End-to-End Object Detection with Fully Convolutional Network
This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.
Experiments in the paper were conducted on the internal framework, thus we reimplement them on cvpods and report details as below.
Requirements
- cvpods
- scipy >= 1.5.4
Get Started
- install cvpods locally (requires cuda to compile)
python3 -m pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
python3 -m pip install -e cvpods
# Or,
pip install -r requirements.txt
python3 setup.py build develop
- prepare datasets
cd /path/to/cvpods
cd datasets
ln -s /path/to/your/coco/dataset coco
- Train & Test
git clone https://github.com/Megvii-BaseDetection/DeFCN.git
cd DeFCN/playground/detection/coco/poto.res50.fpn.coco.800size.3x_ms # for example
# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
OUTPUT_DIR /path/to/your/save_dir # optional
# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"
Results on COCO2017 val set
| model | assignment | with NMS | lr sched. | mAP | mAR | download |
|---|---|---|---|---|---|---|
| FCOS | one-to-many | Yes | 3x + ms | 41.4 | 59.1 | weight | log |
| FCOS baseline | one-to-many | Yes | 3x + ms | 40.9 | 58.4 | weight | log |
| Anchor | one-to-one | No | 3x + ms | 37.1 | 60.5 | weight | log |
| Center | one-to-one | No | 3x + ms | 35.2 | 61.0 | weight | log |
| Foreground Loss | one-to-one | No | 3x + ms | 38.7 | 62.2 | weight | log |
| POTO | one-to-one | No | 3x + ms | 39.2 | 61.7 | weight | log |
| POTO + 3DMF | one-to-one | No | 3x + ms | 40.6 | 61.6 | weight | log |
| POTO + 3DMF + Aux | mixture* | No | 3x + ms | 41.4 | 61.5 | weight | log |
* We adopt a one-to-one assignment in POTO and a one-to-many assignment in the auxiliary loss, respectively.
2x + msschedule is adopted in the paper, but we adopt3x + msschedule here to achieve higher performance.- It's normal to observe ~0.3AP noise in POTO.
Results on CrowdHuman val set
| model | assignment | with NMS | lr sched. | AP50 | mMR | recall | download |
|---|---|---|---|---|---|---|---|
| FCOS | one-to-many | Yes | 30k iters | 86.1 | 54.9 | 94.2 | weight | log |
| ATSS | one-to-many | Yes | 30k iters | 87.2 | 49.7 | 94.0 | weight | log |
| POTO | one-to-one | No | 30k iters | 88.5 | 52.2 | 96.3 | weight | log |
| POTO + 3DMF | one-to-one | No | 30k iters | 88.8 | 51.0 | 96.6 | weight | log |
| POTO + 3DMF + Aux | mixture* | No | 30k iters | 89.1 | 48.9 | 96.5 | weight | log |
* We adopt a one-to-one assignment in POTO and a one-to-many assignment in the auxiliary loss, respectively.
- It's normal to observe ~0.3AP noise in POTO, and ~1.0mMR noise in all methods.
Ablations on COCO2017 val set
| model | assignment | with NMS | lr sched. | mAP | mAR | note |
|---|---|---|---|---|---|---|
| POTO | one-to-one | No | 6x + ms | 40.0 | 61.9 | |
| POTO | one-to-one | No | 9x + ms | 40.2 | 62.3 | |
| POTO | one-to-one | No | 3x + ms | 39.2 | 61.1 | replace Hungarian algorithm by argmax |
| POTO + 3DMF | one-to-one | No | 3x + ms | 40.9 | 62.0 | remove GN in 3DMF |
| POTO + 3DMF + Aux | mixture* | No | 3x + ms | 41.5 | 61.5 | remove GN in 3DMF |
* We adopt a one-to-one assignment in POTO and a one-to-many assignment in the auxiliary loss, respectively.
- For
one-to-oneassignment, more training iters lead to higher performance. - The
argmax(also known as top-1) operation is indeed the approximate solution of bipartite matching in dense prediction methods. - It seems harmless to remove GN in 3DMF, which also leads to higher inference speed.
Acknowledgement
This repo is developed based on cvpods. Please check cvpods for more details and features.
License
This repo is released under the Apache 2.0 license. Please see the LICENSE file for more information.
Citing
If you use this work in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:
@article{wang2020end,
title = {End-to-End Object Detection with Fully Convolutional Network},
author = {Wang, Jianfeng and Song, Lin and Li, Zeming and Sun, Hongbin and Sun, Jian and Zheng, Nanning},
journal = {arXiv preprint arXiv:2012.03544},
year = {2020}
}
Contributing to the project
Any pull requests or issues about the implementation are welcome. If you have any issue about the library (e.g. installation, environments), please refer to cvpods.
42 Nov 10, 2022
92 Jan 3, 2023
114 Nov 28, 2022
1.2k Dec 27, 2022
487 Dec 31, 2022
34 Dec 31, 2022
111 Dec 27, 2022
116 Nov 9, 2022
42 Dec 30, 2022
148 Dec 21, 2022
5 Dec 10, 2022
3 Jan 26, 2022
2k Jan 5, 2023
348 Jan 7, 2023
842 Jan 4, 2023
115 Dec 28, 2022
158 Dec 21, 2022
606 Dec 21, 2022