FCOS with VoVNet(CVPRW'19) Efficient Backbone Networks

This repository contains FCOS(ICCV'19) with VoVNet (CVPRW'19) efficient backbone networks. This code based on pytorch imeplementation of FCOS

Highlights

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

Comparison with ResNet backbones

• same hyperparameters
• same training protocols ( max epoch, learning rate schedule, etc)
• 8 x TITAN Xp GPU
• pytorch1.1
• CUDA v9
• cuDNN v7.2

Backbone	Multi-scale training	Inference time (ms)	Box AP (AP/APs/APm/APl)	DOWNLOAD
R-50-FPN-1x	No	84	37.5/21.3/40.3/49.5	-
V-39-FPN-1x	No	82	37.7/22.4/41.8/48.4	link
R-101-FPN-2x	Yes	104	41.3/25.0/45.5/53.0	-
V-57-FPN-2x	Yes	91	41.6/25.9/45.6/53.1	link
R-101-32x8d-FPN-2x	Yes	171	42.5/26.0/46.1/54.2	-
V-93-FPN-2x	Yes	113	42.1/26.2/46.0/53.9	link

ImageNet pretrained weight

• VoVNet-39
• VoVNet-57
• VoVNet-75
• VoVNet-93

Installation

Check INSTALL.md for installation instructions which is orginate from FCOS

Training

Follow the instructions

For example,

# specify the number of GPU you can use.
export NGPUS=8 
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "configs/vovnet/fcos_V_39_FPN_1x.yaml" 

Evaluation

Follow the instructions

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/8n0wyypfggliplw/FCOS-V-39-FPN-1x.pth?dl=1
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file "configs/vovnet/fcos_V_39_FPN_1x.yaml" TEST.IMS_PER_BATCH 16 MODEL.WEIGHT FCOS-V-39-FPN-1x.pth

single-gpu evaluation & test batch size 1,

wget https://dl.dropbox.com/s/8n0wyypfggliplw/FCOS-V-39-FPN-1x.pth?dl=1
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 FCOS-V-39-FPN-1x.pth

Related projects

• VoVNet-Classification
• VoVNet-Detectron