https://github.com/zhangxiaosong18/FreeAnchor
FreeAnchor: Learning to Match Anchors for Visual Object Detection (NeurIPS 2019)
https://github.com/zhangxiaosong18/FreeAnchor
computer-vision freeanchor neurips-2019 object-detection one-stage pytorch
Last synced: about 1 month ago
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FreeAnchor: Learning to Match Anchors for Visual Object Detection (NeurIPS 2019)
- Host: GitHub
- URL: https://github.com/zhangxiaosong18/FreeAnchor
- Owner: zhangxiaosong18
- License: mit
- Created: 2019-09-04T05:41:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-30T07:03:43.000Z (over 5 years ago)
- Last Synced: 2024-07-31T21:53:39.903Z (9 months ago)
- Topics: computer-vision, freeanchor, neurips-2019, object-detection, one-stage, pytorch
- Language: Python
- Homepage: https://arxiv.org/abs/1909.02466
- Size: 1.82 MB
- Stars: 675
- Watchers: 21
- Forks: 111
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-anchor-free-object-detection - FreeAnchor - Abstract.html)**) (Frameworks)
- Awesome-Object-Detections - pytorch
README
## FreeAnchor
The Code for ["FreeAnchor: Learning to Match Anchors for Visual Object Detection"](https://arxiv.org/abs/1909.02466).
This repository is based on maskrcnn-benchmark, and FreeAnchor has also been implemented in [mmdetection](https://github.com/open-mmlab/mmdetection), thanks [@yhcao6](https://github.com/yhcao6) and [@hellock](https://github.com/hellock).
### New performance on COCO
We added multi-scale testing support and updated experiments. The previous version is in [this branch](https://github.com/zhangxiaosong18/FreeAnchor/tree/previous).
| Backbone | Iteration | Training scales | Multi-scale
testing | AP
(minival) | AP
(test-dev) | Model |
| :-------------------: | :-------: | :-------------: | :--------------: | :-------------: | :--------------: | :--------: |
| ResNet-50-FPN | 90k | 800 | N | 38.7 | 38.7 | [Link](https://drive.google.com/open?id=1o-VvpOIwYCPxyas8n6OngpOznlVy0T6k ) |
| ResNet-101-FPN | 90k | 800 | N | 40.5 | 40.9 | [Link](https://drive.google.com/open?id=1jc5ncxuuuG3-sm-4OpkOfr51ClwCWHu0 ) |
| ResNet-101-FPN | 180k | [640, 800] | N | 42.7 | 43.1 | [Link](https://drive.google.com/open?id=1OvK8Xona8v7mWU2nf5Fp1QzvwUaFhlIg ) |
| ResNet-101-FPN | 180k | [480, 960] | N | 43.2 | 43.9 | [Link](https://drive.google.com/open?id=1ZIx2HTexVyU6xTwAm2ABTYjsJuLvlfhB ) |
| ResNet-101-FPN | 180k | [480, 960] | Y | 44.7 | 45.2 | [Link](https://drive.google.com/open?id=1ZIx2HTexVyU6xTwAm2ABTYjsJuLvlfhB ) |
| ResNeXt-64x4d-101-FPN | 180k | [640, 800] | N | 44.5 | 44.9 | [Link](https://drive.google.com/open?id=1MrtXoBGHceq_BBY5cH-kw2ax5-aDPdTv ) |
| ResNeXt-64x4d-101-FPN | 180k | [480, 960] | N | 45.6 | 46.0 | [Link](https://drive.google.com/open?id=1r17agiu76xtwKxn2oE_pK4R847k-Cu5m ) |
| ResNeXt-64x4d-101-FPN | 180k | [480, 960] | Y | 46.8 | 47.3 | [Link](https://drive.google.com/open?id=1r17agiu76xtwKxn2oE_pK4R847k-Cu5m ) |
**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](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
```bash
cd path_to_free_anchor
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "path/to/config/file.yaml"
```
#### Test on COCO test-dev
```bash
cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$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
```bash
cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/multi_scale_test.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT "path/to/.pth file" DATASETS.TEST "('coco_test-dev',)"
```
#### Evaluate NMS Recall
```bash
cd path_to_free_anchor
python -m torch.distributed.launch --nproc_per_node=$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}
}
```