# simple-faster-rcnn-pytorch **Repository Path**: oldshan/simple-faster-rcnn-pytorch ## Basic Information - **Project Name**: simple-faster-rcnn-pytorch - **Description**: A simplified implemention of Faster R-CNN that replicate performance from origin paper - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-06-23 - **Last Updated**: 2024-06-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # A Simple and Fast Implementation of Faster R-CNN ## 1. Introduction This project is a **Simplified** Faster R-CNN implementation based on [chainercv](https://github.com/chainer/chainercv) and other [projects](#acknowledgement) . 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. (cuda code moves to cupy, Cython acceleration are optional) - 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 with cython) - It's memory-efficient (about 3GB for vgg16) ![img](http://7zh43r.com1.z0.glb.clouddn.com/del/faster-speed.jpg) ## 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 could be faster by removing visualization, logging, averaging loss etc. ## 3. Install dependencies requires python3 and PyTorch 0.3 - install PyTorch >=0.3 with GPU (code are GPU-only), refer to [official website](http://pytorch.org) - install cupy, you can install via `pip install` but it's better to read the [docs](https://docs-cupy.chainer.org/en/latest/install.html#install-cupy-with-cudnn-and-nccl) and make sure the environ is correctly set - install other dependencies: `pip install -r requirements.txt ` - Optional, but strongly recommended: build cython code `nms_gpu_post`: ```Bash cd model/utils/nms/ python3 build.py build_ext --inplace ``` - start vidom for visualization ```Bash nohup python3 -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. Download the training, validation, test data and VOCdevkit ```Bash wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar ``` 2. Extract all of these tars into one directory named `VOCdevkit` ```Bash tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar ``` 3. It should have this basic structure ```Bash $VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ... ``` 4. modify `voc_data_dir` cfg item in `utils/config.py`, or pass it to program using argument like `--voc-data-dir=/path/to/VOCdevkit/VOC2007/` . #### COCO TBD ### 5.2 Prepare caffe-pretrained vgg16 If you want to use caffe-pretrain model as initial weight, you can run below to get vgg16 weights converted from caffe, which is the same as the origin paper use. ````Bash python misc/convert_caffe_pretrain.py ```` This scripts would download pretrained model and converted it to the format compatible with torchvision. Then you should specify where caffe-pretraind model `vgg16_caffe.pth` stored in `utils/config.py` by setting `caffe_pretrain_path` If you want to use pretrained model from torchvision, you may skip this step. **NOTE**, caffe pretrained model has shown slight better performance. **NOTE**: caffe model require images in BGR 0-255, while torchvision model requires images in RGB and 0-1. See `data/dataset.py`for more detail. ### 5.3 begin training ```Bash mkdir checkpoints/ # folder for snapshots ``` ```bash python3 train.py train --env='fasterrcnn-caffe' --plot-every=100 --caffe-pretrain ``` you may refer to `utils/config.py` for more argument. Some Key arguments: - `--caffe-pretrain=False`: use pretrain model from caffe or torchvision (Default: torchvison) - `--plot-every=n`: visualize prediction, loss etc every `n` batches. - `--env`: visdom env for visualization - `--voc_data_dir`: where the VOC data stored - `--use-drop`: use dropout in RoI head, default False - `--use-Adam`: use Adam instead of SGD, default SGD. (You need set a very low `lr` for Adam) - `--load-path`: pretrained model path, default `None`, if it's specified, it would be loaded. you may open browser, visit `http://:8097` and see the visualization of training procedure as below: ![visdom](http://7zh43r.com2.z0.glb.clouddn.com/del/visdom-fasterrcnn.png) If you're in China and encounter problem with visdom (i.e. timeout, blank screen), you may refer to [visdom issue](https://github.com/facebookresearch/visdom/issues/111#issuecomment-321743890), and see [troubleshooting](#troubleshooting) for solution. ## Troubleshooting - visdom Some js files in visdom was blocked in China, see simple solution [here](https://github.com/chenyuntc/PyTorch-book/blob/master/README.md#visdom打不开及其解决方案) Also, `updata=append` doesn't work due to a bug brought in latest version, see [issue](https://github.com/facebookresearch/visdom/issues/233) and [fix](https://github.com/facebookresearch/visdom/pull/234/files) You don't need to build from source, modifying related files would be OK. - dataloader: `received 0 items of ancdata` see [discussion](https://github.com/pytorch/pytorch/issues/973#issuecomment-346405667), It's alreadly fixed in [train.py](https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/master/train.py#L17-L22). So I think you are free from this problem. - cupy `numpy.core._internal.AxisError: axis 1 out of bounds [0, 1)` bug of cupy, see [issue](https://github.com/cupy/cupy/issues/793), fix via [pull request](https://github.com/cupy/cupy/pull/749) You don't need to build from source, modifying related files would be OK. - VGG: Slow in construction VGG16 is slow in construction(i.e. 9 seconds),it could be speed up by this [PR](https://github.com/pytorch/vision/pull/377) You don't need to build from source, modifying related files would be OK. - About the speed One strange thing is that, even the code doesn't use chainer, but if I remove `from chainer import cuda`, the speed drops a lot (train 6.5->6.1,test 14.5->10), because Chainer replaces the default allocator of CuPy by its memory pool implementation. But ever since V4.0, cupy use memory pool as default. However you need to build from souce if you are gona use the latest version of cupy (uninstall cupy -> git clone -> git checkout v4.0 -> setup.py install) @_@ Another simple fix: add `from chainer import cuda` at the begining of `train.py`. in such case,you'll need to `pip install chainer` first. ## More - [ ] training on coco - [ ] resnet - [ ] Maybe；replace cupy with THTensor+cffi? - [ ] Maybe：Convert all numpy code to tensor? - [ ] check python2-compatibility ## Acknowledgement This work builds on many excellent works, which include: - [Yusuke Niitani's ChainerCV](https://github.com/chainer/chainercv) (mainly) - [Ruotian Luo's pytorch-faster-rcnn](https://github.com/ruotianluo/pytorch-faster-rcnn) which based on [Xinlei Chen's tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn) - [faster-rcnn.pytorch by Jianwei Yang and Jiasen Lu](https://github.com/jwyang/faster-rcnn.pytorch).It mainly refer to [longcw's faster_rcnn_pytorch](https://github.com/longcw/faster_rcnn_pytorch) - All the above Repositories have referred to [py-faster-rcnn by Ross Girshick and Sean Bell](https://github.com/rbgirshick/py-faster-rcnn) either directly or indirectly. ## ^_^ Licensed under MIT, see the LICENSE for more detail. Contribution Welcome. If you encounter any problem, feel free to open an issue. Correct me if anything is wrong or unclear. model structure ![img](https://raw.githubusercontent.com/chenyuntc/cloud/master/faster-rcnn的副本的副本.png)