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FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
| Date | Stars |
|---|---|
| 2026-07-24 | 3348 |
| 2026-07-25 | 3348 |
| 2026-07-28 | 3348 |
| 2026-07-30 | 3348 |
| 2026-08-06 | 3348 |
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# FCOS: Fully Convolutional One-Stage Object Detection
This project hosts the code for implementing the FCOS algorithm for object detection, as presented in our paper:
FCOS: Fully Convolutional One-Stage Object Detection;
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He;
In: Proc. Int. Conf. Computer Vision (ICCV), 2019.
arXiv preprint arXiv:1904.01355
The full paper is available at: [https://arxiv.org/abs/1904.01355](https://arxiv.org/abs/1904.01355).
Implementation based on Detectron2 is included in [AdelaiDet](https://github.com/aim-uofa/AdelaiDet#coco-object-detecton-baselines-with-fcos).
**A real-time model with 46FPS and 40.3 in AP on COCO minival is also available [here](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md#fcos-real-time-models).**
## Highlights
- **Totally anchor-free:** FCOS completely avoids the complicated computation related to anchor boxes and all hyper-parameters of anchor boxes.
- **Better performance:** The very simple one-stage detector achieves much better performance (38.7 vs. 36.8 in AP with ResNet-50) than Faster R-CNN. Check out more models and experimental results [here](#models).
- **Faster training and testing:** With the same hardwares and backbone ResNet-50-FPN, FCOS also requires less training hours (6.5h vs. 8.8h) than Faster R-CNN. FCOS also takes 12ms less inference time per image than Faster R-CNN (44ms vs. 56ms).
- **State-of-the-art performance:** Our best model based on ResNeXt-64x4d-101 and deformable convolutions achieves **49.0%** in AP on COCO test-dev (with multi-scale testing).
## Updates
- FCOS with Fast And Diverse (FAD) neural architecture search is avaliable at [FAD](https://github.com/MalongTech/research-fad). (30/10/2020)
- Script for exporting [ONNX models](https://github.com/tianzhi0549/FCOS/tree/master/onnx). (21/11/2019)
- New NMS (see [#165](https://github.com/tianzhi0549/FCOS/pull/165)) speeds up ResNe(x)t based models by up to 30% and MobileNet based models by 40%, with exactly the same performance. Check out [here](#models). (12/10/2019)
- New models with much improved performance are released. The best model achieves **49%** in AP on COCO test-dev with multi-scale testing. (11/09/2019)
- FCOS with VoVNet backbones is available at [VoVNet-FCOS](https://github.com/vov-net/VoVNet-FCOS). (08/08/2019)
- A trick of using a small central region of the BBox for training improves AP by nearly 1 point [as shown here](https://github.com/yqyao/FCOS_PLUS). (23/07/2019)
- FCOS with HRNet backbones is available at [HRNet-FCOS](https://github.com/HRNet/HRNet-FCOS). (03/07/2019)
- FCOS with AutoML searched FPN (R50, R101, ResNeXt101 and MobileNetV2 backbones) is available at [NAS-FCOS](https://github.com/Lausannen/NAS-FCOS). (30/06/2019)
- FCOS has been implemented in [mmdetection](https://github.com/open-mmlab/mmdetection). Many thanks to [@yhcao6](https://github.com/yhcao6) and [@hellock](https://github.com/hellock). (17/05/2019)
## Required hardware
We use 8 Nvidia V100 GPUs. \
But 4 1080Ti GPUs can also train a fully-fledged ResNet-50-FPN based FCOS since FCOS is memory-efficient.
## Installation
#### Testing-only installation
For users who only want to use FCOS as an object detector in their projects, they can install it by pip. To do so, run:
```
pip install torch # install pytorch if you do not have it
pip install git+https://github.com/tianzhi0549/FCOS.git
# run this command line for a demo
fcos https://github.com/tianzhi0549/FCOS/raw/master/demo/images/COCO_val2014_000000000885.jpg
```
Please check out [here](fcos/bin/fcos) for the interface usage.
#### For a complete installation
This FCOS implementation is based on [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). Therefore the installation is the same as original maskrcnn-benchmark.
Please check [INSTALL.md](INSTALL.md) for installation instructions.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6e53ca3b8d255ac2, topic:computer-vision, topic:object-detection, readme:computer vision