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A simplified implemention of Faster R-CNN that replicate performance from origin paper
| Date | Stars |
|---|---|
| 2026-07-24 | 4031 |
| 2026-07-25 | 4031 |
| 2026-07-28 | 4031 |
| 2026-07-30 | 4031 |
| 2026-08-06 | 4031 |
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# A Simple and Fast Implementation of Faster R-CNN ## 1. Introduction **[Update:]** I've further simplified the code to pytorch 1.5, torchvision 0.6, and replace the customized ops roipool and nms with the one from torchvision. if you want the old version code, please checkout branch [v1.0](https://github.com/chenyuntc/simple-faster-rcnn-pytorch/tree/v1.0) This project is a **Simplified** Faster R-CNN implementation based on [chainercv](https://github.com/chainer/chainercv) and other [projects](#acknowledgement) . I hope it can serve as an start code for those who want to know the detail of Faster R-CNN. It aims to: - Simplify the code (*Simple is better than complex*) - Make the code more straightforward (*Flat is better than nested*) - Match the performance reported in [origin paper](https://arxiv.org/abs/1506.01497) (*Speed Counts and mAP Matters*) And it has the following features: - It can be run as pure Python code, no more build affair. - It's a minimal implemention in around 2000 lines valid code with a lot of comment and instruction.(thanks to chainercv's excellent documentation) - It achieves higher mAP than the origin implementation (0.712 VS 0.699) - It achieve speed compariable with other implementation (6fps and 14fps for train and test in TITAN XP) - It's memory-efficient (about 3GB for vgg16)  ## 2. Performance ### 2.1 mAP VGG16 train on `trainval` and test on `test` split. **Note**: the training shows great randomness, you may need a bit of luck and more epoches of training to reach the highest mAP. However, it should be easy to surpass the lower bound. | Implementation | mAP | | :--------------------------------------: | :---------: | | [origin paper](https://arxiv.org/abs/1506.01497) | 0.699 | | train with caffe pretrained model | 0.700-0.712 | | train with torchvision pretrained model | 0.685-0.701 | | model converted from [chainercv](https://github.com/chainer/chainercv/tree/master/examples/faster_rcnn) (reported 0.706) | 0.7053 | ### 2.2 Speed | Implementation | GPU | Inference | Trainining | | :--------------------------------------: | :------: | :-------: | :--------: | | [origin paper](https://arxiv.org/abs/1506.01497) | K40 | 5 fps | NA | | This[1] | TITAN Xp | 14-15 fps | 6 fps | | [pytorch-faster-rcnn](https://github.com/ruotianluo/pytorch-faster-rcnn) | TITAN Xp | 15-17fps | 6fps | [1]: make sure you install cupy correctly and only one program run on the GPU. The training speed is sensitive to your gpu status. see [troubleshooting](troubleshooting) for more info. Morever it's slow in the start of the program -- it need time to warm up. It could be faster by removing visualization, logging, averaging loss etc. ## 3. Install dependencies Here is an example of create environ **from scratch** with `anaconda` ```sh # create conda env conda create --name simp python=3.7 conda activate simp # install pytorch conda install pytorch torchvision cudatoolkit=10.2 -c pytorch # install other dependancy pip install visdom scikit-image tqdm fire ipdb pprint matplotlib torchnet # start visdom nohup python -m visdom.server & ``` If you don't use anaconda, then: - install PyTorch with GPU (code are GPU-only), refer to [official website](http://pytorch.org) - install other dependencies: `pip install visdom scikit-image tqdm fire ipdb pprint matplotlib torchnet` - start visdom for visualization ```Bash nohup python -m visdom.server & ``` ## 4. Demo Download pretrained model from [Google Drive](https://drive.google.com/open?id=1cQ27LIn-Rig4-Uayzy_gH5-cW-NRGVzY) or [Baidu Netdisk( passwd: scxn)](https://pan.baidu.com/s/1o87RuXW) See [demo.ipynb](https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/master/demo.ipynb) for more detail. ## 5. Train ### 5.1 Prepare data #### Pascal VOC2007 1. Downloa
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Yun Chen · Canada
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Moon · SWUST
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8fcc50ef4064c8f5, topic:pytorch
matched fp:8fcc50ef4064c8f5, topic:object-detection