Faster Rcnn.pytorch Save

A faster pytorch implementation of faster r-cnn

Project README

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:

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!


:boom: Good news! This repo supports pytorch-1.0 now!!! We borrowed some code and techniques from 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:

During our implementing, we referred the above implementations, especailly 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

  • Support both python2 and python3 (great thanks to cclauss).
  • Add deformable pooling layer (mainly supported by Xander).
  • Support pytorch-0.4.0 (this branch).
  • Support tensorboardX.
  • Support pytorch-1.0 (go to pytorch-1.0 branch).

Other Implementations



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     1 1 1e-3 5   6   0.76 hr 3265MB   70.1
VGG-16     1 4 4e-3 9   0.50 hr 9083MB   69.6
VGG-16     8 16 1e-2  8   10 0.19 hr 5291MB 69.4
VGG-16     8 24 1e-2 10 11 0.16 hr 11303MB 69.2
Res-101 1 1 1e-3 5 7 0.88 hr 3200 MB 75.2
Res-101   1 4 4e-3 8   10 0.60 hr 9700 MB 74.9
Res-101   8 16 1e-2 8   10 0.23 hr 8400 MB 75.2 
Res-101   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   8 16   1e-2 4   6 6.0 hr 10956 MB 36.2
Res-101   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   8 24   1e-2 4   6 5.4 hr   10659 MB 33.9
Res-101   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   1 P100 4   1e-3 5   20 3.7 hr   12707 MB 4.4

Thanks to Remi 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).


First of all, clone the code

git clone

Then, create a folder:

cd faster-rcnn.pytorch && mkdir data


  • 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 to prepare VOC datasets. Actually, you can refer to any others. After downloading the data, create softlinks in the folder data/.

  • COCO: Please also follow the instructions in py-faster-rcnn to prepare the data.

  • Visual Genome: Please follow the instructions in bottom-up-attention to prepare Visual Genome dataset. You need to download the images and object annotation files first, and then perform proprecessing to obtain the vocabulary and cleansed annotations based on the scripts provided in this repository.

Pretrained Model

We used two pretrained models in our experiments, VGG and ResNet101. You can download these two models from:

Download them and put them into the data/pretrained_model/.

NOTE. We compare the pretrained models from Pytorch and Caffe, and surprisingly find Caffe pretrained models have slightly better performance than Pytorch pretrained. We would suggest to use Caffe pretrained models from the above link to reproduce our results.

If you want to use pytorch pre-trained models, please remember to transpose images from BGR to RGB, and also use the same data transformer (minus mean and normalize) as used in pretrained model.


As pointed out by ruotianluo/pytorch-faster-rcnn, choose the right -arch in file, to compile the cuda code:

GPU model Architecture
TitanX (Maxwell/Pascal) sm_52
GTX 960M sm_50
GTX 1080 (Ti) sm_61
Grid K520 (AWS g2.2xlarge) sm_30
Tesla K80 (AWS p2.xlarge) sm_37

More details about setting the architecture can be found here or here

Install all the python dependencies using pip:

pip install -r requirements.txt

Compile the cuda dependencies using following simple commands:

cd lib

It will compile all the modules you need, including NMS, ROI_Pooing, ROI_Align and ROI_Crop. The default version is compiled with Python 2.7, please compile by yourself if you are using a different python version.

As pointed out in this issue, if you encounter some error during the compilation, you might miss to export the CUDA paths to your environment.


Before training, set the right directory to save and load the trained models. Change the arguments "save_dir" and "load_dir" in and to adapt to your environment.

To train a faster R-CNN model with vgg16 on pascal_voc, simply run:

                   --dataset pascal_voc --net vgg16 \
                   --bs $BATCH_SIZE --nw $WORKER_NUMBER \
                   --lr $LEARNING_RATE --lr_decay_step $DECAY_STEP \

where 'bs' is the batch size with default 1. Alternatively, to train with resnet101 on pascal_voc, simple run:

                    --dataset pascal_voc --net res101 \
                    --bs $BATCH_SIZE --nw $WORKER_NUMBER \
                    --lr $LEARNING_RATE --lr_decay_step $DECAY_STEP \

Above, BATCH_SIZE and WORKER_NUMBER can be set adaptively according to your GPU memory size. On Titan Xp with 12G memory, it can be up to 4.

If you have multiple (say 8) Titan Xp GPUs, then just use them all! Try:

python --dataset pascal_voc --net vgg16 \
                       --bs 24 --nw 8 \
                       --lr $LEARNING_RATE --lr_decay_step $DECAY_STEP \
                       --cuda --mGPUs

Change dataset to "coco" or 'vg' if you want to train on COCO or Visual Genome.


If you want to evaluate the detection performance of a pre-trained vgg16 model on pascal_voc test set, simply run

python --dataset pascal_voc --net vgg16 \
                   --checksession $SESSION --checkepoch $EPOCH --checkpoint $CHECKPOINT \

Specify the specific model session, checkepoch and checkpoint, e.g., SESSION=1, EPOCH=6, CHECKPOINT=416.


If you want to run detection on your own images with a pre-trained model, download the pretrained model listed in above tables or train your own models at first, then add images to folder $ROOT/images, and then run

python --net vgg16 \
               --checksession $SESSION --checkepoch $EPOCH --checkpoint $CHECKPOINT \
               --cuda --load_dir path/to/model/directoy

Then you will find the detection results in folder $ROOT/images.

Note the default merely support pascal_voc categories. You need to change the line to adapt your own model.

Below are some detection results:

Webcam Demo

You can use a webcam in a real-time demo by running

python --net vgg16 \
               --checksession $SESSION --checkepoch $EPOCH --checkpoint $CHECKPOINT \
               --cuda --load_dir path/to/model/directoy \
               --webcam $WEBCAM_ID

The demo is stopped by clicking the image window and then pressing the 'q' key.


This project is equally contributed by Jianwei Yang and Jiasen Lu, and many others (thanks to them!).


    Author = {Jianwei Yang and Jiasen Lu and Dhruv Batra and Devi Parikh},
    Title = {A Faster Pytorch Implementation of Faster R-CNN},
    Journal = {},
    Year = {2017}

    Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
    Title = {Faster {R-CNN}: Towards Real-Time Object Detection
             with Region Proposal Networks},
    Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
    Year = {2015}
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