crazyvertigo/freeanchor

FreeAnchor: Learning to Match Anchors for Visual Object Detection (NeurIPS 2019)

FreeAnchor

The Code for "FreeAnchor: Learning to Match Anchors for Visual Object Detection".

This repository is based on maskrcnn-benchmark, and FreeAnchor has also been implemented in mmdetection, thanks @yhcao6 and @hellock.

New performance on COCO

We added multi-scale testing support and updated experiments. The previous version is in this branch.

Backbone	Iteration	Training scales	Multi-scale testing	AP (minival)	AP (test-dev)	Model
ResNet-50-FPN	90k	800	N	38.7	38.7	Link
ResNet-101-FPN	90k	800	N	40.5	40.9	Link
ResNet-101-FPN	180k	[640, 800]	N	42.7	43.1	Link
ResNet-101-FPN	180k	[480, 960]	N	43.2	43.9	Link
ResNet-101-FPN	180k	[480, 960]	Y	44.7	45.2	Link
ResNeXt-64x4d-101-FPN	180k	[640, 800]	N	44.5	44.9	Link
ResNeXt-64x4d-101-FPN	180k	[480, 960]	N	45.6	46.0	Link
ResNeXt-64x4d-101-FPN	180k	[480, 960]	Y	46.8	47.3	Link

Notes:

• We use 8 GPUs with 2 image / GPU.
• In multi-scale testing, we use image scales in {480, 640, 800, 960, 1120, 1280} and max_size are 1.666× than scales.

Installation

Check INSTALL.md for installation instructions.

Usage

You will need to download the COCO dataset and configure your own paths to the datasets.

For that, all you need to do is to modify maskrcnn_benchmark/config/paths_catalog.py to point to the location where your dataset is stored.

Config Files

We provide four configuration files in the configs directory.

Config File	Backbone	Iteration	Training scales
configs/free_anchor_R-50-FPN_1x.yaml	ResNet-50-FPN	90k	800
configs/free_anchor_R-101-FPN_1x.yaml	ResNet-101-FPN	90k	800
configs/free_anchor_R-101-FPN_j2x.yaml	ResNet-101-FPN	180k	[640, 800]
configs/free_anchor_X-101-FPN_j2x.yaml	ResNeXt-64x4d-101-FPN	180k	[640, 800]
configs/free_anchor_R-101-FPN_e2x.yaml	ResNet-101-FPN	180k	[480, 960]
configs/free_anchor_X-101-FPN_e2x.yaml	ResNeXt-64x4d-101-FPN	180k	[480, 960]

Training with 8 GPUs

cd path_to_free_anchor
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "path/to/config/file.yaml"

Test on COCO test-dev

cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file" DATASETS.TEST "('coco_test-dev',)"

Multi-scale testing

cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/multi_scale_test.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file" DATASETS.TEST "('coco_test-dev',)"

Evaluate NMS Recall

cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/eval_NR.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file"

Citations

Please consider citing our paper in your publications if the project helps your research.

@inproceedings{zhang2019freeanchor,
  title   =  {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection},
  author  =  {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang},
  booktitle =  {Neural Information Processing Systems},
  year    =  {2019}
}