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https://github.com/taokong/foveabox
FoveaBox: Beyond Anchor-based Object Detector
https://github.com/taokong/foveabox
anchor-free coco detection foveabox object-detection pytorch
Last synced: about 10 hours ago
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FoveaBox: Beyond Anchor-based Object Detector
- Host: GitHub
- URL: https://github.com/taokong/foveabox
- Owner: taokong
- License: apache-2.0
- Created: 2019-08-14T03:46:50.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-29T04:24:08.000Z (over 1 year ago)
- Last Synced: 2024-11-06T06:02:24.619Z (7 days ago)
- Topics: anchor-free, coco, detection, foveabox, object-detection, pytorch
- Language: Python
- Homepage: https://arxiv.org/abs/1904.03797
- Size: 3.68 MB
- Stars: 367
- Watchers: 13
- Forks: 46
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# FoveaBox: Beyond Anchor-based Object Detector
This repo is a official implementation of "FoveaBox: Beyond Anchor-based Object Detector" on COCO object detection based on open-mmlab's mmdetection. Many thanks to mmdetection for their simple and clean framework.
## News
FoveaBox is supported by the official mmdetection repo [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox). Thanks again for open-mmlab's work on open source projects.
## Introduction
FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797):
Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
FoveaBox detection process.
## Installation
This FoveaBox implementation is based on [mmdetection](https://github.com/open-mmlab/mmdetection). Therefore the installation is the same as original mmdetection.
Please check [INSTALL.md](INSTALL.md) for installation instructions.
## Train and inference
The FoveaBox config is in [configs/foveabox](configs/foveabox).
### Inference
# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] --eval bbox [--show]
# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] --eval bbox
### Training
# single-gpu training
python tools/train.py ${CONFIG_FILE}
# multi-gpu training
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
Please check [GETTING_STARTED.md](GETTING_STARTED.md) for detailed instructions.
## Main Results
### Results on R50/101-FPN with backbone
| Backbone | Style | align | ms-train| Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50 | pytorch | N | N | 1x | 5.7 | 0.450 | 13.5 | 36.5 | [model](https://drive.google.com/file/d/19eQNnctoC1VTcP2AKdCryQGjb6Dzq62r/view?usp=sharing) |
| R-50 | pytorch | N | N | 2x | - | - | | 36.9 | [model](https://drive.google.com/file/d/1W-9DrNQcaw4vaLLON8GLe86pfBXztbWR/view?usp=sharing) |
| R-50 | pytorch | Y | N | 2x | - | - | | 37.9 | [model](https://drive.google.com/file/d/1RtTizixUDqd7X-PifTI7rseXZ1Q8YaAf/view?usp=sharing) |
| R-50 | pytorch | Y | Y | 2x | - | - | | 40.1 | [model](https://drive.google.com/file/d/1bHwmP3Uy-lFUifAyzkWykZTkYY-v-nEN/view?usp=sharing) |
| R-101 | pytorch | N | N | 1x | 9.4 | 0.712 | 11.5 | 38.5 | [model](https://drive.google.com/file/d/1Xb6hDUquGKB8ad7DigrF8K9sX8xoZigh/view?usp=sharing) |
| R-101 | pytorch | N | N | 2x | - | - | - | 38.5 | [model](https://drive.google.com/file/d/1ToZyqAxjWIZ4N8SDL4gufmkA-Wjz_VUW/view?usp=sharing) |
| R-101 | pytorch | Y | N | 2x | - | - | - | 39.4 | [model](https://drive.google.com/file/d/1n34MNGfgrMmJdpT2xAaEQOw8GJhTd1z8/view?usp=sharing) |
| R-101 | pytorch | Y | Y | 2x | - | - | - | 41.9 | [model](https://drive.google.com/file/d/1ZQAsW9SxMdCTX3_pjIIHotg0yDT2wy34/view?usp=sharing) |
[1] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \
[2] *Align means utilizing deformable convolution to align the cls branch.* \
[3] *All results are obtained with a single model and without any test time data augmentation.*\
[4] *We use 4 NVIDIA Tesla V100 GPUs for training.*
Any pull requests or issues are welcome.
## Citations
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
```
@article{kong2019foveabox,
title={FoveaBox: Beyond Anchor-based Object Detector},
author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Li, Lei and Shi, Jianbo},
journal={IEEE Transactions on Image Processing},
pages={7389--7398},
year={2020}
}
```