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We aimed to make it easy to use in research and products.\n+\n+## Introduction\n+\n+Major features:\n+\n+- Easy to Depoly:\n+  All the operations related with inference are implemented by C++ and CUDA, it makes the detection model easy to depoly in products on server without Python based on the high efficient inference engine of PaddlePaddle.\n+  We release detection models based on ResNet-vd backbone, for example, the accuracy of Faster RCNN model with FPN based on ResNet50 VD is close to model based on ResNet 101. But the former is more smaller and faster.\n+\n+- Easy to Customize:\n+   All components are modular encapsulated, including the data transforms. It's easy to plug in and pull out any module. For example, uers can switch backone easily or add data mixed-up data transform for models.\n+\n+- High Efficiency:\n+  Based on high efficient PaddlePaddle framework, less memory is required. For example, the batch size of Mask-RCNN based on ResNet50 can be 5 per Tesla V100 (16G). The training speed of Yolo v3 is faster than other framework.  \n+\n+\n+\n+The supported architectures are as follows:\n+\n+\n+|                    | ResNet |ResNet vd| ResNeXt  | SENet    | MobileNet | DarkNet|\n+|--------------------|:------:|--------:|:--------:|:--------:|:---------:|:------:|\n+| Faster R-CNN       | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| Faster R-CNN + FPN | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| Mask R-CNN         | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| Mask R-CNN + FPN   | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| Cascade R-CNN      | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| RetinaNet          | ✓      | ✓      | ✓        |  ✓       | ✗        | ✗      |\n+| Yolov3             | ✓      | ✗      | ✗         |  ✗       | ✓        | ✓     |\n+| SSD                | ✗      | ✗      | ✗         |  ✗       | ✓        | ✗      |\n+\n+The extensive capabilities:\n+\n+- [x] **Synchronized batch norm**:  used in Yolo v3.\n+- [x] **Group Norm**: supported this operation, the related model will be added later.\n+- [x] **Dodulated deformable convolution**: suppored this operation, the related model will be added later.\n+- [x] **Deformable PSRoI Pooling**: suppored this operation, the related model will be added later.\n+\n+\n+#### Work in Progress and to Do\n+\n+- About Framework:\n+   - Mixed precision training and distributed training.\n+   - 8-bit deployment.\n+   - Easy to customize user-defined function.\n+\n+- About Algorithms:\n+   - More SOTA models.\n+   - More easy-to-deployed models.\n+\n+\n+We are also pleased to receive your feedback.\n+\n+## Model zoo\n+\n+The trained models can be available in PaddlePaddle [detection model zoo](docs/MODEL_ZOO.md).\n+\n+## Installation\n+\n+Please follow the [installation instructions](docs/INSTALL.md) to install PaddlePaddle and prepare environment.\n+\n+## Get Started\n+\n+For quickly start, infer a image:\n+\n+```bash\n+export PYTHONPATH=`pwd`:$PYTHONPATH\n+python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \\\n+    -o weights=http://\n+    -\n+```\n+\n+The predicted result is visualized in `output/xxx.jpg`.\n+\n+For more detailed training and evaluating pipeline, please refer [GETTING_STARTED.md](docs/GETTING_STARTED.md).\n+\n+\n+For more documentation, please refer:\n+\n+- [How to config an object detection pipeline.](docs/CONFIG.md)\n+- [How to use customized dataset and add data preprocessing.](docs/DATA.md)\n+\n+\n+## Deploy\n+\n+The example how to use PaddlePaddle to deploy detection model will be added later.\n+\n+\n+## Framework\n+\n+The code tree is as follows.","path":"PaddleCV/object_detection/README.md","position":97,"original_position":97,"commit_id":"64e4ef96d41499bc5a52c509e37c41d7966233e8","original_commit_id":"64e4ef96d41499bc5a52c509e37c41d7966233e8","user":{"login":"willthefrog","id":2098301,"node_id":"MDQ6VXNlcjIwOTgzMDE=","avatar_url":"https://avatars1.githubusercontent.com/u/2098301?v=4","gravatar_id":"","url":"https://api.github.com/users/willthefrog","html_url":"https://github.com/willthefrog","followers_url":"https://api.github.com/users/willthefrog/followers","following_url":"https://api.github.com/users/willthefrog/following{/other_user}","gists_url":"https://api.github.com/users/willthefrog/gists{/gist_id}","starred_url":"https://api.github.com/users/willthefrog/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/willthefrog/subscriptions","organizations_url":"https://api.github.com/users/willthefrog/orgs","repos_url":"https://api.github.com/users/willthefrog/repos","events_url":"https://api.github.com/users/willthefrog/events{/privacy}","received_events_url":"https://api.github.com/users/willthefrog/received_events","type":"User","site_admin":false},"body":"IMHO, 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change","created_at":"2019-06-26T01:30:30Z","updated_at":"2019-06-26T01:30:30Z","html_url":"https://github.com/PaddlePaddle/models/pull/2549#discussion_r297453841","pull_request_url":"https://api.github.com/repos/PaddlePaddle/models/pulls/2549","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/PaddlePaddle/models/pulls/comments/297453841"},"html":{"href":"https://github.com/PaddlePaddle/models/pull/2549#discussion_r297453841"},"pull_request":{"href":"https://api.github.com/repos/PaddlePaddle/models/pulls/2549"}}},"pull_request":{"url":"https://api.github.com/repos/PaddlePaddle/models/pulls/2549","id":291492584,"node_id":"MDExOlB1bGxSZXF1ZXN0MjkxNDkyNTg0","html_url":"https://github.com/PaddlePaddle/models/pull/2549","diff_url":"https://github.com/PaddlePaddle/models/pull/2549.diff","patch_url":"https://github.com/PaddlePaddle/models/pull/2549.patch","issue_url":"https://api.github.com/repos/PaddlePaddle/models/issues/2549","number":2549,"state":"open","locked":false,"title":"Add 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