vov-net / VoVNet-Detectron

Detectron with VoVNet(CVPRW'19) backbone networks

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

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

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