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Merge pull request #1003 from RainFrost1/vehicle_reid 

update VehicleReID model
pull/1013/head
Walter 2021-07-01 15:55:14 +08:00 committed by GitHub
parent b67b1df391 f0af483af8
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5 changed files with 6 additions and 15 deletions
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7
docs/en/application/vehicle_recognition_en.md
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@ -58,10 +58,7 @@ This method is used in VERI-Wild dataset. This dataset was captured in a large C
| 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 baseline1 | 65.6 | 92.37 | 97.23 |
| PaddleClas baseline2 | 80.09 | **93.81** | **98.26** |
Baseline1 is the released, and baseline2 will be released soon.
| PaddleClas baseline | 80.57 | **93.81** | **98.06** |
### 2.2 Vehicle Fine-grained Classification
@ -79,7 +76,7 @@ The images in the dataset mainly come from the network and monitoring data. The
| Fine-Tuning DARTS[7] | 95.9% |
| Resnet50 + COOC[8] | 95.6% |
| A3M[9] | 95.4% |
| PaddleClas baseline (ResNet50) | **97.36**% |
| PaddleClas baseline (ResNet50) | **97.37**% |
## 3 References

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docs/en/tutorials/quick_start_recognition_en.md
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@ -41,7 +41,7 @@ The detection model with the recognition inference model for the 4 directions (L
| Cartoon Face Recognition Model| Cartoon Face Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
| Vehicle Fine-Grained Classfication Model | Vehicle Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
| Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - |
| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar).

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docs/zh_CN/application/vehicle_recognition.md
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@ -57,10 +57,7 @@ ReID，也就是 Re-identification，其定义是利用算法，在图像库中
| 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 baseline1 | 65.6 | 92.37 | 97.23 |
| PaddleClas baseline2 | 80.09 | **93.81** | **98.26** |
注：baseline1 为目前的开源模型，baseline2即将开源
| PaddleClas baseline | 80.57 | **93.81** | **98.06** |
### 2.2 车辆细分类

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docs/zh_CN/tutorials/quick_start_recognition.md
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@ -41,7 +41,7 @@
| 动漫人物识别模型 | 动漫人物场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
| 车辆细分类模型 | 车辆场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
| 商品识别模型 | 商品场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - |
| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar)。

5
ppcls/configs/Vehicle/ResNet50_ReID.yaml
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@ -52,11 +52,8 @@ Optimizer:
name: Momentum
momentum: 0.9
lr:
name: MultiStepDecay
name: Cosine
learning_rate: 0.01
milestones: [30, 60, 70, 80, 90, 100, 120, 140]
gamma: 0.5
verbose: False
last_epoch: -1
regularizer:
name: 'L2'