python
import cv2
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
-infer_imgs = cv2.imread("docs/images/whl/demo.jpg")
+infer_imgs = cv2.imread("docs/en/inference_deployment/whl_deploy_en.md")[:, :, ::-1]
result=clas.predict(infer_imgs)
print(next(result))
```
@@ -214,14 +214,14 @@ You can save the prediction result(s) as pre-label, only need to use `pre_label_
```python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', save_dir='./output_pre_label/')
-infer_imgs = 'docs/images/whl/' # it can be infer_imgs folder path which contains all of images you want to predict.
+infer_imgs = 'docs/images/inference_deployment/whl_' # it can be infer_imgs folder path which contains all of images you want to predict.
result=clas.predict(infer_imgs)
print(next(result))
```
* CLI
```bash
-paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/' --save_dir='./output_pre_label/'
+paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_' --save_dir='./output_pre_label/'
```
@@ -247,12 +247,12 @@ For example:
```python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt')
-infer_imgs = 'docs/images/whl/demo.jpg'
+infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))
```
* CLI
```bash
-paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/demo.jpg' --class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt'
+paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg' --class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt'
```
diff --git a/docs/en/installation/install_paddle_en.md b/docs/en/installation/install_paddle_en.md
index c66b8a0e1..3922b7325 100644
--- a/docs/en/installation/install_paddle_en.md
+++ b/docs/en/installation/install_paddle_en.md
@@ -11,6 +11,8 @@
At present, **PaddleClas** requires **PaddlePaddle** version **>=2.0**. Docker is recomended to run Paddleclas, for more detailed information about docker and nvidia-docker, you can refer to the [tutorial](https://docs.docker.com/get-started/). If you do not want to use docker, you can skip section [2. (Recommended) Prepare a docker environment](#2), and go into section [3. Install PaddlePaddle using pip](#3).
+
+
## 1. Environment requirements
- python 3.x
diff --git a/docs/en/models/PP-LCNet_en.md b/docs/en/models/PP-LCNet_en.md
index bc1e07596..0fae0bf8c 100644
--- a/docs/en/models/PP-LCNet_en.md
+++ b/docs/en/models/PP-LCNet_en.md
@@ -56,7 +56,7 @@ The SE module is a channel attention mechanism proposed by SENet, which can effe
The option in the third row of the table was chosen for the location of the SE module in PP-LCNet.
-
+
### 3.3 Larger Convolution Kernels
In the paper of MixNet, the author analyzes the effect of convolutional kernel size on model performance and concludes that larger convolutional kernels within a certain range can improve the performance of the model, but beyond this range will be detrimental to the model’s performance. So the author forms MixConv with split-concat paradigm combined, which can improve the performance of the model but is not conducive to inference. We experimentally summarize the role of some larger convolutional kernels at different positions that are similar to those of the SE module, and find that larger convolutional kernels display more prominent roles in the middle and tail of the network. The following table shows the effect of the position of the 5x5 convolutional kernels on the accuracy：
diff --git a/docs/en/models_training/recognition_en.md b/docs/en/models_training/recognition_en.md
index cb35e548f..7ea65b820 100644
--- a/docs/en/models_training/recognition_en.md
+++ b/docs/en/models_training/recognition_en.md
@@ -138,6 +138,8 @@ cd ../../
For training and evaluation on a single GPU, the `tools/train.py` and `tools/eval.py` scripts are recommended.
+
+
#### 2.2.1 Model Training
Once you have prepared the configuration file, you can start training the image retrieval task in the following way. the method used by PaddleClas to train the image retrieval is metric learning, referring to [metric learning](#metric learning) for more explanations.
@@ -188,6 +190,8 @@ Loss:
The final total Loss is a weighted sum of all Losses, where weight defines the weight of a particular Loss in the final total. If you want to replace other Losses, you can also change the Loss field in the configuration file, for the currently supported Losses please refer to [Loss](../../../ppcls/loss).
+
+
#### 2.2.2 Resume Training
If the training task is terminated for some reasons, it can be recovered by loading the checkpoints weights file and continue training:
@@ -226,6 +230,8 @@ There is no need to modify the configuration file, just set the `Global.checkpoi
.
```
+
+
#### 2.2.3 Model Evaluation
Model evaluation can be carried out with the following commands.
diff --git a/docs/en/others/paddle_mobile_inference_en.md b/docs/en/others/paddle_mobile_inference_en.md
index f425b5465..153c96ffa 100644
--- a/docs/en/others/paddle_mobile_inference_en.md
+++ b/docs/en/others/paddle_mobile_inference_en.md
@@ -16,7 +16,7 @@
[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) is a set of lightweight inference engine which is fully functional, easy to use and then performs well. Lightweighting is reflected in the use of fewer bits to represent the weight and activation of the neural network, which can greatly reduce the size of the model, solve the problem of limited storage space of the mobile device, and the inference speed is better than other frameworks on the whole.
-In [PaddleClas](https://github.com/PaddlePaddle/PaddleClas), we uses Paddle-Lite to [evaluate the performance on the mobile device](../models/Mobile.md), in this section we uses the `MobileNetV1` model trained on the `ImageNet1k` dataset as an example to introduce how to use `Paddle-Lite` to evaluate the model speed on the mobile terminal (evaluated on SD855)
+In [PaddleClas](https://github.com/PaddlePaddle/PaddleClas), we uses Paddle-Lite to [evaluate the performance on the mobile device](../models/Mobile_en.md), in this section we uses the `MobileNetV1` model trained on the `ImageNet1k` dataset as an example to introduce how to use `Paddle-Lite` to evaluate the model speed on the mobile terminal (evaluated on SD855)
## 2. Evaluation Steps
diff --git a/docs/en/tutorials/quick_start_recognition_en.md b/docs/en/quick_start/quick_start_recognition_en.md
similarity index 90%
rename from docs/en/tutorials/quick_start_recognition_en.md
rename to docs/en/quick_start/quick_start_recognition_en.md
index 32ac9d3a3..aebc9154a 100644
--- a/docs/en/tutorials/quick_start_recognition_en.md
+++ b/docs/en/quick_start/quick_start_recognition_en.md
@@ -2,26 +2,26 @@
This tutorial contains 3 parts: Environment Preparation, Image Recognition Experience, and Unknown Category Image Recognition Experience.
-If the image category already exists in the image index database, then you can take a reference to chapter [Image Recognition Experience](#image_recognition_experience)，to complete the progress of image recognition；If you wish to recognize unknow category image, which is not included in the index database，you can take a reference to chapter [Unknown Category Image Recognition Experience](#unkonw_category_image_recognition_experience)，to complete the process of creating an index to recognize it。
+If the image category already exists in the image index database, then you can take a reference to chapter [Image Recognition Experience](#2)，to complete the progress of image recognition；If you wish to recognize unknow category image, which is not included in the index database，you can take a reference to chapter [Unknown Category Image Recognition Experience](#3)，to complete the process of creating an index to recognize it。
## Catalogue
-* [1. Enviroment Preparation](#enviroment_preperation )
-* [2. Image Recognition Experience](#image_recognition_experience)
- * [2.1 Download and Unzip the Inference Model and Demo Data](#download_and_unzip_the_inference_model_and_demo_data)
- * [2.2 Product Recognition and Retrieval](#Product_recognition_and_retrival)
- * [2.2.1 Single Image Recognition](#recognition_of_single_image)
- * [2.2.2 Folder-based Batch Recognition](#folder_based_batch_recognition)
-* [3. Unknown Category Image Recognition Experience](#unkonw_category_image_recognition_experience)
+* [1. Enviroment Preparation](#1)
+* [2. Image Recognition Experience](#2)
+ * [2.1 Download and Unzip the Inference Model and Demo Data](#2.1)
+ * [2.2 Product Recognition and Retrieval](#2.2)
+ * [2.2.1 Single Image Recognition](#2.2.1)
+ * [2.2.2 Folder-based Batch Recognition](#2.2.2)
+* [3. Unknown Category Image Recognition Experience](#3)
* [3.1 Prepare for the new images and labels](#3.1)
- * [3.2 Build a new Index Library](#build_a_new_index_library)
- * [3.3 Recognize the Unknown Category Images](#Image_differentiation_based_on_the_new_index_library)
+ * [3.2 Build a new Index Library](#3.2)
+ * [3.3 Recognize the Unknown Category Images](#3.3)
-
+
## 1. Enviroment Preparation
-* Installation：Please take a reference to [Quick Installation ](./install_en.md)to configure the PaddleClas environment.
+* Installation：Please take a reference to [Quick Installation ](../installation/)to configure the PaddleClas environment.
* Using the following command to enter Folder `deploy`. All content and commands in this section need to be run in folder `deploy`.
@@ -29,7 +29,7 @@ If the image category already exists in the image index database, then you can t
cd deploy
```
-
+
## 2. Image Recognition Experience
The detection model with the recognition inference model for the 4 directions (Logo, Cartoon Face, Vehicle, Product), the address for downloading the test data and the address of the corresponding configuration file are as follows.
@@ -81,7 +81,7 @@ wget {Data download link} && tar -xf {Name of the tar archive}
```
-
+
### 2.1 Download and Unzip the Inference Model and Demo Data
Take the product recognition as an example, download the detection model, recognition model and product recognition demo data with the following commands.
@@ -136,7 +136,7 @@ If you want to use the lightweight generic recognition model, you need to re-ext
python3.7 python/build_gallery.py -c configs/build_product.yaml -o Global.rec_inference_model_dir=./models/general_PPLCNet_x2_5_lite_v1.0_infer
```
-
+
### 2.2 Product Recognition and Retrieval
Take the product recognition demo as an example to show the recognition and retrieval process (if you wish to try other scenarios of recognition and retrieval, replace the corresponding configuration file after downloading and unzipping the corresponding demo data and model to complete the prediction).
@@ -149,7 +149,7 @@ pip install faiss-cpu==1.7.1post2
If error happens when using `import faiss`, please uninstall `faiss` and reinstall it, especially on `Windows`.
-
+
#### 2.2.1 Single Image Recognition
@@ -187,7 +187,7 @@ The detection result is also saved in the folder `output`, for this image, the v
-
+
#### 2.2.2 Folder-based Batch Recognition
If you want to predict the images in the folder, you can directly modify the `Global.infer_imgs` field in the configuration file, or you can also modify the corresponding configuration through the following `-o` parameter.
@@ -217,7 +217,7 @@ All the visualization results are also saved in folder `output`.
Furthermore, the recognition inference model path can be changed by modifying the `Global.rec_inference_model_dir` field, and the path of the index to the index databass can be changed by modifying the `IndexProcess.index_dir` field.
-
+
## 3. Recognize Images of Unknown Category
To recognize the image `./recognition_demo_data_v1.1/test_product/anmuxi.jpg`, run the command as follows:
@@ -268,7 +268,7 @@ gallery/anmuxi/006.jpg Anmuxi Ambrosial Yogurt
Each line can be splited into two fields. The first field denotes the relative image path, and the second field denotes its label. The `delimiter` is `tab` here.
-
+
### 3.2 Build a new Index Base Library
Use the following command to build the index to accelerate the retrieval process after recognition.
@@ -280,8 +280,8 @@ python3.7 python/build_gallery.py -c configs/build_product.yaml -o IndexProcess.
Finally, the new index information is stored in the folder`./recognition_demo_data_v1.1/gallery_product/index_update`. Use the new index database for the above index.
-
-### 3.2 Recognize the Unknown Category Images
+
+### 3.3 Recognize the Unknown Category Images
To recognize the image `./recognition_demo_data_v1.1/test_product/anmuxi.jpg`, run the command as follows.
diff --git a/docs/en/tutorials/data_en.md b/docs/en/tutorials/data_en.md
deleted file mode 100644
index eae2e6f97..000000000
--- a/docs/en/tutorials/data_en.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# Data
-
----
-
-## Introducation
-This document introduces the preparation of ImageNet1k and flowers102
-
-## Dataset
-
-Dataset | train dataset size | valid dataset size | category |
-:------:|:---------------:|:---------------------:|:--------:|
-[flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)|1k | 6k | 102 |
-[ImageNet1k](http://www.image-net.org/challenges/LSVRC/2012/)|1.2M| 50k | 1000 |
-
-* Data format
-
-Please follow the steps mentioned below to organize data, include train_list.txt and val_list.txt
-
-```shell
-# delimiter: "space"
-# the following the content of train_list.txt
-train/n01440764/n01440764_10026.JPEG 0
-...
-
-# the following the content of val_list.txt
-val/ILSVRC2012_val_00000001.JPEG 65
-...
-```
-
-### ImageNet1k
-After downloading data, please organize the data dir as below
-
-```bash
-PaddleClas/dataset/ILSVRC2012/
-|_ train/
-| |_ n01440764
-| | |_ n01440764_10026.JPEG
-| | |_ ...
-| |_ ...
-| |
-| |_ n15075141
-| |_ ...
-| |_ n15075141_9993.JPEG
-|_ val/
-| |_ ILSVRC2012_val_00000001.JPEG
-| |_ ...
-| |_ ILSVRC2012_val_00050000.JPEG
-|_ train_list.txt
-|_ val_list.txt
-```
-
-### Flowers102 Dataset
-
-Download [Data](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/) then decompress:
-
-```shell
-jpg/
-setid.mat
-imagelabels.mat
-```
-
-Please put all the files under ```PaddleClas/dataset/flowers102```
-
-generate generate_flowers102_list.py and train_list.txt和val_list.txt
-
-```bash
-python generate_flowers102_list.py jpg train > train_list.txt
-python generate_flowers102_list.py jpg valid > val_list.txt
-
-```
-
-Please organize data dir as below
-
-```bash
-PaddleClas/dataset/flowers102/
-|_ jpg/
-| |_ image_03601.jpg
-| |_ ...
-| |_ image_02355.jpg
-|_ train_list.txt
-|_ val_list.txt
-```
diff --git a/docs/en/tutorials/getting_started_en.md b/docs/en/tutorials/getting_started_en.md
deleted file mode 100644
index fe8dca132..000000000
--- a/docs/en/tutorials/getting_started_en.md
+++ /dev/null
@@ -1,242 +0,0 @@
-# Getting Started
----
-Please refer to [Installation](install_en.md) to setup environment at first, and prepare flower102 dataset by following the instruction mentioned in the [Quick Start](quick_start_en.md).
-
-## 1. Training and Evaluation on CPU or Single GPU
-
-If training and evaluation are performed on CPU or single GPU, it is recommended to use the `tools/train.py` and `tools/eval.py`.
-For training and evaluation in multi-GPU environment on Linux, please refer to [2. Training and evaluation on Linux+GPU](#2-training-and-evaluation-on-linuxgpu).
-
-
-## 1.1 Model training
-
-After preparing the configuration file, The training process can be started in the following way.
-
-```
-python tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Arch.pretrained=False \
- -o Global.device=gpu
-```
-
-Among them, `-c` is used to specify the path of the configuration file, `-o` is used to specify the parameters needed to be modified or added, `-o Arch.pretrained=False` means to not using pre-trained models.
-`-o Global.device=gpu` means to use GPU for training. If you want to use the CPU for training, you need to set `Global.device` to `cpu`.
-
-
-Of course, you can also directly modify the configuration file to update the configuration. For specific configuration parameters, please refer to [Configuration Document](config_description_en.md).
-
-* The output log examples are as follows:
- * If mixup or cutmix is used in training, top-1 and top-k (default by 5) will not be printed in the log:
-
- ```
- ...
- epoch:0 , train step:20 , loss: 4.53660, lr: 0.003750, batch_cost: 1.23101 s, reader_cost: 0.74311 s, ips: 25.99489 images/sec, eta: 0:12:43
- ...
- END epoch:1 valid top1: 0.01569, top5: 0.06863, loss: 4.61747, batch_cost: 0.26155 s, reader_cost: 0.16952 s, batch_cost_sum: 10.72348 s, ips: 76.46772 images/sec.
- ...
- ```
-
- * If mixup or cutmix is not used during training, in addition to the above information, top-1 and top-k (The default is 5) will also be printed in the log:
-
- ```
- ...
- epoch:0 , train step:30 , top1: 0.06250, top5: 0.09375, loss: 4.62766, lr: 0.003728, batch_cost: 0.64089 s, reader_cost: 0.18857 s, ips: 49.93080 images/sec, eta: 0:06:18
- ...
- END epoch:0 train top1: 0.01310, top5: 0.04738, loss: 4.65124, batch_cost: 0.64089 s, reader_cost: 0.18857 s, batch_cost_sum: 13.45863 s, ips: 49.93080 images/sec.
- ...
- ```
-
-During training, you can view loss changes in real time through `VisualDL`, see [VisualDL](../extension/VisualDL_en.md) for details.
-
-### 1.2 Model finetuning
-
-After configuring the configuration file, you can finetune it by loading the pretrained weights, The command is as shown below.
-
-```
-python tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Arch.pretrained=True \
- -o Global.device=gpu
-```
-
-Among them, `-o Arch.pretrained` is used to set the address to load the pretrained weights. When using it, you need to replace it with your own pretrained weights' path, or you can modify the path directly in the configuration file. You can also set it into `True` to use pretrained weights that trained in ImageNet1k.
-
-We also provide a lot of pre-trained models trained on the ImageNet-1k dataset. For the model list and download address, please refer to the [model library overview](../models/models_intro_en.md).
-
-### 1.3 Resume Training
-
-If the training process is terminated for some reasons, you can also load the checkpoints to continue training.
-
-```
-python tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Global.checkpoints="./output/MobileNetV3_large_x1_0/epoch_5" \
- -o Global.device=gpu
-```
-
-The configuration file does not need to be modified. You only need to add the `Global.checkpoints` parameter during training, which represents the path of the checkpoints. The parameter weights, learning rate, optimizer and other information will be loaded using this parameter.
-
-**Note**:
-
-* The `-o Global.checkpoints` parameter does not need to include the suffix of the checkpoints. The above training command will generate the checkpoints as shown below during the training process. If you want to continue training from the epoch `5`, Just set the `Global.checkpoints` to `../output/MobileNetV3_large_x1_0/epoch_5`, PaddleClas will automatically fill in the `pdopt` and `pdparams` suffixes.
-
- ```shell
- output
- ├── MobileNetV3_large_x1_0
- │ ├── best_model.pdopt
- │ ├── best_model.pdparams
- │ ├── best_model.pdstates
- │ ├── epoch_1.pdopt
- │ ├── epoch_1.pdparams
- │ ├── epoch_1.pdstates
- .
- .
- .
- ```
-
-
-### 1.4 Model evaluation
-
-The model evaluation process can be started as follows.
-
-```bash
-python tools/eval.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
-```
-
-The above command will use `./configs/quick_start/MobileNetV3_large_x1_0.yaml` as the configuration file to evaluate the model `./output/MobileNetV3_large_x1_0/best_model`. You can also set the evaluation by changing the parameters in the configuration file, or you can update the configuration with the `-o` parameter, as shown above.
-
-Some of the configurable evaluation parameters are described as follows:
-* `Arch.name`: Model name
-* `Global.pretrained_model`: The path of the model file to be evaluated
-
-**Note:** If the model is a dygraph type, you only need to specify the prefix of the model file when loading the model, instead of specifying the suffix, such as [1.3 Resume Training](#13-resume-training).
-
-
-### 2. Training and evaluation on Linux+GPU
-
-If you want to run PaddleClas on Linux with GPU, it is highly recommended to use `paddle.distributed.launch` to start the model training script(`tools/train.py`) and evaluation script(`tools/eval.py`), which can start on multi-GPU environment more conveniently.
-
-### 2.1 Model training
-
-After preparing the configuration file, The training process can be started in the following way. `paddle.distributed.launch` specifies the GPU running card number by setting `gpus`:
-
-```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-
-python3 -m paddle.distributed.launch \
- --gpus="0,1,2,3" \
- tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml
-```
-
-The format of output log information is the same as above, see [1.1 Model training](#11-model-training) for details.
-
-### 2.2 Model finetuning
-
-After configuring the configuration file, you can finetune it by loading the pretrained weights, The command is as shown below.
-
-```
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-
-python3 -m paddle.distributed.launch \
- --gpus="0,1,2,3" \
- tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Arch.pretrained=True
-```
-
-Among them, `Arch.pretrained` is set to `True` or `False`. It also can be used to set the address to load the pretrained weights. When using it, you need to replace it with your own pretrained weights' path, or you can modify the path directly in the configuration file.
-
-There contains a lot of examples of model finetuning in [Quick Start](./quick_start_en.md). You can refer to this tutorial to finetune the model on a specific dataset.
-
-
-### 2.3 Resume Training
-
-If the training process is terminated for some reasons, you can also load the checkpoints to continue training.
-
-```
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-
-python3 -m paddle.distributed.launch \
- --gpus="0,1,2,3" \
- tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Global.checkpoints="./output/MobileNetV3_large_x1_0/epoch_5" \
- -o Global.device=gpu
-```
-
-The configuration file does not need to be modified. You only need to add the `Global.checkpoints` parameter during training, which represents the path of the checkpoints. The parameter weights, learning rate, optimizer and other information will be loaded using this parameter as described in [1.3 Resume training](#13-resume-training).
-
-### 2.4 Model evaluation
-
-The model evaluation process can be started as follows.
-
-```bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-python3 -m paddle.distributed.launch \
- tools/eval.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
-```
-
-About parameter description, see [1.4 Model evaluation](#14-model-evaluation) for details.
-
-
-## 3. Use the pre-trained model to predict
-After the training is completed, you can predict by using the pre-trained model obtained by the training, as follows:
-
-```python
-python3 tools/infer.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Infer.infer_imgs=dataset/flowers102/jpg/image_00001.jpg \
- -o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
-```
-
-Among them:
-+ `Infer.infer_imgs`: The path of the image file or folder to be predicted;
-+ `Global.pretrained_model`: Weight file path, such as `./output/MobileNetV3_large_x1_0/best_model`;
-
-## 4. Use the inference model to predict
-
-PaddlePaddle supports inference using prediction engines, which will be introduced next.
-
-Firstly, you should export inference model using `tools/export_model.py`.
-
-```bash
-python3 tools/export_model.py \
- -c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
- -o Global.pretrained_model=output/MobileNetV3_large_x1_0/best_model
-```
-
-Among them, `Global.pretrained_model` parameter is used to specify the model file path that does not need to include the file suffix name.
-
-The above command will generate the model structure file (`inference.pdmodel`) and the model weight file (`inference.pdiparams`), and then the inference engine can be used for inference:
-
-Go to the deploy directory:
-
-```
-cd deploy
-```
-
-Using inference engine to inference. Because the mapping file of ImageNet1k dataset is used by default, we should set `PostProcess.Topk.class_id_map_file` into `None`.
-
-```bash
-python3 python/predict_cls.py \
- -c configs/inference_cls.yaml \
- -o Global.infer_imgs=../dataset/flowers102/jpg/image_00001.jpg \
- -o Global.inference_model_dir=../inference/ \
- -o PostProcess.Topk.class_id_map_file=None
-```
-Among them:
-+ `Global.infer_imgs`: The path of the image file to be predicted;
-+ `Global.inference_model_dir`: Model structure file path, such as `../inference/inference.pdmodel`;
-+ `Global.use_tensorrt`: Whether to use the TesorRT, default by `False`;
-+ `Global.use_gpu`: Whether to use the GPU, default by `True`
-+ `Global.enable_mkldnn`: Wheter to use `MKL-DNN`, default by `False`. It is valid when `Global.use_gpu` is `False`.
-+ `Global.use_fp16`: Whether to enable FP16, default by `False`;
-
-**Note**: If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `resize_short=384`, `resize=384`.
-
-If you want to evaluate the speed of the model, it is recommended to enable TensorRT to accelerate for GPU, and MKL-DNN for CPU.
diff --git a/docs/en/tutorials/getting_started_retrieval_en.md b/docs/en/tutorials/getting_started_retrieval_en.md
deleted file mode 100644
index f548572d9..000000000
--- a/docs/en/tutorials/getting_started_retrieval_en.md
+++ /dev/null
@@ -1,253 +0,0 @@
-# Quick Start
----
-At first，please take a reference to [Installation Guide](./install_en.md) to prepare your environment.
-
-PaddleClas image retrieval supports the following training/evaluation environments:
-```shell
-└── CPU/Single GPU
- ├── Linux
- └── Windows
-```
-## Content
-
-* [1. Data Preparation](#Data-Preparation)
-* [2. Training and Evaluation on Single GPU](#Training-and-Evaluation-on-Single-GPU)
- * [2.1 Model Training](#Model-Training)
- * [2.2 Resume Training](#Resume-Training)
- * [2.3 Model Evaluation](#Model-Evaluation)
-* [3. Export Inference Model](#Export-Inference-Model)
-
-
-## 1. Data Preparation
-
-* Go to PaddleClas directory。
-
-```bash
-## linux or mac, $path_to_PaddleClas indicates the root directory of PaddleClas, which the user needs to modify according to their real directory
-cd $path_to_PaddleClas
-```
-
-* Please go to the `dataset` catalog. In order to quickly experiment the image retrieval module of PaddleClas, the dataset we used is [CUB_200_2011](http://vision.ucsd.edu/sites/default/files/WelinderEtal10_CUB-200.pdf), which is a fine grid dataset with 200 different types of birds. Firstly, we need to download the dataset. For download, please refer to [Official Website](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html).
-
-```shell
-# linux or mac
-cd dataset
-
-# Copy the downloaded data into a directory.
-cp {Data storage path}/CUB_200_2011.tgz .
-
-# Unzip
-tar -xzvf CUB_200_2011.tgz
-
-#go to `CUB_200_2011`
-cd CUB_200_2011
-```
-
-When using the dataset for image retrieval, we usually use the first 100 classes as the training set, and the last 100 classes as the testing set, so we need to process those data so as to adapt the model training of image retrival.
-
-```shell
-#Create train and test directories
-mkdir train && mkdir test
-
-#Divide data into training set with the first 100 classes and testing set with the last 100 classes.
-ls images | awk -F "." '{if(int($1)<101)print "mv images/"$0" train/"int($1)}' | sh
-ls images | awk -F "." '{if(int($1)>100)print "mv images/"$0" test/"int($1)}' | sh
-
-#Generate train_list.txt test_list.txt
-tree -r -i -f train | grep jpg | awk -F "/" '{print $0" "int($2) " "NR}' > train_list.txt
-tree -r -i -f test | grep jpg | awk -F "/" '{print $0" "int($2) " "NR}' > test_list.txt
-```
-
-
-So far, we have the training set (in the `train` catalog) and testing set (in the `test` catalog) of `CUB_200_2011`.
-After data preparation, the `train` directory of `CUB_200_2011` should be:
-
-```
-├── 1
-│ ├── Black_Footed_Albatross_0001_796111.jpg
-│ ├── Black_Footed_Albatross_0002_55.jpg
- ...
-├── 10
-│ ├── Red_Winged_Blackbird_0001_3695.jpg
-│ ├── Red_Winged_Blackbird_0005_5636.jpg
-...
-```
-
-`train_list.txt` Should be：
-
-```
-train/99/Ovenbird_0137_92639.jpg 99 1
-train/99/Ovenbird_0136_92859.jpg 99 2
-train/99/Ovenbird_0135_93168.jpg 99 3
-train/99/Ovenbird_0131_92559.jpg 99 4
-train/99/Ovenbird_0130_92452.jpg 99 5
-...
-```
-The separators are shown as spaces, and the meaning of those three columns of data are the directory of training set, labels of training set and unique ids of training set.
-
-The format of testing set is the same as the one of training set.
-
-
-**Note**：
-
-* When the gallery dataset and query dataset are the same, in order to remove the first data retrieved (the retrieved images themselves do not need to be evaluated), each data needs to correspond to a unique id for subsequent evaluation of metrics such as mAP, recall@1, etc. Please refer to [Introduction to image retrieval datasets](#Introduction to image retrieval datasets) for the analysis of gallery datasets and query datasets, and [Image retrieval evaluation metrics](#Image retrieval evaluation metrics) for the evaluation of mAP, recall@1, etc.
-
-Back to `PaddleClas` root directory
-
-```shell
-# linux or mac
-cd ../../
-```
-
-
-## 2. Single GPU-based Training and Evaluation
-
-For training and evaluation on a single GPU, the `tools/train.py` and `tools/eval.py` scripts are recommended.
-
-
-
-### 2.1 Model Training
-
-Once you have prepared the configuration file, you can start training the image retrieval task in the following way. the method used by PaddleClas to train the image retrieval is metric learning, refering to [metric learning](#Metric-Learning) for an explanation of metric learning.
-
-
-```
-python3 tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV1_retrieval.yaml \
- -o Arch.Backbone.pretrained=True \
- -o Global.device=gpu
-```
-
- `-c` is used to specify the path to the configuration file, and `-o` is used to specify the parameters that need to be modified or added, where `-o Arch.Backbone.pretrained=True` indicates that the Backbone part uses the pre-trained model, in addition, `Arch.Backbone.pretrained` can also specify backbone.`pretrained` can also specify the address of a specific model weight file, which needs to be replaced with the path to your own pre-trained model weight file when using it. `-o Global.device=gpu` indicates that the GPU is used for training. If you want to use a CPU for training, you need to set `Global.device` to `cpu`.
-
-For more detailed training configuration, you can also modify the corresponding configuration file of the model directly. Refer to the [configuration document](config_description_en.md) for specific configuration parameters.
-
-Run the above commands to check the output log, an example is as follows:
-
- ```
- ...
- [Train][Epoch 1/50][Avg]CELoss: 6.59110, TripletLossV2: 0.54044, loss: 7.13154
- ...
- [Eval][Epoch 1][Avg]recall1: 0.46962, recall5: 0.75608, mAP: 0.21238
- ...
- ```
-
-The Backbone here is MobileNetV1, if you want to use other backbone, you can rewrite the parameter `Arch.Backbone.name`, for example by adding `-o Arch.Backbone.name={other Backbone}` to the command. In addition, as the input dimension of the `Neck` section differs between models, replacing a Backbone may require rewriting the input size here in a similar way to replacing the Backbone's name.
-
-In the Training Loss section, [CELoss](../../../ppcls/loss/celoss.py) and [TripletLossV2](../../../ppcls/loss/triplet.py) is used here with the following configuration files.
-
-```
-Loss:
- Train:
- - CELoss:
- weight: 1.0
- - TripletLossV2:
- weight: 1.0
- margin: 0.5
-```
-
-The final total Loss is a weighted sum of all Losses, where weight defines the weight of a particular Loss in the final total. If you want to replace other Losses, you can also change the Loss field in the configuration file, for the currently supported Losses please refer to [Loss](../../../ppcls/loss).
-
-
-### 2.2 Resume Training
-
-If the training task is terminated for some reasons, it can be recovered by loading the checkpoints weights file and continue training.
-
-
-```
-python3 tools/train.py \
- -c ./ppcls/configs/quick_start/MobileNetV1_retrieval.yaml \
- -o Global.checkpoints="./output/RecModel/epoch_5" \
- -o Global.device=gpu
-```
-
-There is no need to modify the configuration file, just set the `Global.checkpoints` parameter when continuing training, indicating the path to the loaded breakpoint weights file, using this parameter will load both the saved checkpoints weights and information about the learning rate, optimizer, etc.
-
-**Note**：
-
-* The `-o Global.checkpoints` parameter need not contain the suffix name of the checkpoint weights file, the above training command will generate the breakpoint weights file as shown below during training, if you want to continue training from breakpoint `5` then the `Global.checkpoints` parameter just needs to be set to `". /output/RecModel/epoch_5"` and PaddleClas will automatically supplement the suffix name.
-
- ```shell
- output/
- └── RecModel
- ├── best_model.pdopt
- ├── best_model.pdparams
- ├── best_model.pdstates
- ├── epoch_1.pdopt
- ├── epoch_1.pdparams
- ├── epoch_1.pdstates
- .
- .
- .
- ```
-
-
-### 2.3 Model Evaluation
-
-Model evaluation can be carried out with the following commands.
-
-```bash
-python3 tools/eval.py \
- -c ./ppcls/configs/quick_start/MobileNetV1_retrieval.yaml \
- -o Global.pretrained_model=./output/RecModel/best_model
-```
-
-The above command will use `. /configs/quick_start/MobileNetV1_retrieval.yaml` as a configuration file to evaluate the model obtained from the above training `. /output/RecModel/best_model` for evaluation. You can also set up the evaluation by changing the parameters in the configuration file, or you can update the configuration with the `-o` parameter, as shown above.
-
-Some of the configurable evaluation parameters are introduced as follows.
-* `Arch.name`: the name of the model
-* `Global.pretrained_model`: path to the pre-trained model file of the model to be evaluated, unlike `Global.Backbone.pretrained` where the pre-trained model is the weight of the whole model, whereas `Global.Backbone.pretrained` is only the Backbone.`pretrained` is only the weight of the Backbone part. When it is time to do model evaluation, the weights of the whole model need to be loaded.
-* `Metric.Eval`: the metric to be evaluated, by default evaluates recall@1, recall@5, mAP. when you are not going to evaluate a metric, you can remove the corresponding trial marker from the configuration file; when you want to add a certain evaluation metric, you can also refer to [Metric](../../../ppcls/metric/metrics.py) section to add the relevant metric to the configuration file `Metric.Eval`.
-
-**Note：**
-
-* When loading the model to be evaluated, the path to the model file needs to be specified, but it is not necessary to include the file suffix, PaddleClas will automatically complete the `.pdparams` suffix, e.g. [2.2 Resume Training](#Resume-Training).
-
-* Metric learning are generally not evaluated for TopkAcc.
-
-
-## 3. Export Inference Model
-
-By exporting the inference model, PaddlePaddle supports the transformation of the trained model using prediction with inference engine.
-
-```bash
-python3 tools/export_model.py \
- -c ./ppcls/configs/quick_start/MobileNetV1_retrieval.yaml \
- -o Global.pretrained_model=output/RecModel/best_model \
- -o Global.save_inference_dir=./inference
-```
-
- `Global.pretrained_model` is used to specify the model file path, which still does not need to contain the model file suffix (e.g. [2.2 Model recovery training](#Model recovery training)). When executed, it will generate the `. /inference` directory, which contains the `inference.pdiparams`, `inference.pdiparams.info`, and `inference.pdmodel` files. `Global.save_inference_dir` allows you to specify the path to export the inference model. The inference model saved here is truncated at the embedding feature level, i.e. the final output of the model is n-dimensional embedding features.
-
-The above command will generate the model structure file (`inference.pdmodel`) and the model weights file (`inference.pdiparams`), which can then be used for inference using the inference engine. The process of inference using the inference model can be found in [Predictive inference based on the Python prediction engine](@shengyu).
-
-## Basic knowledge
-
-Image retrieval refers to a query image given a specific instance (e.g. a specific target, scene, item, etc.) that contains the same instance from a database image. Unlike image classification, image retrieval solves an open set problem where the training set may not contain the class of the image being recognised. The overall process of image retrieval is: firstly, the images are represented in a suitable feature vector, secondly, a nearest neighbour search is performed on these image feature vectors using Euclidean or Cosine distances to find similar images in the base, and finally, some post-processing techniques can be used to fine-tune the retrieval results and determine information such as the category of the image being recognised. Therefore, the key to determining the performance of an image retrieval algorithm lies in the goodness of the feature vectors corresponding to the images.
-
-
-- Metric Learning
-
-Metric learning studies how to learn a distance function on a particular task so that the distance function can help nearest-neighbour based algorithms (kNN, k-means, etc.) to achieve better performance. Deep Metric Learning is a method of metric learning that aims to learn a mapping from the original features to a low-dimensional dense vector space (embedding space) such that similar objects on the embedding space are closer together using commonly used distance functions (Euclidean distance, cosine distance, etc.) ) on the embedding space, while the distances between objects of different classes are relatively close to each other. Deep metric learning has achieved very successful applications in the field of computer vision, such as face recognition, commodity recognition, image retrieval, pedestrian re-identification, etc.
-
-
-- Introduction to image retrieval datasets
-
- - Training Dataset: used to train the model so that it can learn the image features of the collection.
- - Gallery Dataset: used to provide the gallery data for the image retrieval task. The gallery dataset can be the same as the training set or the test set, or different.
- - Test Set (Query Dataset): used to test the goodness of the model, usually each test image in the test set is extracted with features, and then matched with the features of the underlying data to obtain recognition results, and then the metrics of the whole test set are calculated based on the recognition results.
-
-
-- Image Retrieval Evaluation Metrics
-
-
- - recall：indicates the number of predicted positive cases with positive labels / the number of cases with positive labels
-
- - recall@1：Number of predicted positive cases in top-1 with positive label / Number of cases with positive label
- - recall@5：Number of all predicted positive cases in top-5 retrieved with positive label / Number of cases with positive label
-
-
- - mean Average Precision(mAP)
-
- - AP: AP refers to the average precision on different recall rates
- - mAP: Average of the APs for all images in the test set
diff --git a/docs/en/tutorials/image_classification_en.md b/docs/en/tutorials/image_classification_en.md
deleted file mode 100644
index 097fff2e7..000000000
--- a/docs/en/tutorials/image_classification_en.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Image Classification
----
-
-Image Classification is a fundamental task that classifies the image by semantic information and assigns it to a specific label. Image Classification is the foundation of Computer Vision tasks, such as object detection, image segmentation, object tracking and behavior analysis. Image Classification has comprehensive applications, including face recognition and smart video analysis in the security and protection field, traffic scenario recognition in the traffic field, image retrieval and electronic photo album classification in the internet industry, and image recognition in the medical industry.
-
-Generally speaking, Image Classification attempts to comprehend an entire image as a whole by feature engineering and assigns labels by a classifier. Hence, how to extract the features of image is the essential part. Before we have deep learning, the most used classification method is the Bag of Words model. However, Image Classification based on deep learning can learn the hierarchical feature description by supervised and unsupervised learning, replacing the manually image feature selection. Recently, Convolution Neural Network in deep learning has an awesome performance in the image field. CNN uses the pixel information as the input to get the all information to the maximum extent. Additionally, since the model uses convolution to extract features, the classification result is the output. Thus, this kind of end-to-end method achieves ideal performance and is applied widely.
-
-Image Classification is a very basic but important field in the subject of computer vision. Its research results have always influenced the development of computer vision and even deep learning. Image classification has many sub-fields, such as multi-label image classification and fine-grained image classification. Here is only a brief description of single-label image classification.
-
-
-## 1 Dataset Introduction
-
-### 1.1 ImageNet-1k
-
-The ImageNet project is a large-scale visual database for the research of visual object recognition software. More than 14 million images have been annotated manually to point out objects in the picture in this project, and at least more than 1 million images provide borders. ImageNet-1k is a subset of the ImageNet dataset, which contains 1000 categories. The training set contains 1281167 image data, and the validation set contains 50,000 image data. Since 2010, the ImageNet project has held an image classification competition every year, which is the ImageNet Large-scale Visual Recognition Challenge (ILSVRC). The dataset used in the challenge is ImageNet-1k. So far, ImageNet-1k has become one of the most important data sets for the development of computer vision, and it promotes the development of the entire computer vision. The initialization models of many computer vision downstream tasks are based on the weights trained on this dataset.
-
-### 1.2 CIFAR-10/CIFAR-100
-
-The CIFAR-10 data set consists of 60,000 color images in 10 categories, with an image resolution of 32x32, and each category has 6000 images, including 5000 in the training set and 1000 in the validation set. 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships and trucks. The CIFAR-100 data set is an extension of CIFAR-10. It consists of 60,000 color images in 100 classes, with an image resolution of 32x32, and each class has 600 images, including 500 in the training set and 100 in the validation set. Researchers can try different algorithms quickly because these two data sets are small in scale. These two data sets are also commonly used data sets for testing the quality of models in the image classification field.
-
-## 2 Image Classification Process
-
-The prepared training data is preprocessed by the corresponding data and then passed through the image classification model. The output of the model and the real label are used in a cross-entropy loss function. This loss function describes the convergence direction of the model. Traverse all the image data input model, do the corresponding gradient descent for the final loss function through some optimizers, return the gradient information to the model, update the weight of the model, and traverse the data repeatedly. Finally, an image classification model can be obtained.
-
-### 2.1 Data and its preprocessing
-
-The quality and quantity of data often determine the performance of a model. In the field of image classification, data includes images and labels. In most cases, labeled data is scarce, so the amount of data is difficult to reach the level of saturation of the model. In order to enable the model to learn more image features, a lot of image transformation or data augmentation is required before the image enters the model, so as to ensure the diversity of input image data. Ultimately ensure that the model has better generalization capabilities. PaddleClas provides standard image transformation for training ImageNet-1k, and also provides 8 data augmentation methods. For related codes, please refer to[data preprocess](../../../ppcls/data/preprocess)，The configuration file refer to [Data Augmentation Configuration File](../../../ppcls/configs/ImageNet/DataAugment).
-
-### 2.2 Prepare the model
-
-After the data is determined, the model often determines the upper limit of the final accuracy. In the field of image classification, classic models emerge in an endless stream. PaddleClas provides 35 series and a total of 164 ImageNet pre-trained models. For specific accuracy, speed and other indicators, please refer to[Backbone network introduction](../models).
-
-### 2.3 Train the model
-
-After preparing the data and model, you can start training the model and update the parameters of the model. After many iterations, a trained model can finally be obtained for image classification tasks. The training process of image classification requires a lot of experience and involves the setting of many hyperparameters. PaddleClas provides a series of [training tuning methods](../models/Tricks_en.md), which can quickly help you obtain a high-precision model.
-
-### 2.4 Evaluate the model
-
-After a model is trained, the evaluation results of the model on the validation set can determine the performance of the model. The evaluation index is generally Top1-Acc or Top5-Acc. The higher the index, the better the model performance.
-
-
-## 3 Main Algorithms Introduction
-
-- LeNet: Yan LeCun et al. first applied convolutional neural networks to image classification tasks in the 1990s, and creatively proposed LeNet, which achieved great success in handwritten digit recognition tasks.
-
-- AlexNet: Alex Krizhevsky et al. proposed AlexNet in 2012 and applied it to ImageNet, and won the 2012 ImageNet classification competition. Since then, deep learning has become popular
-
-- VGG: Simonyan and Zisserman proposed the VGG network structure in 2014. This network structure uses a smaller convolution kernel to stack the entire network, achieving better performance in ImageNet classification, it provides new ideas for the subsequent network structure design.
-
-- GoogLeNet: Christian Szegedy et al. proposed GoogLeNet in 2014. This network uses a multi-branch structure and a global average pooling layer (GAP). While maintaining the accuracy of the model, the amount of model storage and calculation is greatly reduced. The network won the 2014 ImageNet classification competition.
-
-- ResNet: Kaiming He et al. proposed ResNet in 2015, which deepened the depth of the network by introducing a residual module. In the end, the network reduced the recognition error rate of ImageNet classification to 3.6%, which exceeded the recognition accuracy of normal human eyes for the first time.
-
-- DenseNet: Huang Gao et al. proposed DenseNet in 2017. The network designed a denser connected block and achieved higher performance with a smaller amount of parameters.
-
-- EfficientNet: Mingxing Tan et al. proposed EfficientNet in 2019. This network balances the width of the network, the depth of the network, and the resolution of the input image. With the same FLOPS and parameters, the accuracy reaches the state-of-the-art.
-
-For more algorithm introduction, please refer to [Algorithm Introduction](../models).
diff --git a/docs/en/tutorials/index.rst b/docs/en/tutorials/index.rst
deleted file mode 100644
index 469fd1f05..000000000
--- a/docs/en/tutorials/index.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-tutorials
-================================
-
-.. toctree::
- :maxdepth: 1
-
- install_en.md
- quick_start_en.md
- data_en.md
- getting_started_en.md
- config_en.md
diff --git a/docs/en/tutorials/install_en.md b/docs/en/tutorials/install_en.md
deleted file mode 100644
index 0e11df4a7..000000000
--- a/docs/en/tutorials/install_en.md
+++ /dev/null
@@ -1,105 +0,0 @@
-# Installation
-
----
-This tutorial introduces how to install PaddleClas and its requirements.
-
-## 1. Install PaddlePaddle
-
-`PaddlePaddle 2.1` or later is required for PaddleClas. You can use the following steps to install PaddlePaddle.
-
-### 1.1 Environment requirements
-
-- python 3.x
-- cuda >= 10.1 (necessary if you want to use paddlepaddle-gpu)
-- cudnn >= 7.6.4 (necessary if you want to use paddlepaddle-gpu)
-- nccl >= 2.1.2 (necessary if you want the use distributed training/eval)
-- gcc >= 8.2
-
-Docker is recomended to run Paddleclas, for more detailed information about docker and nvidia-docker, you can refer to the [tutorial](https://www.runoob.com/docker/docker-tutorial.html).
-
-When you use cuda10.1, the driver version needs to be larger or equal than 418.39. When you use cuda10.2, the driver version needs to be larger or equal than 440.33. For more cuda versions and specific driver versions, you can refer to the [link](https://docs.nvidia.com/deploy/cuda-compatibility/index.html).
-
-If you do not want to use docker, you can skip section 1.2 and go into section 1.3 directly.
-
-
-### 1.2 (Recommended) Prepare a docker environment. The first time you use this docker image, it will be downloaded automatically. Please be patient.
-
-
-```
-# Switch to the working directory
-cd /home/Projects
-# You need to create a docker container for the first run, and do not need to run the current command when you run it again
-# Create a docker container named ppcls and map the current directory to the /paddle directory of the container
-# It is recommended to set a shared memory greater than or equal to 8G through the --shm-size parameter
-sudo docker run --name ppcls -v $PWD:/paddle --shm-size=8G --network=host -it paddlepaddle/paddle:2.1.0 /bin/bash
-
-# Use the following command to create a container if you want to use GPU in the container
-sudo nvidia-docker run --name ppcls -v $PWD:/paddle --shm-size=8G --network=host -it paddlepaddle/paddle:2.1.0-gpu-cuda10.2-cudnn7 /bin/bash
-```
-
-You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get more docker images.
-
-```
-# use ctrl+P+Q to exit docker, to re-enter docker using the following command:
-sudo docker exec -it ppcls /bin/bash
-```
-
-### 1.3 Install PaddlePaddle using pip
-
-If you want to use PaddlePaddle on GPU, you can use the following command to install PaddlePaddle.
-
-```bash
-pip3 install paddlepaddle-gpu --upgrade -i https://mirror.baidu.com/pypi/simple
-```
-
-If you want to use PaddlePaddle on CPU, you can use the following command to install PaddlePaddle.
-
-```bash
-pip3 install paddlepaddle --upgrade -i https://mirror.baidu.com/pypi/simple
-```
-
-**Note:**
-* If you have already installed CPU version of PaddlePaddle and want to use GPU version now, you should uninstall CPU version of PaddlePaddle and then install GPU version to avoid package confusion.
-* You can also compile PaddlePaddle from source code, please refer to [PaddlePaddle Installation tutorial](http://www.paddlepaddle.org.cn/install/quick) to more compilation options.
-
-### 1.4 Verify Installation process
-
-```python
-import paddle
-paddle.utils.run_check()
-```
-
-Check PaddlePaddle version：
-
-```bash
-python3 -c "import paddle; print(paddle.__version__)"
-```
-
-Note:
-- Make sure the compiled source code is later than PaddlePaddle2.0.
-- Indicate **WITH_DISTRIBUTE=ON** when compiling, Please refer to [Instruction](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#id3) for more details.
-- When running in docker, in order to ensure that the container has enough shared memory for dataloader acceleration of Paddle, please set the parameter `--shm_size=8g` at creating a docker container, if conditions permit, you can set it to a larger value.
-
-
-
-## 2. Install PaddleClas
-
-### 2.1 Clone PaddleClas source code
-
-```
-git clone https://github.com/PaddlePaddle/PaddleClas.git -b develop
-```
-
-If it is too slow for you to download from github, you can download PaddleClas from gitee. The command is as follows.
-
-```bash
-git clone https://gitee.com/paddlepaddle/PaddleClas.git -b develop
-```
-
-### 2.2 Install requirements
-
-PaddleClas dependencies are listed in file `requirements.txt`, you can use the following command to install the dependencies.
-
-```
-pip3 install --upgrade -r requirements.txt -i https://mirror.baidu.com/pypi/simple
-```
diff --git a/docs/images/whl/demo.jpg b/docs/images/whl/demo.jpg
deleted file mode 100644
index cc7bff445..000000000
Binary files a/docs/images/whl/demo.jpg and /dev/null differ
diff --git a/docs/zh_CN/advanced_tutorials/DataAugmentation.md b/docs/zh_CN/advanced_tutorials/DataAugmentation.md
index 2b2d3c10e..9e5159a4a 100644
--- a/docs/zh_CN/advanced_tutorials/DataAugmentation.md
+++ b/docs/zh_CN/advanced_tutorials/DataAugmentation.md
@@ -307,7 +307,7 @@ sh tools/train.sh
* 在使用数据增强后，模型可能会趋于欠拟合状态，建议可以适当的调小 `l2_decay` 的值来获得更高的验证集准确率。
-* 几乎每一类图像增强均含有超参数，我们只提供了基于 ImageNet-1k 的超参数，其他数据集需要用户自己调试超参数，具体超参数的含义用户可以阅读相关的论文，调试方法也可以参考训练技巧的章节。
+* 几乎每一类图像增强均含有超参数，我们只提供了基于 ImageNet-1k 的超参数，其他数据集需要用户自己调试超参数，具体超参数的含义用户可以阅读相关的论文，调试方法也可以参考[训练技巧](../models_training/train_strategy.md)。
## 4. 实验结果
diff --git a/docs/zh_CN/advanced_tutorials/shitu_deploy.md b/docs/zh_CN/advanced_tutorials/shitu_deploy.md
index 9958dacae..064f68ee4 100644
--- a/docs/zh_CN/advanced_tutorials/shitu_deploy.md
+++ b/docs/zh_CN/advanced_tutorials/shitu_deploy.md
@@ -85,7 +85,7 @@
- 采用蒸馏方法，对小模型进行模型能力提升，详见[模型蒸馏](../algorithm_introduction/knowledge_distillation.md)
- 增补数据集。针对错误样本，添加badcase数据
-模型训练完成后，参照[1.2 检索库更新](#1.2 检索库更新)进行检索库更新。同时，对整个pipeline进行测试，如果精度不达预期，则重复此步骤。
+模型训练完成后，参照[1.2 检索库更新](#1.2)进行检索库更新。同时，对整个pipeline进行测试，如果精度不达预期，则重复此步骤。
diff --git a/docs/zh_CN/algorithm_introduction/DataAugmentation.md b/docs/zh_CN/algorithm_introduction/DataAugmentation.md
index 3b14de810..32bdfd92d 100644
--- a/docs/zh_CN/algorithm_introduction/DataAugmentation.md
+++ b/docs/zh_CN/algorithm_introduction/DataAugmentation.md
@@ -15,7 +15,7 @@
- [2.2.4 GridMask](#2.2.4)
- [2.3 图像混叠类](#2.3)
- [2.3.1 Mixup](#2.3.1)
- - [2.3.2 Cutmix](#3.2.2)
+ - [2.3.2 Cutmix](#2.3.2)
## 1. 数据增强简介
diff --git a/docs/zh_CN/algorithm_introduction/image_classification.md b/docs/zh_CN/algorithm_introduction/image_classification.md
index afe9be549..b55aad053 100644
--- a/docs/zh_CN/algorithm_introduction/image_classification.md
+++ b/docs/zh_CN/algorithm_introduction/image_classification.md
@@ -51,7 +51,7 @@ CIFAR-10 数据集由 10 个类的 60000 个彩色图像组成，图像分辨率
### 2.3 模型训练
-在准备好数据、模型后，便可以开始迭代模型并更新模型的参数。经过多次迭代最终可以得到训练好的模型来做图像分类任务。图像分类的训练过程需要很多经验，涉及很多超参数的设置，PaddleClas 提供了一些列的[训练调优方法](../models/Tricks.md)，可以快速助你获得高精度的模型。
+在准备好数据、模型后，便可以开始迭代模型并更新模型的参数。经过多次迭代最终可以得到训练好的模型来做图像分类任务。图像分类的训练过程需要很多经验，涉及很多超参数的设置，PaddleClas 提供了一些列的[训练调优方法](../models_training/train_strategy.md)，可以快速助你获得高精度的模型。
### 2.4 模型评估
diff --git a/docs/zh_CN/inference_deployment/whl_deploy.md b/docs/zh_CN/inference_deployment/whl_deploy.md
index 1605aebc9..14582ace5 100644
--- a/docs/zh_CN/inference_deployment/whl_deploy.md
+++ b/docs/zh_CN/inference_deployment/whl_deploy.md
@@ -192,14 +192,14 @@ paddleclas --model_name='ResNet50' --infer_imgs='https://raw.githubusercontent.c
### 4.6 对 `NumPy.ndarray` 格式数据进行预测
-在 Python 中，可以对 `Numpy.ndarray` 格式的图像数据进行预测，只需通过参数 `infer_imgs` 指定即可。注意该图像数据必须为三通道图像数据。
+在 Python 中，可以对 `Numpy.ndarray` 格式的图像数据进行预测，只需通过参数 `infer_imgs` 指定即可。注意，PaddleClas 所提供的模型仅支持 3 通道图像数据，且通道顺序为 `RGB`。
* python
```python
import cv2
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
-infer_imgs = cv2.imread("docs/images/inference_deployment/whl_demo.jpg")
+infer_imgs = cv2.imread("docs/images/inference_deployment/whl_demo.jpg")[:, :, ::-1]
result=clas.predict(infer_imgs)
print(next(result))
```
diff --git a/docs/zh_CN/models/ESNet.md b/docs/zh_CN/models/ESNet.md
index de604cb83..cff135425 100644
--- a/docs/zh_CN/models/ESNet.md
+++ b/docs/zh_CN/models/ESNet.md
@@ -2,8 +2,8 @@
---
## 目录
-* [1. 概述](#)
-* [2. 精度、FLOPs 和参数量](#FLOPs)
+* [1. 概述](#1)
+* [2. 精度、FLOPs 和参数量](#2)
diff --git a/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml b/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
index 1df2cbd1e..ea2e073ca 100644
--- a/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
+++ b/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml b/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml
index e512e5b32..4848cfc85 100644
--- a/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml
+++ b/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA102.yaml b/ppcls/configs/ImageNet/DLA/DLA102.yaml
index d808afeba..b6033f70c 100644
--- a/ppcls/configs/ImageNet/DLA/DLA102.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA102.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA102x.yaml b/ppcls/configs/ImageNet/DLA/DLA102x.yaml
index 10a8375f1..a1e2c09d2 100644
--- a/ppcls/configs/ImageNet/DLA/DLA102x.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA102x.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA102x2.yaml b/ppcls/configs/ImageNet/DLA/DLA102x2.yaml
index f95279bb9..8bd4c4646 100644
--- a/ppcls/configs/ImageNet/DLA/DLA102x2.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA102x2.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA169.yaml b/ppcls/configs/ImageNet/DLA/DLA169.yaml
index dd1d4a295..18c244d0b 100644
--- a/ppcls/configs/ImageNet/DLA/DLA169.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA169.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA34.yaml b/ppcls/configs/ImageNet/DLA/DLA34.yaml
index 4051e3677..d9218df78 100644
--- a/ppcls/configs/ImageNet/DLA/DLA34.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA34.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA46_c.yaml b/ppcls/configs/ImageNet/DLA/DLA46_c.yaml
index c40d76354..8d2034135 100644
--- a/ppcls/configs/ImageNet/DLA/DLA46_c.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA46_c.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml b/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
index 61a6d8978..e7f7d672e 100644
--- a/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA60.yaml b/ppcls/configs/ImageNet/DLA/DLA60.yaml
index da53f14c0..a255f053b 100644
--- a/ppcls/configs/ImageNet/DLA/DLA60.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA60.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA60x.yaml b/ppcls/configs/ImageNet/DLA/DLA60x.yaml
index 1de82f5b2..143b87f91 100644
--- a/ppcls/configs/ImageNet/DLA/DLA60x.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA60x.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml b/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
index 646e9721f..779281980 100644
--- a/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
+++ b/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DPN/DPN107.yaml b/ppcls/configs/ImageNet/DPN/DPN107.yaml
index 239da60ea..7df1256d4 100644
--- a/ppcls/configs/ImageNet/DPN/DPN107.yaml
+++ b/ppcls/configs/ImageNet/DPN/DPN107.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DPN/DPN131.yaml b/ppcls/configs/ImageNet/DPN/DPN131.yaml
index ff81e4fe6..88f1b57aa 100644
--- a/ppcls/configs/ImageNet/DPN/DPN131.yaml
+++ b/ppcls/configs/ImageNet/DPN/DPN131.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DPN/DPN68.yaml b/ppcls/configs/ImageNet/DPN/DPN68.yaml
index fd7dc147b..c1e280814 100644
--- a/ppcls/configs/ImageNet/DPN/DPN68.yaml
+++ b/ppcls/configs/ImageNet/DPN/DPN68.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DPN/DPN92.yaml b/ppcls/configs/ImageNet/DPN/DPN92.yaml
index 3559e8f19..fb5b0ed58 100644
--- a/ppcls/configs/ImageNet/DPN/DPN92.yaml
+++ b/ppcls/configs/ImageNet/DPN/DPN92.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DPN/DPN98.yaml b/ppcls/configs/ImageNet/DPN/DPN98.yaml
index 11af49262..e394710ef 100644
--- a/ppcls/configs/ImageNet/DPN/DPN98.yaml
+++ b/ppcls/configs/ImageNet/DPN/DPN98.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml b/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
index ba1478ffa..ec0f822ac 100644
--- a/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
+++ b/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml
index a00968a0c..ab4c29c30 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml
index 603dfec98..d75fede9e 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml
index 6ab79d35c..2fefb9f4b 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml
index 5a60a76be..4bf530664 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml
index ed180b551..c0016aa00 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml
@@ -104,7 +104,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml
index 107738f2f..12e4ac8db 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml
index 448440ecf..3434cab5a 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml
index b0b6bfdfc..153451e13 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml
index 216caa3de..8e89c5ca1 100644
--- a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml
+++ b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml
@@ -104,7 +104,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml
index f03543f77..979a04a38 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml
index fcf2981be..859f57d72 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
index 7b328905e..3cdd10202 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
index a2990ecdb..88a8fbae9 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml
index b565d03ab..54d962e68 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
index 9e9c5de12..05c3ac1f3 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml
index 53f54b1b2..f66617613 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
index 8fa668563..647050a77 100644
--- a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
@@ -127,7 +127,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
index 510065849..42c7e7843 100644
--- a/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
+++ b/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
index 283490363..3f9bbb687 100644
--- a/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
+++ b/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
index 09fde348d..3a046fb8f 100644
--- a/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
+++ b/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
index df2545c78..ba6268213 100644
--- a/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
+++ b/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
index bee9a263a..a0a8193b3 100644
--- a/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
+++ b/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml b/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml
index d67704e09..9612be4e6 100644
--- a/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml
+++ b/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml
@@ -122,7 +122,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: "docs/images/whl/demo.jpg"
+ infer_imgs: "docs/images/inference_deployment/whl_demo.jpg"
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
index e01e853ac..9243cebf9 100644
--- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
index fc51351f3..fa217f75e 100644
--- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
index 265a622d2..54b888d7c 100644
--- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
index 44fd7d5f1..ae99f9992 100644
--- a/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
+++ b/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
index 2748e2fbb..2d5b7d079 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml
index 92ec91b7e..b23030f43 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml
index 16f2e2f11..de48d03a8 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml
index 0847256f6..3f0b559d4 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml
index c5396ff2b..e3a009adf 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml
index d37b93c60..795dfa12a 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml
index edaab5546..f86dd04b3 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml
index 1ced43d1d..d57d841d6 100644
--- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml
+++ b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
index ecf64c0a1..ba44691a1 100644
--- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
+++ b/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
index 613aef80b..a4e6e37a9 100644
--- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
+++ b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
index d1d40e0a7..69921bea4 100644
--- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
+++ b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
index 05b4353f5..935b0b588 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
index 1e80d0981..5f7067cd4 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
index 8d8cc6c76..fcc6dc125 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml
index 6224a2c69..a7096773d 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml
index cef9845fc..f530cc2db 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml
index 096910472..1c7ffc965 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml
index 557c1f555..e72b0b3f6 100644
--- a/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml
+++ b/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml
index ac21d5e85..2aa8e6816 100644
--- a/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml
+++ b/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml
index 258ce1439..2f0ef126a 100644
--- a/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml
+++ b/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml
index a3837d2a8..cf8f2edfc 100644
--- a/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml
+++ b/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml
index f60e10b23..851285925 100644
--- a/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml
+++ b/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml b/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml
index 579b01c67..5bc3c9e30 100644
--- a/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml
+++ b/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Inception/InceptionV3.yaml b/ppcls/configs/ImageNet/Inception/InceptionV3.yaml
index a8c30ea1a..3749ed86a 100644
--- a/ppcls/configs/ImageNet/Inception/InceptionV3.yaml
+++ b/ppcls/configs/ImageNet/Inception/InceptionV3.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Inception/InceptionV4.yaml b/ppcls/configs/ImageNet/Inception/InceptionV4.yaml
index 17415b3ce..7df00cc1e 100644
--- a/ppcls/configs/ImageNet/Inception/InceptionV4.yaml
+++ b/ppcls/configs/ImageNet/Inception/InceptionV4.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml
index 101642129..a1a4f7303 100644
--- a/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml
+++ b/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml
index 84c03534d..bfc6eb4e4 100644
--- a/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml
+++ b/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml
index 5b9c10785..9596e8689 100644
--- a/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml
+++ b/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml
index a7e99af85..fb4270012 100644
--- a/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml
+++ b/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml
index f6d5852ec..8347c4aee 100644
--- a/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml
+++ b/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
index b3c04aa07..54bb18d81 100644
--- a/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
+++ b/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
index 82248b608..2c2a18d0a 100644
--- a/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
+++ b/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
index 20d68c703..e0f5c6a8e 100644
--- a/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
+++ b/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml
index 53d1d1dd2..3b9412432 100644
--- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml
index 9fc0dd989..86324cfec 100644
--- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml
index ef7096409..1693e7876 100644
--- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml
index b9be283f3..b8b0477c9 100644
--- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml
index fc0e61bb6..b9c99c6a0 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml
index 0ebe45893..d9f30fd70 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml
index c2023908f..7abddd407 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml
index e581d72f9..e620d708b 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml
index 2ac0d6992..f9d6abc63 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml
index ca4bf6650..fa5bf6802 100644
--- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml
index 54ffaff5f..0c81ebc98 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml
index 61626e8bf..76c70289a 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml
index 5b0b159ae..a1e9126a7 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
index 56544d0f3..77389dbff 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml
index a0d6bf83b..097c41e79 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml
index 3525fa87b..30ea2eba5 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml
index 01561991c..3c13bbbb9 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml
index 058e81fcb..45608dfca 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml
index df15f7a4a..02a3949c3 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml
index 7db67b5b6..eeae6907d 100644
--- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml
+++ b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
index 3f328c676..8b0924c94 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
index a3c01014f..ed2501e65 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
index 8f5d2124b..0f01d58a3 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
index b3fb75eeb..785788232 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
index 62aff5a47..f55a044f0 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
index 390286dfc..d654d4208 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
index 283e8afd9..50b19aa56 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
index 64efb3432..4f677e5de 100644
--- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml
index 27684e9cd..6c0854cb4 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml
index 2e3e176c8..42134c740 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml
index ce81bccaa..4d0d5a432 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml
index 5844dccc4..a5feb260b 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml
index 7b26cd350..be300aca6 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml
@@ -133,7 +133,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml
index 30b63a201..b6a895339 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml
@@ -133,7 +133,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml
index 0992d5805..9d36b2807 100644
--- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml
+++ b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml
@@ -133,7 +133,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
index 9406bfe37..709d72fce 100644
--- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
+++ b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
index 9f0b52988..18607c6de 100644
--- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
+++ b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
index 09a86cfee..99dca8b35 100644
--- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
+++ b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
index 52336cde7..285b8dfbc 100644
--- a/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
+++ b/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
index d1598fc38..a44294e3c 100644
--- a/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
+++ b/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/RedNet/RedNet101.yaml b/ppcls/configs/ImageNet/RedNet/RedNet101.yaml
index ca64ec87a..95ea5185b 100644
--- a/ppcls/configs/ImageNet/RedNet/RedNet101.yaml
+++ b/ppcls/configs/ImageNet/RedNet/RedNet101.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/RedNet/RedNet152.yaml b/ppcls/configs/ImageNet/RedNet/RedNet152.yaml
index f44623c93..7d5cc03c4 100644
--- a/ppcls/configs/ImageNet/RedNet/RedNet152.yaml
+++ b/ppcls/configs/ImageNet/RedNet/RedNet152.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/RedNet/RedNet26.yaml b/ppcls/configs/ImageNet/RedNet/RedNet26.yaml
index c81395b1e..089db6f38 100644
--- a/ppcls/configs/ImageNet/RedNet/RedNet26.yaml
+++ b/ppcls/configs/ImageNet/RedNet/RedNet26.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/RedNet/RedNet38.yaml b/ppcls/configs/ImageNet/RedNet/RedNet38.yaml
index 5f40ab391..c2fb86349 100644
--- a/ppcls/configs/ImageNet/RedNet/RedNet38.yaml
+++ b/ppcls/configs/ImageNet/RedNet/RedNet38.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/RedNet/RedNet50.yaml b/ppcls/configs/ImageNet/RedNet/RedNet50.yaml
index e4169ee3a..02e045a2f 100644
--- a/ppcls/configs/ImageNet/RedNet/RedNet50.yaml
+++ b/ppcls/configs/ImageNet/RedNet/RedNet50.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml
index bf27b303d..ed16b0366 100644
--- a/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml
+++ b/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml
index 90b7b879c..af1f43825 100644
--- a/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml
+++ b/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml
index af1c4c73e..782405224 100644
--- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml
+++ b/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml
index e792e9d03..60767baa7 100644
--- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml
+++ b/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml
index 58d4968b4..977c14422 100644
--- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml
+++ b/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml
index 724b56df4..d99e885e3 100644
--- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml
+++ b/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml
index acf55ec78..d822c8b2e 100644
--- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml
+++ b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
index 9488195d8..eb973afba 100644
--- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
+++ b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
@@ -103,7 +103,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
index 413930882..e0d0a5b32 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml
index b911f9d79..d68f5f7f0 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml
index c400b9e28..eadd9eede 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml
index 4f5f3c793..5c59e5a8b 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml
index 29f807e6e..8bad3f6e3 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml
index 94c91c077..104f37a53 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml
index d3054143d..638feef32 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml
index c8b76d0f6..7c05197da 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml
index 2d81048ed..ef78f60b8 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml
index 38fa51e9e..b75035710 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml
index 3a03646f5..baf38e36f 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml
index c9b9a1015..dba5f8633 100644
--- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml
index 792311a3a..71193aa9b 100644
--- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml
index a943ebb47..346d2ea25 100644
--- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml
index 7f8fa72b9..2db3bd6c2 100644
--- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
index 3266c3467..bed3cc201 100644
--- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
+++ b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml b/ppcls/configs/ImageNet/ResNet/ResNet101.yaml
index a4f228f26..2c98acf0c 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet101.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml
index f30ca0777..d62b7bc38 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml b/ppcls/configs/ImageNet/ResNet/ResNet152.yaml
index b91571322..0dbbaf8e4 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet152.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml
index f3168c432..735c84b0c 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet18.yaml b/ppcls/configs/ImageNet/ResNet/ResNet18.yaml
index 64edd5ed0..4e0e460ac 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet18.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet18.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml
index 2dc6bba0c..015063347 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml
index a52c83748..c9209f123 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet34.yaml b/ppcls/configs/ImageNet/ResNet/ResNet34.yaml
index 92f4569a8..5b90cf05f 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet34.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet34.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml
index daae960b5..a894ea470 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50.yaml
index b338b6395..c2da23fb3 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet50.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml
index 769ff361e..1dccec121 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml
@@ -116,7 +116,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml
index 59d4bcec2..bbd35083b 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml
@@ -116,7 +116,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
index 0a2c4aa4e..be7b2d9db 100644
--- a/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+++ b/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml b/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml
index f7f1ba0f9..6545cbf95 100644
--- a/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml
+++ b/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml
index 3b09c3fd3..f97430ef1 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_fp16.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_fp16.yaml
index 540858698..8140424a5 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_fp16.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_fp16.yaml
@@ -108,7 +108,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml
index d04f298a3..b31250b02 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml
index cabff29b4..292b52d9c 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml
index fcaada934..47d175472 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml
index 69d15ccaf..174c181e3 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml
index f670c159e..f503ea66c 100644
--- a/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml
+++ b/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml
index 79bb34ede..e01891e9c 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml
index aa1cce189..c2e98055c 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml
index 3f0742e54..dc7a5ef0e 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml
index f14a24923..796fb7a35 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml
index dd4820da4..c2c4c8647 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml
index 9a05a59d6..eb3e0132c 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml
index c871ec7dc..730cf437b 100644
--- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml
+++ b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml
index 0f5d97253..28eba49d0 100644
--- a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml
+++ b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml
index 1f239220e..b61a28c23 100644
--- a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml
+++ b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml
index 903afef96..4dd0ac4cf 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml
index 227b73ba0..a42dea1f9 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml
index d50e83365..36b5e5e38 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml
index ba0c59b59..96a9befd2 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml
index e7997aaf0..ffbbcf080 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
index 707d93473..066db715d 100644
--- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
+++ b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
@@ -130,7 +130,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/TNT/TNT_small.yaml b/ppcls/configs/ImageNet/TNT/TNT_small.yaml
index dff9af2e8..1eab42306 100644
--- a/ppcls/configs/ImageNet/TNT/TNT_small.yaml
+++ b/ppcls/configs/ImageNet/TNT/TNT_small.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml
index ccf4cfe9c..74c402ee7 100644
--- a/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml
+++ b/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml
index 216995607..ca66e9a33 100644
--- a/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml
+++ b/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml
index 4006de9a1..9e97c0f99 100644
--- a/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml
+++ b/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml
index 7922efaec..7831e9289 100644
--- a/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml
+++ b/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml
index 360b1d42b..8e160b3c2 100644
--- a/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml
+++ b/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml
index 10dcebb52..582382d4d 100644
--- a/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml
+++ b/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml
@@ -132,7 +132,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VGG/VGG11.yaml b/ppcls/configs/ImageNet/VGG/VGG11.yaml
index d8cdb9864..e55c4d0da 100644
--- a/ppcls/configs/ImageNet/VGG/VGG11.yaml
+++ b/ppcls/configs/ImageNet/VGG/VGG11.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VGG/VGG13.yaml b/ppcls/configs/ImageNet/VGG/VGG13.yaml
index 75565b817..b4a0ee341 100644
--- a/ppcls/configs/ImageNet/VGG/VGG13.yaml
+++ b/ppcls/configs/ImageNet/VGG/VGG13.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VGG/VGG16.yaml b/ppcls/configs/ImageNet/VGG/VGG16.yaml
index 6f0db0867..154c4684e 100644
--- a/ppcls/configs/ImageNet/VGG/VGG16.yaml
+++ b/ppcls/configs/ImageNet/VGG/VGG16.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VGG/VGG19.yaml b/ppcls/configs/ImageNet/VGG/VGG19.yaml
index 195c641f1..0a7022e3a 100644
--- a/ppcls/configs/ImageNet/VGG/VGG19.yaml
+++ b/ppcls/configs/ImageNet/VGG/VGG19.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml
index 59c38e644..6d5857db5 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
index 0c76f694a..925d82770 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml
index 88eab3f51..fc4747b84 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml
index 74a6f405c..3882c5552 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml
index 81abc1be2..3bdb38719 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml
index 933fd7acb..25212dd22 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml
index eea492244..0a956b498 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Xception/Xception41.yaml b/ppcls/configs/ImageNet/Xception/Xception41.yaml
index 76dd77644..45e64a150 100644
--- a/ppcls/configs/ImageNet/Xception/Xception41.yaml
+++ b/ppcls/configs/ImageNet/Xception/Xception41.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml b/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml
index a580fd3aa..daf0598e8 100644
--- a/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml
+++ b/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Xception/Xception65.yaml b/ppcls/configs/ImageNet/Xception/Xception65.yaml
index f9217cf77..c6bb5297b 100644
--- a/ppcls/configs/ImageNet/Xception/Xception65.yaml
+++ b/ppcls/configs/ImageNet/Xception/Xception65.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml b/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml
index bba5ceef6..1248a29d1 100644
--- a/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml
+++ b/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/ImageNet/Xception/Xception71.yaml b/ppcls/configs/ImageNet/Xception/Xception71.yaml
index 7475a5f95..7f714cc23 100644
--- a/ppcls/configs/ImageNet/Xception/Xception71.yaml
+++ b/ppcls/configs/ImageNet/Xception/Xception71.yaml
@@ -102,7 +102,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml b/ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml
index c9a8b7b40..ad77ea950 100644
--- a/ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml
+++ b/ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml
@@ -111,7 +111,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml b/ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml
index 7b7c1d592..d87dc0991 100644
--- a/ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml
+++ b/ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml
@@ -100,7 +100,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/ResNet50_vd.yaml b/ppcls/configs/quick_start/ResNet50_vd.yaml
index 89cafca38..90b2c88d6 100644
--- a/ppcls/configs/quick_start/ResNet50_vd.yaml
+++ b/ppcls/configs/quick_start/ResNet50_vd.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml b/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
index 26bef7fc5..124636690 100644
--- a/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
+++ b/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
@@ -99,7 +99,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml b/ppcls/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml
index f0b864d3f..423a45389 100644
--- a/ppcls/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml
+++ b/ppcls/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml b/ppcls/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml
index b2447c2b4..a27068d16 100644
--- a/ppcls/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml
+++ b/ppcls/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml
@@ -119,7 +119,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: "docs/images/whl/demo.jpg"
+ infer_imgs: "docs/images/inference_deployment/whl_demo.jpg"
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml b/ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml
index 397796905..10c326a40 100644
--- a/ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml
+++ b/ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml
@@ -98,7 +98,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml b/ppcls/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml
index 3e09ae58f..d8ff817f8 100644
--- a/ppcls/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml
+++ b/ppcls/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml
@@ -101,7 +101,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml b/ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
index 958a1364b..6655c3a0a 100644
--- a/ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
+++ b/ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
@@ -109,7 +109,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml b/ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
index 683999862..517c4677c 100644
--- a/ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
+++ b/ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
@@ -108,7 +108,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml b/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml
index b01122cf7..40111a036 100644
--- a/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml
+++ b/ppcls/configs/slim/PPLCNet_x1_0_quantization.yaml
@@ -108,7 +108,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/slim/ResNet50_vd_prune.yaml b/ppcls/configs/slim/ResNet50_vd_prune.yaml
index f3e9e6666..7bfc537b1 100644
--- a/ppcls/configs/slim/ResNet50_vd_prune.yaml
+++ b/ppcls/configs/slim/ResNet50_vd_prune.yaml
@@ -110,7 +110,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/ppcls/configs/slim/ResNet50_vd_quantization.yaml b/ppcls/configs/slim/ResNet50_vd_quantization.yaml
index 23325ba07..f9db41020 100644
--- a/ppcls/configs/slim/ResNet50_vd_quantization.yaml
+++ b/ppcls/configs/slim/ResNet50_vd_quantization.yaml
@@ -109,7 +109,7 @@ DataLoader:
use_shared_memory: True
Infer:
- infer_imgs: docs/images/whl/demo.jpg
+ infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
diff --git a/tools/train.sh b/tools/train.sh
index 083934a57..412f1081e 100755
--- a/tools/train.sh
+++ b/tools/train.sh
@@ -4,4 +4,5 @@
# python3.7 tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml
# for multi-cards train
+export CUDA_VISIBLE_DEVICES=0,1,2,3
python3.7 -m paddle.distributed.launch --gpus="0,1,2,3" tools/train.py -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml
+