fast-rcnn.pytorch：更快的r-cnn的更快的pytorch实现

# A *Faster* Pytorch Implementation of Faster R-CNN ## Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the first open-sourced object detection code which supports multi-gpu training. It has been integrating tremendous efforts from many people. However, we have seen many high-quality repos emerged in the last years, such as: * [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) * [detectron2](https://github.com/facebookresearch/detectron2) * [mmdetection](https://github.com/open-mmlab/mmdetection) **At this point, I think this repo is out-of-data in terms of the pipeline and coding style, and will not maintain actively. Though you can still use this repo as a playground, I highly recommend you move to the above repos to delve into west world of object detection!** ## Introduction ### :boom: Good news! This repo supports pytorch-1.0 now!!! We borrowed some code and techniques from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). Just go to pytorch-1.0 branch! This project is a *faster* pytorch implementation of faster R-CNN, aimed to accelerating the training of faster R-CNN object detection models. Recently, there are a number of good implementations: * [rbgirshick/py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn), developed based on Pycaffe + Numpy * [longcw/faster_rcnn_pytorch](https://github.com/longcw/faster_rcnn_pytorch), developed based on Pytorch + Numpy * [endernewton/tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn), developed based on TensorFlow + Numpy * [ruotianluo/pytorch-faster-rcnn](https://github.com/ruotianluo/pytorch-faster-rcnn), developed based on Pytorch + TensorFlow + Numpy During our implementing, we referred the above implementations, especailly [longcw/faster_rcnn_pytorch](https://github.com/longcw/faster_rcnn_pytorch). However, our implementation has several unique and new features compared with the above implementations: * **It is pure Pytorch code**. We convert all the numpy implementations to pytorch! * **It supports multi-image batch training**. We revise all the layers, including dataloader, rpn, roi-pooling, etc., to support multiple images in each minibatch. * **It supports multiple GPUs training**. We use a multiple GPU wrapper (nn.DataParallel here) to make it flexible to use one or more GPUs, as a merit of the above two features. * **It supports three pooling methods**. We integrate three pooling methods: roi pooing, roi align and roi crop. More importantly, we modify all of them to support multi-image batch training. * **It is memory efficient**. We limit the image aspect ratio, and group images with similar aspect ratios into a minibatch. As such, we can train resnet101 and VGG16 with batchsize = 4 (4 images) on a single Titan X (12 GB). When training with 8 GPU, the maximum batchsize for each GPU is 3 (Res101), totaling 24. * **It is faster**. Based on the above modifications, the training is much faster. We report the training speed on NVIDIA TITAN Xp in the tables below. ### What we are doing and going to do - [x] Support both python2 and python3 (great thanks to [cclauss](https://github.com/cclauss)). - [x] Add deformable pooling layer (mainly supported by [Xander](https://github.com/xanderchf)). - [x] Support pytorch-0.4.0 (this branch). - [x] Support tensorboardX. - [x] Support pytorch-1.0 (go to pytorch-1.0 branch). ## Other Implementations * [Feature Pyramid Network (FPN)](https://github.com/jwyang/fpn.pytorch) * [Mask R-CNN](https://github.com/roytseng-tw/mask-rcnn.pytorch) (~~ongoing~~ already implemented by [roytseng-tw](https://github.com/roytseng-tw)) * [Graph R-CNN](https://github.com/jwyang/graph-rcnn.pytorch) (extension to scene graph generation) ## Tutorial * [Blog](http://www.telesens.co/2018/03/11/object-detection-and-classification-using-r-cnns/) by [ankur6ue](https://github.com/ankur6ue) ## Benchmarking We benchmark our code thoroughly on three datasets: pascal voc, coco and visual genome, using two different network architectures: vgg16 and resnet101. Below are the results: 1). PASCAL VOC 2007 (Train/Test: 07trainval/07test, scale=600, ROI Align) model   | #GPUs | batch size | lr       | lr_decay | max_epoch     | time/epoch | mem/GPU | mAP ---------|--------|-----|--------|-----|-----|-------|--------|----- [VGG-16](https://www.dropbox.com/s/6ief4w7qzka6083/faster_rcnn_1_6_10021.pth?dl=0)     | 1 | 1 | 1e-3 | 5   | 6   | 0.76 hr | 3265MB   | 70.1 [VGG-16](https://www.dropbox.com/s/cpj2nu35am0f9hp/faster_rcnn_1_9_2504.pth?dl=0)     | 1 | 4 | 4e-3 | 8  | 9  | 0.50 hr | 9083MB   | 69.6 [VGG-16](https://www.dropbox.com/s/1a31y7vicby0kvy/faster_rcnn_1_10_625.pth?dl=0)     | 8 | 16| 1e-2 | 8   | 10 | 0.19 hr | 5291MB | 69.4 [VGG-16](https://www.dropbox.com/s/hkj7i6mbhw9tq4k/faster_rcnn_1_11_416.pth?dl=0)     | 8 | 24| 1e-2 | 10 | 11 | 0.16 hr | 11303MB | 69.2 [Res-101](https://www.dropbox.com/s/4v3or0054kzl19q/faster_rcnn_1_7_10021.pth?dl=0) | 1 | 1 | 1e-3 | 5 | 7 | 0.88 hr | 3200 MB | 75.2 [Res-101](https://www.dropbox.com/s/8bhldrds3mf0yuj/faster_rcnn_1_10_2504.pth?dl=0)   | 1 | 4 | 4e-3 | 8   | 10 | 0.60 hr | 9700 MB | 74.9 [Res-101](https://www.dropbox.com/s/5is50y01m1l9hbu/faster_rcnn_1_10_625.pth?dl=0)   | 8 | 16| 1e-2 | 8   | 10 | 0.23 hr | 8400 MB | 75.2  [Res-101](https://www.dropbox.com/s/cn8gneumg4gjo9i/faster_rcnn_1_12_416.pth?dl=0)   | 8 | 24| 1e-2 | 10 | 12 | 0.17 hr | 10327MB | 75.1   2). COCO (Train/Test: coco_train+coco_val-minival/minival, scale=800, max_size=1200, ROI Align) model | #GPUs | batch size |lr | lr_decay | max_epoch | time/epoch | mem/GPU | mAP ---------|--------|-----|--------|-----|-----|-------|--------|----- VGG-16     | 8 | 16   |1e-2| 4 | 6 | 4.9 hr | 7192 MB | 29.2 [Res-101](https://www.dropbox.com/s/5if6l7mqsi4rfk9/faster_rcnn_1_6_14657.pth?dl=0)   | 8 | 16   |1e-2| 4   | 6 | 6.0 hr |10956 MB | 36.2 [Res-101](https://www.dropbox.com/s/be0isevd22eikqb/faster_rcnn_1_10_14657.pth?dl=0)   | 8 | 16   |1e-2| 4   | 10 | 6.0 hr |10956 MB | 37.0 **NOTE**. Since the above models use scale=800, you need add "--ls" at the end of test command. 3). COCO (Train/Test: coco_train+coco_val-minival/minival, scale=600, max_size=1000, ROI Align) model | #GPUs | batch size |lr | lr_decay | max_epoch | time/epoch | mem/GPU | mAP ---------|--------|-----|--------|-----|-----|-------|--------|----- [Res-101](https://www.dropbox.com/s/y171ze1sdw1o2ph/faster_rcnn_1_6_9771.pth?dl=0)   | 8 | 24   |1e-2| 4   | 6 | 5.4 hr   |10659 MB | 33.9 [Res-101](https://www.dropbox.com/s/dpq6qv0efspelr3/faster_rcnn_1_10_9771.pth?dl=0)   | 8 | 24   |1e-2| 4   | 10 | 5.4 hr   |10659 MB | 34.5 4). Visual Genome (Train/Test: vg_train/vg_test, scale=600, max_size=1000, ROI Align, category=2500) model | #GPUs | batch size |lr | lr_decay | max_epoch | time/epoch | mem/GPU | mAP ---------|--------|-----|--------|-----|-----|-------|--------|----- [VGG-16](http://data.lip6.fr/cadene/faster-rcnn.pytorch/faster_rcnn_1_19_48611.pth)   | 1 P100 | 4   |1e-3| 5   | 20 | 3.7 hr   |12707 MB | 4.4 Thanks to [Remi](https://github.com/Cadene) for providing the pretrained detection model on visual genome! * Click the links in the above tables to download our pre-trained faster r-cnn models. * If not mentioned, the GPU we used is NVIDIA Titan X Pascal (12GB). ## Preparation First of all, clone the code ``` git clone https://github.com/jwyang/faster-rcnn.pytorch.git ``` Then, create a folder: ``` cd faster-rcnn.pytorch && mkdir data ``` ### prerequisites * Python 2.7 or 3.6 * Pytorch 0.4.0 (**now it does not support 0.4.1 or higher**) * CUDA 8.0 or higher ### Data Preparation * **PASCAL_VOC 07+12**: Please follow the instructions in [py-faster-rcnn](https://github.com/rbgirshick/py-f