From 84fc7d12557822aef6311d31b3a6a1ed09d54a9e Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Thu, 17 Sep 2020 22:47:36 +0800 Subject: [PATCH] Change README to cn (#1430) test=document_fix --- README.md | 177 +-------------------------------------------------- README_cn.md | 2 +- README_en.md | 176 ++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 178 insertions(+), 177 deletions(-) mode change 100644 => 120000 README.md create mode 100644 README_en.md diff --git a/README.md b/README.md deleted file mode 100644 index 184962734..000000000 --- a/README.md +++ /dev/null @@ -1,176 +0,0 @@ -English | [简体中文](README_cn.md) - -Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io) - -# PaddleDetection - -PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which -aims to help developers in the whole development of training models, optimizing performance and -inference speed, and deploying models. PaddleDetection provides varied object detection architectures -in modular design, and wealthy data augmentation methods, network components, loss functions, etc. -PaddleDetection supported practical projects such as industrial quality inspection, remote sensing -image object detection, and automatic inspection with its practical features such as model compression -and multi-platform deployment. - -[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4, -has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single -Tesla V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details. - -**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.** - -
- -
- - -## Introduction - -Features: - -- Rich models: - - PaddleDetection provides rich of models, including 100+ pre-trained models -such as object detection, instance segmentation, face detection etc. It covers -the champion models, the practical detection models for cloud and edge device. - -- Production Ready: - - Key operations are implemented in C++ and CUDA, together with PaddlePaddle's -highly efficient inference engine, enables easy deployment in server environments. - -- Highly Flexible: - - Components are designed to be modular. Model architectures, as well as data -preprocess pipelines, can be easily customized with simple configuration -changes. - -- Performance Optimized: - - With the help of the underlying PaddlePaddle framework, faster training and -reduced GPU memory footprint is achieved. Notably, YOLOv3 training is -much faster compared to other frameworks. Another example is Mask-RCNN -(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during -multi-GPU training. - -Supported Architectures: - -| | ResNet | ResNet-vd [1](#vd) | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net | -| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----: | -| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | -| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | -| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | -| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | -| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | -| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | -| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | -| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | -| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | -| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | -| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | -| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | - -[1] [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost. - -**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally. - -More models: - -- EfficientDet -- FCOS -- CornerNet-Squeeze -- YOLOv4 -- PP-YOLO - -More Backbones: - -- DarkNet -- VGG -- GCNet -- CBNet - -Advanced Features: - -- [x] **Synchronized Batch Norm** -- [x] **Group Norm** -- [x] **Modulated Deformable Convolution** -- [x] **Deformable PSRoI Pooling** -- [x] **Non-local and GCNet** - -**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device. - -The following is the relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones. - -
- -
- -**NOTE:** -- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models -- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% -- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework -- All these models can be get in [Model Zoo](#Model-Zoo) - -The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details. - -
- -
- -## Tutorials - - -### Get Started - -- [Installation guide](docs/tutorials/INSTALL.md) -- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md) -- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md) -- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md) -- [FAQ](docs/FAQ.md) - -### Advanced Tutorial - -- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md) -- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md) -- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md) -- [Parameter configuration](docs/advanced_tutorials/config_doc): - - [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md) - - [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md) -- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb) -- [Model compression](slim) - - [Model compression benchmark](slim) - - [Quantization](slim/quantization) - - [Model pruning](slim/prune) - - [Model distillation](slim/distillation) - - [Neural Architecture Search](slim/nas) -- [Deployment](deploy) - - [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md) - - [Python inference](deploy/python) - - [C++ inference](deploy/cpp) - - [Inference benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md) - -## Model Zoo - -- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md). -- [Mobile models](configs/mobile/README.md) -- [Anchor free models](configs/anchor_free/README.md) -- [Face detection models](docs/featured_model/FACE_DETECTION_en.md) -- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md) -- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md) -- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well -- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100 -- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md) -- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md) -- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%. -- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets. - - -## License -PaddleDetection is released under the [Apache 2.0 license](LICENSE). - -## Updates -v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc. -Please refer to [版本更新文档](docs/CHANGELOG.md) for details. - -## Contributing - -Contributions are highly welcomed and we would really appreciate your feedback!! diff --git a/README.md b/README.md new file mode 120000 index 000000000..4015683cf --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +README_cn.md \ No newline at end of file diff --git a/README_cn.md b/README_cn.md index deb4aa30d..8b3ee850e 100644 --- a/README_cn.md +++ b/README_cn.md @@ -1,4 +1,4 @@ -简体中文 | [English](README.md) +简体中文 | [English](README_en.md) 文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io) diff --git a/README_en.md b/README_en.md new file mode 100644 index 000000000..184962734 --- /dev/null +++ b/README_en.md @@ -0,0 +1,176 @@ +English | [简体中文](README_cn.md) + +Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io) + +# PaddleDetection + +PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which +aims to help developers in the whole development of training models, optimizing performance and +inference speed, and deploying models. PaddleDetection provides varied object detection architectures +in modular design, and wealthy data augmentation methods, network components, loss functions, etc. +PaddleDetection supported practical projects such as industrial quality inspection, remote sensing +image object detection, and automatic inspection with its practical features such as model compression +and multi-platform deployment. + +[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4, +has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single +Tesla V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details. + +**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.** + +
+ +
+ + +## Introduction + +Features: + +- Rich models: + + PaddleDetection provides rich of models, including 100+ pre-trained models +such as object detection, instance segmentation, face detection etc. It covers +the champion models, the practical detection models for cloud and edge device. + +- Production Ready: + + Key operations are implemented in C++ and CUDA, together with PaddlePaddle's +highly efficient inference engine, enables easy deployment in server environments. + +- Highly Flexible: + + Components are designed to be modular. Model architectures, as well as data +preprocess pipelines, can be easily customized with simple configuration +changes. + +- Performance Optimized: + + With the help of the underlying PaddlePaddle framework, faster training and +reduced GPU memory footprint is achieved. Notably, YOLOv3 training is +much faster compared to other frameworks. Another example is Mask-RCNN +(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during +multi-GPU training. + +Supported Architectures: + +| | ResNet | ResNet-vd [1](#vd) | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net | +| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----: | +| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | +| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | +| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | +| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | +| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | +| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | +| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | +| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | +| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | +| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | +| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | +| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | + +[1] [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost. + +**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally. + +More models: + +- EfficientDet +- FCOS +- CornerNet-Squeeze +- YOLOv4 +- PP-YOLO + +More Backbones: + +- DarkNet +- VGG +- GCNet +- CBNet + +Advanced Features: + +- [x] **Synchronized Batch Norm** +- [x] **Group Norm** +- [x] **Modulated Deformable Convolution** +- [x] **Deformable PSRoI Pooling** +- [x] **Non-local and GCNet** + +**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device. + +The following is the relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones. + +
+ +
+ +**NOTE:** +- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models +- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% +- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework +- All these models can be get in [Model Zoo](#Model-Zoo) + +The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details. + +
+ +
+ +## Tutorials + + +### Get Started + +- [Installation guide](docs/tutorials/INSTALL.md) +- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md) +- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md) +- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md) +- [FAQ](docs/FAQ.md) + +### Advanced Tutorial + +- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md) +- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md) +- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md) +- [Parameter configuration](docs/advanced_tutorials/config_doc): + - [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md) + - [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md) +- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb) +- [Model compression](slim) + - [Model compression benchmark](slim) + - [Quantization](slim/quantization) + - [Model pruning](slim/prune) + - [Model distillation](slim/distillation) + - [Neural Architecture Search](slim/nas) +- [Deployment](deploy) + - [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md) + - [Python inference](deploy/python) + - [C++ inference](deploy/cpp) + - [Inference benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md) + +## Model Zoo + +- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md). +- [Mobile models](configs/mobile/README.md) +- [Anchor free models](configs/anchor_free/README.md) +- [Face detection models](docs/featured_model/FACE_DETECTION_en.md) +- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md) +- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md) +- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well +- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100 +- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md) +- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md) +- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%. +- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets. + + +## License +PaddleDetection is released under the [Apache 2.0 license](LICENSE). + +## Updates +v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc. +Please refer to [版本更新文档](docs/CHANGELOG.md) for details. + +## Contributing + +Contributions are highly welcomed and we would really appreciate your feedback!! -- GitLab