/VoVNet-Detectron

Detectron with VoVNet(CVPRW'19) backbone networks

Primary LanguagePythonMIT LicenseMIT

Detectron with VoVNet(CVPRW'19) Backbone Networks

This repository contains Detectron with VoVNet (CVPRW'19) Backbone Networks. This code based on pytorch imeplementation of Detectron (maskrcnn-benchmark)

Highlights

  • Memory efficient
  • Better performance, especially for small objects
  • Faster speed

Comparison with ResNet backbones

  • 2x schedule
  • same hyperparameters
  • same training protocols ( max epoch, learning rate schedule, etc)
  • NOT multi-scale training augmentation
  • 8 x TITAN Xp GPU
  • pytorch1.1
  • CUDA v9
  • cuDNN v7.2

Note

'*' indicates ResNeXt models from Detectron official caffe2 page.

Backbone Detector Train mem(GB) Inference time (ms) Box AP (AP/APs/APm/APl) Mask AP (AP/APs/APm/APl) DOWNLOAD
R-50 Faster 3.6 78 37.5/21.3/40.3/49.5 - link
V-39 Faster 3.9 78 39.8/23.7/42.6/51.5 - link
R-101 Faster 4.7 97 39.6/22.8/43.2/51.9 - link
V-57 Faster 4.4 87 40.8/24.8/43.8/52.4 - link
V-75 Faster 5.3 96 41.2/24.1/44.3/53.0 - link
X-101-64x4d* Faster - 41.3/-/-/- - -
X-101-32x8d* Faster - 40.6/-/-/- - -
V-93 Faster 6.1 110 41.8/24.8/45.1/53.8 - link
R-50 Mask 3.6 83 38.6/22.1/41.3/51.4 34.9/16.0/37.3/52.2 link
V-39 Mask 4 81 41.0/24.6/43.9/53.1 36.7/17.9/39.3/53.0 link
R-101 Mask 4.7 102 40.8/23.2/44.0/53.9 36.7/16.7/39.4/54.3 link
V-57 Mask 4.5 90 42.0/25.1/44.9/53.8 37.5/18.3/39.8/54.3 link
V-75 Mask 5.4 106 42.4/26.0/45.5/54.8 37.7/18.9/40.4/54.9 link
X-101-64x4d* Faster - 42.2/-/-/- 37.2/-/-/- -
X-101-32x8d* Faster - 41.7/-/-/- 36.9/-/-/- -
V-93 Mask 6.7 114 42.7/24.9/45.8/55.3 38.0/17.7/40.9/55.2 link

ImageNet pretrained weight

Installation

Check INSTALL.md for installation instructions which is orginate from maskrcnn-benchmark

Training

Follow the instructions maskrcnn-benchmark guides

For example,

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "configs/vovnet/e2e_faster_rcnn_V_39_FPN_2x.yaml"

Evaluation

Follow the instruction

First of all, you have to download the weight file you want to inference.

For examaple,

multi-gpu evaluation & test batch size 16,
wget https://dl.dropbox.com/s/f1per2rj4pi8t71/FRCN-V-39-FPN-2x-norm.pth
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file "configs/vovnet/e2e_faster_rcnn_V_39_FPN_2x.yaml"   TEST.IMS_PER_BATCH 16 MODEL.WEIGHT FRCN-V-39-FPN-2x-norm.pth
For single-gpu evaluation & test batch size 1,
wget https://dl.dropbox.com/s/f1per2rj4pi8t71/FRCN-V-39-FPN-2x-norm.pth
CUDA_VISIBLE_DEVICES=0
python tools/test_net.py --config-file "configs/vovnet/e2e_faster_rcnn_V_39_FPN_2x.yaml" TEST.IMS_PER_BATCH 1 MODEL.WEIGHT FRCN-V-39-FPN-2x-norm.pth

TO DO LIST

  • super slim models
  • slim models
  • Larger models
  • Multi-scale training & test