The Code for "FreeAnchor: Learning to Match Anchors for Visual Object Detection". [https://arxiv.org/abs/1909.02466]
Detection performance on COCO:
| Hardware | Backbone | Iteration | Scale jittering train / test |
AP (minival) |
AP (test-dev) |
model link |
|---|---|---|---|---|---|---|
| 4 x V100 | ResNet-50-FPN | 90k | N / N | 38.6 | 39.1 | Google Drive Baidu Drive |
| 4 x V100 | ResNet-101-FPN | 90k | N / N | 41.0 | 41.3 | Google Drive Baidu Drive |
| 4 x V100 | ResNet-101-FPN | 135k | N / N | 41.3 | 41.8 | Google Drive Baidu Drive |
| 4 x V100 | ResNeXt-101-32x8d-FPN | 135k | Y / N | 44.2 | 44.8 | Google Drive Baidu Drive |
| Hardware | Backbone | Iteration | Scale jittering train / test |
AP (minival) |
AP (test-dev) |
model link |
|---|---|---|---|---|---|---|
| 8 x 2080Ti | ResNet-50-FPN | 90k | N / N | 38.4 | 38.9 | Google Drive Baidu Drive |
| 8 x 2080Ti | ResNet-101-FPN | 90k | N / N | 40.4 | 41.1 | Google Drive Baidu Drive |
| 8 x 2080Ti | ResNet-101-FPN | 135k | N / N | 41.1 | 41.5 | Google Drive Baidu Drive |
| 8 x 2080Ti | ResNeXt-101-32x8d-FPN | 135k | Y / N | 44.2 | 44.9 | Google Drive Baidu Drive |
| Hardware | Backbone | Iteration | Scale jittering train / test |
AP (minival) |
AP (test-dev) |
model link |
|---|---|---|---|---|---|---|
| 8 x 2080Ti | ResNet-101-FPN | 180k | Y / N | 42.7 | 43.1 | Google Drive Baidu Drive |
Check INSTALL.md for installation instructions.
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.
We provide four configuration files in the configs directory.
| Backbone | Iteration | Scale jittering train / test |
Config File |
|---|---|---|---|
| ResNet-50-FPN | 90k | N / N | configs/free_anchor_R-50-FPN_1x.yaml |
| ResNet-101-FPN | 90k | N / N | configs/free_anchor_R-101-FPN_1x.yaml |
| ResNet-101-FPN | 135k | N / N | configs/free_anchor_R-101-FPN_1.5x.yaml |
| ResNeXt-101-32x8d-FPN | 135k | Y / N | configs/free_anchor_X-101-FPN_j1.5x.yaml |
cd path_to_free_anchor
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "path/to/config/file.yaml"cd path_to_free_anchor
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "path/to/config/file.yaml"cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file" DATASETS.TEST "('coco_test-dev',)"cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/eval_NR.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file"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}
}