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A Simple and Fast Implementation of Faster R-CNN

Introduction

This project is a Simplified Faster R-CNN implementation based on chainercv and other projects . 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 (Speed Counts and mAP Matters)

Performance

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 0.699
train with caffe pretrained model 0.701-0.712
train with torchvision pretrained model 0.685-0.701
model converted from chainercv (reported 0.706) 0.7053

Speed

Implementation GPU Inference Trainining
origin paper K40 5 fps NA
This TITAN Xp 12 fps 1 5-6 fps2
pytorch-faster-rcnn TITAN Xp NA 5-6fps

NOTE you should make sure you install cupy correctly to reach the benchmark.

Install dependencies

requires python3 and PyTorch 0.3

  • install PyTorch >=0.3 with GPU (code are GPU-only), refer to official website

  • install cupy, you can install via pip install but it's better to read the docs and make sure the environ is correctly set

  • install other dependencies: pip install -r requirements.txt

  • build nms_gpu_post: cd model/utils/nms/; python3 build.py build_ext --inplace

  • start vidom for visualize

nohup python3 -m visdom.server &

If you're in China and have encounter problem with visdom (i.e. timeout, blank screen), you may refer to visdom issue, and a temporary solution provided by me

Demo

Download pretrained model from Google Drive.

See demo.ipynb for more detail.

Train

Prepare data

Pascal VOC2007

  1. Download the training, validation, test data and VOCdevkit

    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

    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

    $VOCdevkit/                           # development kit
$VOCdevkit/VOCcode/                   # VOC utility code
$VOCdevkit/VOC2007                    # image sets, annotations, etc.
# ... and several other directories ...

  4. specifiy the voc_data_dir in config.py, or pass it to program using argument like --voc-data-dir=/path/to/VOCdevkit/VOC2007/ .

COCO

TBD

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.

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 config.py by setting caffe_pretrain_path

If you want to use torchvision pretrained model, 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.pyfor more detail.

begin training

mkdir checkpoints/ # folder for snapshots
python3 train.py train --env='fasterrcnn-caffe' --plot-every=100 --caffe-pretrain

you may refer to config.py for more argument.

Some Key arguments:

  • --caffe-pretrain=True: use caffe pretrain model or use torchvision pretrained model (Default: torchvison)
  • --plot-every=n: visualize predict, 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 without dropout
  • --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, the pretrained model would be loaded.

you may open browser, type:http://<ip>:8097 and see the visualization of training procedure as below: visdom

Troubleshooting

TODO: make it clear

  • visdom
  • dataloader/ulimit
  • cupy
  • vgg

TODO

  • training on coco
  • resnet
  • replace cupy with THTensor+cffi?
  • Convert all numpy code to tensor?

Acknowledgement

This work builds on many excellent works, which include:

LICENSE

MIT, see the LICENSE for more detail.

Footnotes

  1. include reading images from disk, preprocessing, etc. see eval in train.py for more detail. ↩

  2. it depends on the e ↩