From 32ce68374e9cb3e9c134c7328389cb105a80abab Mon Sep 17 00:00:00 2001 From: shippingwang Date: Wed, 6 May 2020 10:24:52 +0000 Subject: [PATCH 01/75] add ema --- tools/ema.py | 165 +++++++++++++++++++++++++++++++++++++++++++++ tools/ema_clean.py | 42 ++++++++++++ tools/program.py | 12 +++- tools/train.py | 8 +++ 4 files changed, 225 insertions(+), 2 deletions(-) create mode 100644 tools/ema.py create mode 100644 tools/ema_clean.py diff --git a/tools/ema.py b/tools/ema.py new file mode 100644 index 000000000..bbd5af2ed --- /dev/null +++ b/tools/ema.py @@ -0,0 +1,165 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import paddle +import paddle.fluid as fluid +from paddle.fluid.wrapped_decorator import signature_safe_contextmanager +from paddle.fluid.framework import Program, program_guard, name_scope, default_main_program +from paddle.fluid import unique_name, layers + + +class ExponentialMovingAverage(object): + def __init__(self, + decay=0.999, + thres_steps=None, + zero_debias=False, + name=None): + self._decay = decay + self._thres_steps = thres_steps + self._name = name if name is not None else '' + self._decay_var = self._get_ema_decay() + + self._params_tmps = [] + for param in default_main_program().global_block().all_parameters(): + if param.do_model_average != False: + tmp = param.block.create_var( + name=unique_name.generate(".".join( + [self._name + param.name, 'ema_tmp'])), + dtype=param.dtype, + persistable=False, + stop_gradient=True) + self._params_tmps.append((param, tmp)) + + self._ema_vars = {} + for param, tmp in self._params_tmps: + with param.block.program._optimized_guard( + [param, tmp]), name_scope('moving_average'): + self._ema_vars[param.name] = self._create_ema_vars(param) + + self.apply_program = Program() + block = self.apply_program.global_block() + with program_guard(main_program=self.apply_program): + decay_pow = self._get_decay_pow(block) + for param, tmp in self._params_tmps: + param = block._clone_variable(param) + tmp = block._clone_variable(tmp) + ema = block._clone_variable(self._ema_vars[param.name]) + layers.assign(input=param, output=tmp) + # bias correction + if zero_debias: + ema = ema / (1.0 - decay_pow) + layers.assign(input=ema, output=param) + + self.restore_program = Program() + block = self.restore_program.global_block() + with program_guard(main_program=self.restore_program): + for param, tmp in self._params_tmps: + tmp = block._clone_variable(tmp) + param = block._clone_variable(param) + layers.assign(input=tmp, output=param) + + def _get_ema_decay(self): + with default_main_program()._lr_schedule_guard(): + decay_var = layers.tensor.create_global_var( + shape=[1], + value=self._decay, + dtype='float32', + persistable=True, + name="scheduled_ema_decay_rate") + + if self._thres_steps is not None: + decay_t = (self._thres_steps + 1.0) / (self._thres_steps + 10.0) + with layers.control_flow.Switch() as switch: + with switch.case(decay_t < self._decay): + layers.tensor.assign(decay_t, decay_var) + with switch.default(): + layers.tensor.assign( + np.array( + [self._decay], dtype=np.float32), + decay_var) + return decay_var + + def _get_decay_pow(self, block): + global_steps = layers.learning_rate_scheduler._decay_step_counter() + decay_var = block._clone_variable(self._decay_var) + decay_pow_acc = layers.elementwise_pow(decay_var, global_steps + 1) + return decay_pow_acc + + def _create_ema_vars(self, param): + param_ema = layers.create_global_var( + name=unique_name.generate(self._name + param.name + '_ema'), + shape=param.shape, + value=0.0, + dtype=param.dtype, + persistable=True) + + return param_ema + + def update(self): + """ + Update Exponential Moving Average. Should only call this method in + train program. + """ + param_master_emas = [] + for param, tmp in self._params_tmps: + with param.block.program._optimized_guard( + [param, tmp]), name_scope('moving_average'): + param_ema = self._ema_vars[param.name] + if param.name + '.master' in self._ema_vars: + master_ema = self._ema_vars[param.name + '.master'] + param_master_emas.append([param_ema, master_ema]) + else: + ema_t = param_ema * self._decay_var + param * ( + 1 - self._decay_var) + layers.assign(input=ema_t, output=param_ema) + + # for fp16 params + for param_ema, master_ema in param_master_emas: + default_main_program().global_block().append_op( + type="cast", + inputs={"X": master_ema}, + outputs={"Out": param_ema}, + attrs={ + "in_dtype": master_ema.dtype, + "out_dtype": param_ema.dtype + }) + + @signature_safe_contextmanager + def apply(self, executor, need_restore=True): + """ + Apply moving average to parameters for evaluation. + Args: + executor (Executor): The Executor to execute applying. + need_restore (bool): Whether to restore parameters after applying. + """ + executor.run(self.apply_program) + try: + yield + finally: + if need_restore: + self.restore(executor) + + def restore(self, executor): + """Restore parameters. + Args: + executor (Executor): The Executor to execute restoring. + """ + executor.run(self.restore_program) diff --git a/tools/ema_clean.py b/tools/ema_clean.py new file mode 100644 index 000000000..e8ddc2e14 --- /dev/null +++ b/tools/ema_clean.py @@ -0,0 +1,42 @@ +#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import argparse +import functools +import shutil +import sys + +def main(): + cleaned_model_dir = sys.argv[1] + ema_model_dir = sys.argv[2] + if not os.path.exists(cleaned_model_dir): + os.makedirs(cleaned_model_dir) + + items = os.listdir(ema_model_dir) + for item in items: + if item.find('ema') > -1: + item_clean = item.replace('_ema_0', '') + shutil.copyfile(os.path.join(ema_model_dir, item), + os.path.join(cleaned_model_dir, item_clean)) + elif item.find('mean') > -1 or item.find('variance') > -1: + shutil.copyfile(os.path.join(ema_model_dir, item), + os.path.join(cleaned_model_dir, item)) + +if __name__ == '__main__': + main() diff --git a/tools/program.py b/tools/program.py index 50c609de1..f36c8c5c7 100644 --- a/tools/program.py +++ b/tools/program.py @@ -86,7 +86,7 @@ def create_dataloader(feeds): return dataloader -def create_model(architecture, image, classes_num): +def create_model(architecture, image, classes_num, is_train): """ Create a model @@ -101,6 +101,8 @@ def create_model(architecture, image, classes_num): """ name = architecture["name"] params = architecture.get("params", {}) + params['is_test'] = not is_train + print(params) model = architectures.__dict__[name](**params) out = model.net(input=image, class_dim=classes_num) return out @@ -323,7 +325,7 @@ def build(config, main_prog, startup_prog, is_train=True): feeds = create_feeds(config.image_shape, use_mix=use_mix) dataloader = create_dataloader(feeds.values()) out = create_model(config.ARCHITECTURE, feeds['image'], - config.classes_num) + config.classes_num, is_train) fetchs = create_fetchs( out, feeds, @@ -339,6 +341,12 @@ def build(config, main_prog, startup_prog, is_train=True): fetchs['lr'] = (lr, AverageMeter('lr', 'f', need_avg=False)) optimizer = dist_optimizer(config, optimizer) optimizer.minimize(fetchs['loss'][0]) + if config.get('use_ema'): + + global_steps = fluid.layers.learning_rate_scheduler._decay_step_counter() + ema = ExponentialMovingAverage(config.get('ema_decay'), thres_steps=global_steps) + ema.update() + fetchs['ema'] = ema return dataloader, fetchs diff --git a/tools/train.py b/tools/train.py index ab7752fd1..b188646a3 100644 --- a/tools/train.py +++ b/tools/train.py @@ -98,6 +98,14 @@ def main(args): if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0: # 2. validate with validate dataset if config.validate and epoch_id % config.valid_interval == 0: + if config.get('use_ema'): + logger.info(logger.coloring("EMA validate start...")) + with train_fetchs('ema').apply(exe): + top1_acc = program.run(valid_dataloader, exe, + compiled_valid_prog, valid_fetchs, + epoch_id, 'valid') + logger.info(logger.coloring("EMA validate over!")) + top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, epoch_id, 'valid') From 4ccfca291b09a01cb86725c7b49aa7f910f27adf Mon Sep 17 00:00:00 2001 From: shippingwang Date: Wed, 6 May 2020 11:17:39 +0000 Subject: [PATCH 02/75] add autoargument --- configs/EfficientNet/EfficientNetB0.yaml | 84 ++++++++++++++++++++++++ ppcls/data/imaug/operators.py | 13 ++++ ppcls/optimizer/learning_rate.py | 53 +++++++++++++++ 3 files changed, 150 insertions(+) create mode 100644 configs/EfficientNet/EfficientNetB0.yaml diff --git a/configs/EfficientNet/EfficientNetB0.yaml b/configs/EfficientNet/EfficientNetB0.yaml new file mode 100644 index 000000000..ae28a3cc6 --- /dev/null +++ b/configs/EfficientNet/EfficientNetB0.yaml @@ -0,0 +1,84 @@ +mode: 'train' +ARCHITECTURE: + name: "EfficientNetB0" + drop_connect_rate: 0.1 + padding_type : "SAME" +pretrained_model: "" +model_save_dir: "./output/" +classes_num: 1000 +total_images: 1281167 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 360 +topk: 5 +image_shape: [3, 224, 224] +use_ema: True +ema_decay: 0.9999 +use_aa: True +ls_epsilon: 0.1 + +LEARNING_RATE: + function: 'ExponentialWarmup' + params: + lr: 0.032 + +OPTIMIZER: + function: 'RMSProp' + params: + momentum: 0.9 + rho: 0.9 + epsilon: 0.001 + regularizer: + function: 'L2' + factor: 0.00001 + +TRAIN: + batch_size: 512 + num_workers: 4 + file_list: "./dataset/ILSVRC2012/train_list.txt" + data_dir: "./dataset/ILSVRC2012/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: Fals + channel_first: False + - RandCropImage: + size: 224 + - RandFlipImage: + flip_code: 1 + - AA: + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + + +VALID: + batch_size: 128 + num_workers: 4 + file_list: "./dataset/ILSVRC2012/val_list.txt" + data_dir: "./dataset/ILSVRC2012/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + interpolation: 2 + resize_short: 256 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + diff --git a/ppcls/data/imaug/operators.py b/ppcls/data/imaug/operators.py index a8454740f..7da406977 100644 --- a/ppcls/data/imaug/operators.py +++ b/ppcls/data/imaug/operators.py @@ -25,6 +25,7 @@ import random import cv2 import numpy as np +from autoargument import ImageNetPolicy class OperatorParamError(ValueError): """ OperatorParamError @@ -171,6 +172,18 @@ class RandFlipImage(object): else: return img +class AA(object): + + def __init__(self): + self.policy = ImageNetPolicy() + + def __call__(self,img): + from PIL import Image + img = np.ascontiguousarray(img) + img = Image.fromarray(img) + img = self.policy(img) + img = np.asarray(img) + class NormalizeImage(object): """ normalize image such as substract mean, divide std diff --git a/ppcls/optimizer/learning_rate.py b/ppcls/optimizer/learning_rate.py index 197f8af14..b6de18baf 100644 --- a/ppcls/optimizer/learning_rate.py +++ b/ppcls/optimizer/learning_rate.py @@ -145,6 +145,59 @@ class CosineWarmup(object): return learning_rate +class ExponentialWarmup(object): + + """ + Exponential learning rate decay with warmup + [0, warmup_epoch): linear warmup + [warmup_epoch, epochs): Exponential decay + + Args: + lr(float): initial learning rate + step_each_epoch(int): steps each epoch + decay_epochs(float): decay epochs + decay_rate(float): decay rate + warmup_epoch(int): epoch num of warmup + """ + + def __init__(self, lr, step_each_epoch, decay_epochs=2.4, decay_rate=0.97, warmup_epoch=5, **kwargs): + super(CosineWarmup, self).__init__() + self.lr = lr + self.step_each_epoch = step_each_epoch + self.decay_epochs = decay_epochs * self.step_each_epoch + self.decay_rate = decay_rate + self.warmup_epoch = fluid.layers.fill_constant( + shape=[1], + value=float(warmup_epoch), + dtype='float32', + force_cpu=True) + + def __call__(self): + global_step = _decay_step_counter() + learning_rate = fluid.layers.tensor.create_global_var( + shape=[1], + value=0.0, + dtype='float32', + persistable=True, + name="learning_rate") + + epoch = ops.floor(global_step / self.step_each_epoch) + with fluid.layers.control_flow.Switch() as switch: + with switch.case(epoch < self.warmup_epoch): + decayed_lr = self.lr * \ + (global_step / (self.step_each_epoch * self.warmup_epoch)) + fluid.layers.tensor.assign( + input=decayed_lr, output=learning_rate) + with switch.default(): + rest_step = global_step - self.warmup_epoch * self.step_each_epoch + div_res = ops.floor(rest_step / self.decay_epochs) + + decayed_lr = self.lr*(self.decay_rate**div_res) + fluid.layers.tensor.assign( + input=decayed_lr, output=learning_rate) + + return learning_rate + class LearningRateBuilder(): """ Build learning rate variable From a1983251e7833ba67794c859b05175d6d0325cef Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Tue, 26 May 2020 07:11:38 +0000 Subject: [PATCH 03/75] add readme en doc --- README_en.md | 155 +++++++++++++++++++++++++++++ docs/images/main_features_s_en.png | Bin 0 -> 173544 bytes 2 files changed, 155 insertions(+) create mode 100644 README_en.md create mode 100644 docs/images/main_features_s_en.png diff --git a/README_en.md b/README_en.md new file mode 100644 index 000000000..804fbfe6d --- /dev/null +++ b/README_en.md @@ -0,0 +1,155 @@ +# PaddleClas + +**Book**: https://paddleclas.readthedocs.io + +**Quick start PaddleClas in 30 minutes**: https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/quick_start.html + +## Introduction + +PaddleClas is a tool set for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply in real scenarios. + +
+ +
+ +## Rich model zoo + +Based on ImageNet1k dataset, PaddleClas provides a brief introduction to 23 series of imaget classification networks such as ResNet, ResNet_vd, Res2Net, HRNet, and MobileNetV3 besides their reproduction configurations and training techniques. At the same time, the corresponding 117 image classification pretrained models are also open source. The GPU inference time of the server-side model is evaluated based on TensorRT. The CPU inference time and the mobile-side model storage size are evaluated on the Snapdragon 855 (SD855). For more detailed information of the supported pretrained models and their download links, please refer to [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html). + + +
+ +
+ +The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **82.4%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the infecne speed of T4 GPU with different batch size), please refer to the documentation tutorial formal details: [https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) + + +
+ +
+ + +The above figure shows performance of some commonly used mobile-side models, including MobileNetV1, MobileNetV2, MobileNetV3 and ShuffleNetV2 series. The inferece time is tested on Snapdragon 855 (SD855) with the batch size set as 1. the `Top1` accuracy of the MV3_large_x1_0_ssld, MV3_small_x1_0_ssld, MV1_ssld and MV2_ssld pretrained model on ImageNet1k-val dataset are 79%, 71.3%, 76.74%, 77.89%, respectively (M is short for MobileNet). MV3_large_x1_0_ssld_int8 is a quantizatied pretrained model for MV3_large_x1_0. More details about the mobile-side models can be seen in [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) + +- TODO +- [ ] Reproduction and performance evaluation of EfficientLite, GhostNet and RegNet. + + +## High-level support + +In addition to providing rich classification network structures and pretrained models, PaddleClas also supports a series of algorithms or tools that help to improve the effectiveness and efficiency of image classification tasks. + +### knowledge distillation (SSLD) + +Knowledge distillation refers to using the teacher model to guide the student model to learn specific tasks, ensuring that the small model has a relatively large effect improvement with the computation cost unchanged, and even obtains similar accuracy with the large model. PaddleClas provides a Simple Semi-supervised Label Distillation method (SSLD). With this method, different models on ImageNet1k-val dataset has 3% absolute improvement(Top1 accuracy) on ImageNet1k-val dataset. The following figure shows the models' performance after SSLD. + +
+ +
+ +Taking the ImageNet1k dataset as an example, the following figure shows the SSLD knowledge distillation method framework. The key points of the method include the choice of teacher model, loss calculation method, iteration number, use of unlabeled data, and ImageNet1k dataset finetune. For detailed introduction and experiments, please refer to [**knowledge distillation tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/distillation/index.html) + +
+ +
+ +### Data augmentation + +For a certain image classification task, data augmentation is a commonly used regularization method, which can effectively improve the effect of image classification, especially for scenarios where the data is insufficient or the network is large. Commonly used data augmentation can be divided into 3 categories, `transformation`, `cropping` and `aliasing`, as shown below. The image transformation refers to performing some transformations on the entire image, such as AutoAugment and RandAugment. The image cropping refers to the transformation of the image to block some areas in a certain way, such as CutOut, RandErasing, HideAndSeek and GridMask. Image aliasing refers to the transformation of multiple images to alias a new image, such as Mixup and Cutmix. + + +
+ +
+ + +PaddleClas provides the reproduction of the above 8 data augmentation algorithms and the evaluation of the effect in a unified environment. The following figure shows the performance of different data augmentation methods based on ResNet50. Compared with the standard transformation, using data augmentation, the recognition accuracy can be increased by up to 1%. For more detailed introduction of data augmentation methods, please refer to the [**data augmentation tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/image_augmentation/index.html). + + +
+ +
+ + +## Quick start + +Based on flowers102 dataset, you can easily experience different networks, pretrained models and SSLD knowledge distillation method in PaddleClas. More details can be seen in [**Quick start PaddleClas in 30 minutes**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/quick_start.html). + + +## Getting started + +For installation, model training, inference, evaluation and finetune in PaddleClas, you can refer to [**gettting started tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/index.html). + + +## Featured extension and application + +### A classification pretrained model with 100K categories + +The models trained on ImageNet1K dataset are often used as pretrained models for other classification tasks in practical applications due to lack of training data. However, there are only 1,000 categories in the ImageNet1K dataset, and the feature migration capability of the pretrained model is limited. Therefore, Baidu developed a tag system including 100,000 categories, with semantic information and different granularity. Through manual or semi-supervised methods, more than 55,000,000 training images have been collectet. The system is the largest image classification system and training set in the country and even the world. PaddleClas provides the ResNet50_vd model trained on this dataset. The following table shows the comparison of using the ImageNet pretrained model and the above 100,000 image classification pretrained model in some practical application scenarios. Using the 100,000 image classification pretrained model, the recognition accuracy can be increased by up to 30%. + +| Dataset | Dataset statistics | ImageNet pretrained model | 100,000-categories' pretrained model | +|:--:|:--:|:--:|:--:| +| Flowers | class_num:102
train/val:5789/2396 | 0.7779 | 0.9892 | +| Hand-painted stick figures | class_num:18
train/val:1007/432 | 0.8785 | 0.9107 | +| Leaves | class_num:6
train/val:5256/2278 | 0.8212 | 0.8385 | +| Container vehicle | class_num:115
train/val:4879/2094 | 0.623 | 0.9524 | +| Chair | class_num:5
train/val:169/78 | 0.8557 | 0.9077 | +| Geology | class_num:4
train/val:671/296 | 0.5719 | 0.6781 | + + +The 100,000 categories' pretrained model can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_10w_pretrained.tar). More details can be seen in [**Transfer learning tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/transfer_learning.html). + + +### Object detection + +In recent years, object detection tasks atrtract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model n object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Pratical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while take limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html). + + +
+ +
+ +- TODO +- [ ] Application of PaddleClas in OCR tasks. +- [ ] Application of PaddleClas in face detection and recognition tasks. + + + +## Industrial-grade application deployment tools + +PaddlePaddle provides a series of practical tools to conveniently deploy the PaddleClas models for industrial applications. For more details, please refer to [**extension tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/extension/index.html) + +- TensorRT inference +- Paddle-Lite +- Paddle-Serving +- Model quantization +- Multi-machine training +- Paddle Hub + +## Competition support + +PaddleClas stems from the Baidu's visual business applications and the exploration of frontier visual capabilities. It has helped us achieve leading results in many key events, and continues to promote more frontier visual solutions and landing applications. For more details, please refer to [**competition support tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/competition_support.html) + + +- 1st place in 2018 Kaggle Open Images V4 object detection challenge +- A-level certificate of three tasks: printed text OCR, face recognition and landmark recognition in the first multimedia information recognition technology competition +- 2nd place in 2019 Kaggle Open Images V5 object detection challenge +- 2nd place in Kaggle Landmark Retrieval Challenge 2019 +- 2nd place in Kaggle Landmark Recognition Challenge 2019 + + +## License + +PaddleClas is released under the Apache 2.0 license + +## Updates + +## Contributing + +Contributions are highly welcomed and we would really appreciate your feedback!! diff --git a/docs/images/main_features_s_en.png b/docs/images/main_features_s_en.png new file mode 100644 index 0000000000000000000000000000000000000000..643fb7375c928e8cc4df2b7ad1ac8383e8687a93 GIT binary patch literal 173544 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industry and academia. It helps users train better computer vision models and apply in real scenarios. +PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
@@ -14,14 +14,14 @@ PaddleClas is a tool set for image classification tasks prepared for the industr ## Rich model zoo -Based on ImageNet1k dataset, PaddleClas provides a brief introduction to 23 series of imaget classification networks such as ResNet, ResNet_vd, Res2Net, HRNet, and MobileNetV3 besides their reproduction configurations and training techniques. At the same time, the corresponding 117 image classification pretrained models are also open source. The GPU inference time of the server-side model is evaluated based on TensorRT. The CPU inference time and the mobile-side model storage size are evaluated on the Snapdragon 855 (SD855). For more detailed information of the supported pretrained models and their download links, please refer to [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html). +Based on ImageNet1k dataset, PaddleClas provides a brief introduction to 23 series of image classification networks such as ResNet, ResNet_vd, Res2Net, HRNet, and MobileNetV3 besides their reproduction configurations and training techniques. At the same time, the corresponding 117 image classification pretrained models are also open source. The GPU inference time of the server-side model is evaluated based on TensorRT. The CPU inference time and the mobile-side model storage size are evaluated on the Snapdragon 855 (SD855). For more detailed information on the supported pretrained models and their download links, please refer to [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html).
-The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **82.4%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the infecne speed of T4 GPU with different batch size), please refer to the documentation tutorial formal details: [https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) +The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **82.4%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the inference speed of T4 GPU with different batch size), please refer to the documentation tutorial formal details: [https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)
@@ -30,7 +30,7 @@ src="./docs/images/models/mobile_arm_top1.png" width="700">
-The above figure shows performance of some commonly used mobile-side models, including MobileNetV1, MobileNetV2, MobileNetV3 and ShuffleNetV2 series. The inferece time is tested on Snapdragon 855 (SD855) with the batch size set as 1. the `Top1` accuracy of the MV3_large_x1_0_ssld, MV3_small_x1_0_ssld, MV1_ssld and MV2_ssld pretrained model on ImageNet1k-val dataset are 79%, 71.3%, 76.74%, 77.89%, respectively (M is short for MobileNet). MV3_large_x1_0_ssld_int8 is a quantizatied pretrained model for MV3_large_x1_0. More details about the mobile-side models can be seen in [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) +The above figure shows the performance of some commonly used mobile-side models, including MobileNetV1, MobileNetV2, MobileNetV3 and ShuffleNetV2 series. The inference time is tested on Snapdragon 855 (SD855) with the batch size set as 1. the `Top1` accuracy of the MV3_large_x1_0_ssld, MV3_small_x1_0_ssld, MV1_ssld and MV2_ssld pretrained model on ImageNet1k-val dataset are 79%, 71.3%, 76.74%, 77.89%, respectively (M is short for MobileNet). MV3_large_x1_0_ssld_int8 is a quantizatied pretrained model for MV3_large_x1_0. More details about the mobile-side models can be seen in [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) - TODO - [ ] Reproduction and performance evaluation of EfficientLite, GhostNet and RegNet. @@ -78,7 +78,7 @@ src="./docs/images/image_aug/main_image_aug_s.jpg" width="600"> ## Quick start -Based on flowers102 dataset, you can easily experience different networks, pretrained models and SSLD knowledge distillation method in PaddleClas. More details can be seen in [**Quick start PaddleClas in 30 minutes**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/quick_start.html). +Based on flowers102 dataset, one can easily experience different networks, pretrained models and SSLD knowledge distillation method in PaddleClas. More details can be seen in [**Quick start PaddleClas in 30 minutes**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/quick_start.html). ## Getting started @@ -90,7 +90,7 @@ For installation, model training, inference, evaluation and finetune in PaddleCl ### A classification pretrained model with 100K categories -The models trained on ImageNet1K dataset are often used as pretrained models for other classification tasks in practical applications due to lack of training data. However, there are only 1,000 categories in the ImageNet1K dataset, and the feature migration capability of the pretrained model is limited. Therefore, Baidu developed a tag system including 100,000 categories, with semantic information and different granularity. Through manual or semi-supervised methods, more than 55,000,000 training images have been collectet. The system is the largest image classification system and training set in the country and even the world. PaddleClas provides the ResNet50_vd model trained on this dataset. The following table shows the comparison of using the ImageNet pretrained model and the above 100,000 image classification pretrained model in some practical application scenarios. Using the 100,000 image classification pretrained model, the recognition accuracy can be increased by up to 30%. +The models trained on ImageNet1K dataset are often used as pretrained models for other classification tasks in practical applications due to lack of training data. However, there are only 1,000 categories in the ImageNet1K dataset, and the feature migration capability of the pretrained model is limited. Therefore, Baidu developed a tag system including 100,000 categories, with semantic information and different granularity. Through manual or semi-supervised methods, more than 55,000,000 training images have been collected. It is the largest image classification system in China and even in the worldwide. PaddleClas provides the ResNet50_vd model trained on this dataset. The following table shows the comparison of using the ImageNet pretrained model and the above 100,000 image classification pretrained model in some practical application scenarios. Using the 100,000 image classification pretrained model, the recognition accuracy can be increased by up to 30%. | Dataset | Dataset statistics | ImageNet pretrained model | 100,000-categories' pretrained model | |:--:|:--:|:--:|:--:| @@ -107,7 +107,7 @@ The 100,000 categories' pretrained model can be downloaded here: [download link] ### Object detection -In recent years, object detection tasks atrtract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model n object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Pratical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while take limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html). +In recent years, object detection tasks attract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model in object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Practical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while taking limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html).
From cfca7647576c3f7689494adc0206b1a98b47dfb7 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Thu, 28 May 2020 07:35:07 +0000 Subject: [PATCH 05/75] fix doc --- README_en.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_en.md b/README_en.md index f9c6aae32..182220950 100644 --- a/README_en.md +++ b/README_en.md @@ -42,7 +42,7 @@ In addition to providing rich classification network structures and pretrained m ### knowledge distillation (SSLD) -Knowledge distillation refers to using the teacher model to guide the student model to learn specific tasks, ensuring that the small model has a relatively large effect improvement with the computation cost unchanged, and even obtains similar accuracy with the large model. PaddleClas provides a Simple Semi-supervised Label Distillation method (SSLD). With this method, different models on ImageNet1k-val dataset has 3% absolute improvement(Top1 accuracy) on ImageNet1k-val dataset. The following figure shows the models' performance after SSLD. +Knowledge distillation refers to using the teacher model to guide the student model to learn specific tasks, ensuring that the small model has a relatively large effect improvement with the computation cost unchanged, and even obtains similar accuracy with the large model. PaddleClas provides a Simple Semi-supervised Label Distillation method (SSLD). With this method, different models on ImageNet1k-val dataset have 3% absolute improvement(Top1 accuracy) on ImageNet1k-val dataset. The following figure shows the models' performance after SSLD.
Date: Fri, 29 May 2020 15:11:37 +0800 Subject: [PATCH 06/75] Update README.md --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 227c86c25..c78e28f86 100644 --- a/README.md +++ b/README.md @@ -97,10 +97,6 @@ PaddleClas的安装说明、模型训练、预测、评估以及模型微调(f 近年来,学术界和工业界广泛关注图像中目标检测任务,而图像分类的网络结构以及预训练模型效果直接影响目标检测的效果。PaddleDetection使用PaddleClas的82.39%的ResNet50_vd的预训练模型,结合自身丰富的检测算子,提供了一种面向服务器端应用的目标检测方案,PSS-DET (Practical Server Side Detection)。该方案融合了多种只增加少许计算量,但是可以有效提升两阶段Faster RCNN目标检测效果的策略,包括检测模型剪裁、使用分类效果更优的预训练模型、DCNv2、Cascade RCNN、AutoAugment、Libra sampling以及多尺度训练。其中基于82.39%的R50_vd_ssld预训练模型,与79.12%的R50_vd的预训练模型相比,检测效果可以提升1.5%。在COCO目标检测数据集上测试PSS-DET,当V100单卡预测速度为61FPS时,mAP是41.6%,预测速度为20FPS时,mAP是47.8%。详情请参考[**通用目标检测章节**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html)。 -
- -
- TODO - [ ] PaddleClas在OCR任务中的应用 From a559cda33153de129b1550239e755556504f0455 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Fri, 29 May 2020 07:42:14 +0000 Subject: [PATCH 07/75] fix en doc --- README.md | 2 +- README_en.md | 22 +++++++----------- .../image_aug/image_aug_samples_s_en.jpg | Bin 0 -> 1381853 bytes docs/images/main_features_s_en.png | Bin 173544 -> 105529 bytes 4 files changed, 10 insertions(+), 14 deletions(-) create mode 100644 docs/images/image_aug/image_aug_samples_s_en.jpg diff --git a/README.md b/README.md index 227c86c25..4ead4ccad 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ src="./docs/images/models/mobile_arm_top1.png" width="700"> 上图对比了一些最新的面向移动端应用场景的模型,在骁龙855(SD855)上预测一张图像的时间和其准确率,包括MobileNetV1系列、MobileNetV2系列、MobileNetV3系列和ShuffleNetV2系列。图中准确率79%的MV3_large_x1_0_ssld(M是MobileNet的简称),71.3%的MV3_small_x1_0_ssld、76.74%的MV2_ssld和77.89%的MV1_ssld,是采用PaddleClas提供的SSLD蒸馏方法训练的模型。MV3_large_x1_0_ssld_int8是进一步进行INT8量化的模型。不同模型的简介、FLOPS、Parameters和模型存储大小请参考文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。 - TODO -- [ ] EfficientLite、GhostNet、RegNet论文指标复现和性能评估 +- [ ] EfficientLite、GhostNet、RegNet、ResNeSt的论文指标复现和性能评估 ## 高阶优化支持 除了提供丰富的分类网络结构和预训练模型,PaddleClas也支持了一系列有助于图像分类任务效果和效率提升的算法或工具。 diff --git a/README_en.md b/README_en.md index 182220950..142d5983e 100644 --- a/README_en.md +++ b/README_en.md @@ -14,14 +14,14 @@ PaddleClas is a toolset for image classification tasks prepared for the industry ## Rich model zoo -Based on ImageNet1k dataset, PaddleClas provides a brief introduction to 23 series of image classification networks such as ResNet, ResNet_vd, Res2Net, HRNet, and MobileNetV3 besides their reproduction configurations and training techniques. At the same time, the corresponding 117 image classification pretrained models are also open source. The GPU inference time of the server-side model is evaluated based on TensorRT. The CPU inference time and the mobile-side model storage size are evaluated on the Snapdragon 855 (SD855). For more detailed information on the supported pretrained models and their download links, please refer to [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html). +Based on ImageNet1k dataset, PaddleClas provides 23 series of image classification networks such as ResNet, ResNet_vd, Res2Net, HRNet, and MobileNetV3 with brief introductions, reproduction configurations and training tricks. At the same time, the corresponding 117 image classification pretrained models are also available. The GPU inference time of the server-side models are evaluated based on TensorRT. The CPU inference time and storage size of the mobile-side models are evaluated on the Snapdragon 855 (SD855). For more detailed information on the supported pretrained models and their download links, please refer to [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html).
-The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **82.4%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the inference speed of T4 GPU with different batch size), please refer to the documentation tutorial formal details: [https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) +The above figure shows some of the latest server-side pretrained models. It can be seen from the figure that when using V100 GPU with FP32 and TensorRT, the `Top1` accuracy of the ResNet50_vd_ssld pretrained model on ImageNet1k-val dataset is **82.4%** and that of ResNet101_vd_ssld pretrained model is 83.7%. These pretained models are obtained from SSLD knowledge distillation solution provided by PaddleClas. The marks of the same color and symbol in the figure represent models of different model sizes in the same series. For the introduction of different models, FLOPS, Params and detailed GPU inference time (including the inference speed of T4 GPU with different batch size), please refer to the documentation tutorial for more details: [https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)
@@ -30,20 +30,21 @@ src="./docs/images/models/mobile_arm_top1.png" width="700">
-The above figure shows the performance of some commonly used mobile-side models, including MobileNetV1, MobileNetV2, MobileNetV3 and ShuffleNetV2 series. The inference time is tested on Snapdragon 855 (SD855) with the batch size set as 1. the `Top1` accuracy of the MV3_large_x1_0_ssld, MV3_small_x1_0_ssld, MV1_ssld and MV2_ssld pretrained model on ImageNet1k-val dataset are 79%, 71.3%, 76.74%, 77.89%, respectively (M is short for MobileNet). MV3_large_x1_0_ssld_int8 is a quantizatied pretrained model for MV3_large_x1_0. More details about the mobile-side models can be seen in [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) +The above figure shows the performance of some commonly used mobile-side models, including MobileNetV1, MobileNetV2, MobileNetV3 and ShuffleNetV2 series. The inference time is tested on Snapdragon 855 (SD855) with the batch size set as 1. The `Top1` accuracy of the MV3_large_x1_0_ssld, MV3_small_x1_0_ssld, MV1_ssld and MV2_ssld pretrained model on ImageNet1k-val dataset are 79%, 71.3%, 76.74%, 77.89%, respectively (M is short for MobileNet). MV3_large_x1_0_ssld_int8 is a quantizatied pretrained model for MV3_large_x1_0. More details about the mobile-side models can be seen in [**models introduction tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html) - TODO -- [ ] Reproduction and performance evaluation of EfficientLite, GhostNet and RegNet. +- [ ] Reproduction and performance evaluation of EfficientLite, GhostNet, RegNet and ResNeSt. ## High-level support -In addition to providing rich classification network structures and pretrained models, PaddleClas also supports a series of algorithms or tools that help to improve the effectiveness and efficiency of image classification tasks. +In addition to providing rich classification network structures and pretrained models, PaddleClas supports a series of algorithms or tools that help to improve the effectiveness and efficiency of image classification tasks. ### knowledge distillation (SSLD) Knowledge distillation refers to using the teacher model to guide the student model to learn specific tasks, ensuring that the small model has a relatively large effect improvement with the computation cost unchanged, and even obtains similar accuracy with the large model. PaddleClas provides a Simple Semi-supervised Label Distillation method (SSLD). With this method, different models on ImageNet1k-val dataset have 3% absolute improvement(Top1 accuracy) on ImageNet1k-val dataset. The following figure shows the models' performance after SSLD. +
@@ -63,7 +64,7 @@ For a certain image classification task, data augmentation is a commonly used re
+src="./docs/images/image_aug/image_aug_samples_s_en.jpg" width="800">
@@ -88,9 +89,9 @@ For installation, model training, inference, evaluation and finetune in PaddleCl ## Featured extension and application -### A classification pretrained model with 100K categories +### A classification pretrained model for 100,000 categories -The models trained on ImageNet1K dataset are often used as pretrained models for other classification tasks in practical applications due to lack of training data. However, there are only 1,000 categories in the ImageNet1K dataset, and the feature migration capability of the pretrained model is limited. Therefore, Baidu developed a tag system including 100,000 categories, with semantic information and different granularity. Through manual or semi-supervised methods, more than 55,000,000 training images have been collected. It is the largest image classification system in China and even in the worldwide. PaddleClas provides the ResNet50_vd model trained on this dataset. The following table shows the comparison of using the ImageNet pretrained model and the above 100,000 image classification pretrained model in some practical application scenarios. Using the 100,000 image classification pretrained model, the recognition accuracy can be increased by up to 30%. +The models trained on ImageNet1K dataset are often used as pretrained models for other classification tasks in practical applications due to lack of training data. However, there are only 1,000 categories in the ImageNet1K dataset, and the feature capability of the pretrained model is limited. Therefore, Baidu developed a tag system including 100,000 categories, with semantic information and different granularity. Through manual or semi-supervised methods, more than 55,000,000 training images have been collected. It is the largest image classification system in China and even in the worldwide. PaddleClas provides the ResNet50_vd model trained on this dataset. The following table shows the comparison of using the ImageNet pretrained model and the above 100,000 image classification pretrained model in some practical application scenarios. Using the 100,000 image classification pretrained model, the recognition accuracy can be increased by up to 30%. | Dataset | Dataset statistics | ImageNet pretrained model | 100,000-categories' pretrained model | |:--:|:--:|:--:|:--:| @@ -110,11 +111,6 @@ The 100,000 categories' pretrained model can be downloaded here: [download link] In recent years, object detection tasks attract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model in object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Practical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while taking limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html). -
- -
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-Taking the ImageNet1k dataset as an example, the following figure shows the SSLD knowledge distillation method framework. The key points of the method include the choice of teacher model, loss calculation method, iteration number, use of unlabeled data, and ImageNet1k dataset finetune. For detailed introduction and experiments, please refer to [**knowledge distillation tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/distillation/index.html) +Taking the ImageNet1k dataset as an example, the following figure shows the SSLD knowledge distillation method framework. The key points of the method include the choice of teacher model, loss calculation method, iteration number, use of unlabeled data, and ImageNet1k dataset finetune. For detailed introduction and experiments, please refer to [**knowledge distillation tutorial**](https://paddleclas-en.readthedocs.io/en/latest/advanced_tutorials/distillation/distillation_en.html)
-PaddleClas provides the reproduction of the above 8 data augmentation algorithms and the evaluation of the effect in a unified environment. The following figure shows the performance of different data augmentation methods based on ResNet50. Compared with the standard transformation, using data augmentation, the recognition accuracy can be increased by up to 1%. For more detailed introduction of data augmentation methods, please refer to the [**data augmentation tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/image_augmentation/index.html). +PaddleClas provides the reproduction of the above 8 data augmentation algorithms and the evaluation of the effect in a unified environment. The following figure shows the performance of different data augmentation methods based on ResNet50. Compared with the standard transformation, using data augmentation, the recognition accuracy can be increased by up to 1%. For more detailed introduction of data augmentation methods, please refer to the [**data augmentation tutorial**](https://paddleclas-en.readthedocs.io/en/latest/advanced_tutorials/image_augmentation/ImageAugment_en.html).
@@ -83,12 +83,12 @@ src="./docs/images/image_aug/main_image_aug_s.jpg" width="600"> ## Quick start -Based on flowers102 dataset, one can easily experience different networks, pretrained models and SSLD knowledge distillation method in PaddleClas. More details can be seen in [**Quick start PaddleClas in 30 minutes**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/quick_start.html). +Based on flowers102 dataset, one can easily experience different networks, pretrained models and SSLD knowledge distillation method in PaddleClas. More details can be seen in [**Quick start PaddleClas in 30 minutes**](https://paddleclas-en.readthedocs.io/en/latest/tutorials/quick_start_en.html). ## Getting started -For installation, model training, inference, evaluation and finetune in PaddleClas, you can refer to [**gettting started tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/tutorials/index.html). +For installation, model training, inference, evaluation and finetune in PaddleClas, you can refer to [**gettting started tutorial**](https://paddleclas-en.readthedocs.io/en/latest/tutorials/index.html). ## Featured extension and application @@ -107,12 +107,12 @@ The models trained on ImageNet1K dataset are often used as pretrained models for | Geology | class_num:4
train/val:671/296 | 0.5719 | 0.6781 | -The 100,000 categories' pretrained model can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_10w_pretrained.tar). More details can be seen in [**Transfer learning tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/transfer_learning.html). +The 100,000 categories' pretrained model can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_10w_pretrained.tar). More details can be seen in [**Transfer learning tutorial**](https://paddleclas-en.readthedocs.io/en/latest/application/transfer_learning_en.html). ### Object detection -In recent years, object detection tasks attract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model in object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Practical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while taking limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas.readthedocs.io/zh_CN/latest/application/object_detection.html). +In recent years, object detection tasks attract a lot of attention in academia and industry. The ImageNet classification model is often used for pretrained model in object detection, which can directly affect the effect of object detection. Based on 82.39% ResNet50_vd pretrained model, PaddleDetection provides a Practical Server-side Detection solution, PSS-DET. The solution contains many strategies that can effectively improve the performance while taking limited extra computation cost, such as model pruning, better pretrained model, deformable convolution, cascade rcnn, autoaugment, libra sampling and multi-scale training. Compared with the 79.12% ImageNet1k pretrained model, the 82.39% model can help improve the COCO mAP by 1.5% without any computation cost. Using PSS-DET, the inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%, and reach 61FPS when COCO mAP is 41.6%. For more details, please refer to [**Object Detection tutorial**](https://paddleclas-en.readthedocs.io/en/latest/application/object_detection_en.html). - TODO diff --git a/docs/en/competition_support_en.md b/docs/en/competition_support_en.md new file mode 100644 index 000000000..da63d960b --- /dev/null +++ b/docs/en/competition_support_en.md @@ -0,0 +1,21 @@ +### Competition Support + +PaddleClas stems from the Baidu's visual business applications and the exploration of frontier visual capabilities. It has helped us achieve leading results in many key events, and continues to promote more frontier visual solutions and landing applications. + + +* 1st place in 2018 Kaggle Open Images V4 object detection challenge + + +* 2nd place in 2019 Kaggle Open Images V5 object detection challenge + * The report is avaiable here: [https://arxiv.org/pdf/1911.07171.pdf](https://arxiv.org/pdf/1911.07171.pdf) + * The pretrained model and code is avaiable here: [source code](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/featured_model/OIDV5_BASELINE_MODEL.md) + +* 2nd place in Kacggle Landmark Retrieval Challenge 2019 + * The report is avaiable here: [https://arxiv.org/abs/1906.03990](https://arxiv.org/abs/1906.03990) + * The pretrained model and code is avaiable here: [source code](https://github.com/PaddlePaddle/Research/tree/master/CV/landmark) + +* 2nd place in Kaggle Landmark Recognition Challenge 2019 + * The report is avaiable here: [https://arxiv.org/abs/1906.03990](https://arxiv.org/abs/1906.03990) + * The pretrained model and code is avaiable here: [source code](https://github.com/PaddlePaddle/Research/tree/master/CV/landmark) + +* A-level certificate of three tasks: printed text OCR, face recognition and landmark recognition in the first multimedia information recognition technology competition diff --git a/docs/en/faq_en.md b/docs/en/faq_en.md new file mode 100644 index 000000000..70d73e651 --- /dev/null +++ b/docs/en/faq_en.md @@ -0,0 +1,48 @@ +# FAQ + +>> +* Why are the metrics different for different cards? +* A: Fleet is the default option for the use of PaddleClas. Each GPU card is taken as a single trainer and deals with different images, which cause the final small difference. Single card evalution is suggested to get the accurate results if you use `tools/eval.py`. You can also use `tools/eval_multi_platform.py` to evalute the models on multiple GPU cards, which is also supported on Windows and CPU. + + +>> +* Q: Why `Mixup` or `Cutmix` is not used even if I have already add the data operation in the configuration file? +* A: When using `Mixup` or `Cutmix`, you also need to add `use_mix: True` in the configuration file to make it work properly. + + +>> +* Q: During evaluation and inference, pretrained model address is assgined, but the weights can not be imported. Why? +* A: Prefix of the pretrained model is needed. For example, if the pretained weights are located in `output/ResNet50_vd/19`, with the filename `output/ResNet50_vd/19/ppcls.pdparams`, then `pretrained_model` in the configuration file needs to be `output/ResNet50_vd/19/ppcls`. + +>> +* Q: Why are the metrics 0.3% lower than that shown in the model zoo for `EfficientNet` series of models? +* A: Resize method is set as `Cubic` for `EfficientNet`(interpolation is set as 2 in OpenCV), while other models are set as `Bilinear`(interpolation is set as None in OpenCV). Therefore, you need to modify the interpolation explicitly in `ResizeImage`. Specifically, the following configuration is a demo for EfficientNet. + +``` +VALID: + batch_size: 16 + num_workers: 4 + file_list: "./dataset/ILSVRC2012/val_list.txt" + data_dir: "./dataset/ILSVRC2012/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 256 + interpolation: 2 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: +``` + +>> +* Q: What should I do if I want to transform the weights' format from `pdparams` to an earlier version(before Paddle1.7.0), which consists of the scattered files? +* A: You can use `fluid.load` to load the `pdparams` weights and use `fluid.io.save_vars` to save the weights as scattered files. diff --git a/docs/en/index.rst b/docs/en/index.rst index 2b5a7fd7f..b8a2e9096 100644 --- a/docs/en/index.rst +++ b/docs/en/index.rst @@ -11,7 +11,7 @@ Welcome to PaddleClas! advanced_tutorials/index application/index extension/index - competition_support.md + competition_support_en.md update_history_en.md faq_en.md From 8ae48bb4273ece54c6dfff27787ae69e069759fa Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Thu, 18 Jun 2020 09:17:12 +0000 Subject: [PATCH 60/75] add 84.0r50_vd doc --- README.md | 2 +- .../distillation/distillation_en.md | 12 +++++++++++- .../distillation/distillation_perform_s.jpg | Bin 53917 -> 68629 bytes .../T4_benchmark/t4.fp16.bs4.ResNet.png | Bin 85865 -> 86119 bytes .../T4_benchmark/t4.fp32.bs4.ResNet.flops.png | Bin 80224 -> 80457 bytes .../t4.fp32.bs4.ResNet.params.png | Bin 82419 -> 82670 bytes .../T4_benchmark/t4.fp32.bs4.ResNet.png | Bin 84849 -> 85323 bytes .../t4.fp32.bs4.main_fps_top1.png | Bin 154367 -> 154457 bytes .../distillation/distillation.md | 16 +++++++++++++--- 9 files changed, 25 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4ed14b60a..965d1b3dc 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@
-上图对比了一些最新的面向服务器端应用场景的模型,在使用V100,FP32和TensorRT,batch size为1时的预测时间及其准确率,图中准确率82.4%的ResNet50_vd_ssld和83.7%的ResNet101_vd_ssld,是采用PaddleClas提供的SSLD知识蒸馏方案训练的模型。图中相同颜色和符号的点代表同一系列不同规模的模型。不同模型的简介、FLOPS、Parameters以及详细的GPU预测时间(包括不同batchsize的T4卡预测速度)请参考文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。 +上图对比了一些最新的面向服务器端应用场景的模型,在使用V100,FP32和TensorRT,batch size为1时的预测时间及其准确率,图中准确率83.0%的ResNet50_vd_ssld_v2和83.7%的ResNet101_vd_ssld,是采用PaddleClas提供的SSLD知识蒸馏方案训练的模型,其中v2表示在训练时添加了AutoAugment数据增广策略。图中相同颜色和符号的点代表同一系列不同规模的模型。不同模型的简介、FLOPS、Parameters以及详细的GPU预测时间(包括不同batchsize的T4卡预测速度)请参考文档教程中的[**模型库章节**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。
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