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;
Tian Zhi, 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.
- 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.
- Faster training: With the same hardwares, FCOS also requires less training hours (6.5h vs. 8.8h) than Faster R-CNN.
- 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).
- New models with much improved performance are released. The best model achieves 49% in AP on COCO test-dev with multi-scale testing.
- FCOS with VoVNet backbones is available at VoVNet-FCOS.
- A trick of using a small central region of the BBox for training improves AP by nearly 1 point as shown here.
- FCOS with HRNet backbones is available at HRNet-FCOS.
- FCOS with AutoML searched FPN (R50, R101, ResNeXt101 and MobileNetV2 backbones) is available at NAS-FCOS.
- FCOS has been implemented in mmdetection. Many thanks to @yhcao6 and @hellock.
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.
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 fcos
# 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 for the interface usage.
This FCOS implementation is based on maskrcnn-benchmark. Therefore the installation is the same as original maskrcnn-benchmark.
Please check INSTALL.md for installation instructions. You may also want to see the original README.md of maskrcnn-benchmark.
Once the installation is done, you can follow the below steps to run a quick demo.
# assume that you are under the root directory of this project,
# and you have activated your virtual environment if needed.
wget https://cloudstor.aarnet.edu.au/plus/s/ZSAqNJB96hA71Yf/download -O FCOS_imprv_R_50_FPN_1x.pth
python demo/fcos_demo.py
The inference command line on coco minival split:
python tools/test_net.py \
--config-file configs/fcos/fcos_imprv_R_50_FPN_1x.yaml \
MODEL.WEIGHT FCOS_imprv_R_50_FPN_1x.pth \
TEST.IMS_PER_BATCH 4
Please note that:
- If your model's name is different, please replace
FCOS_imprv_R_50_FPN_1x.pthwith your own. - If you enounter out-of-memory error, please try to reduce
TEST.IMS_PER_BATCHto 1. - If you want to evaluate a different model, please change
--config-fileto its config file (in configs/fcos) andMODEL.WEIGHTto its weights file. - Multi-GPU inference is available, please refer to #78.
For your convenience, we provide the following trained models (more models are coming soon).
ResNe(x)ts:
All ResNe(x)t based models are trained with 16 images in a mini-batch and frozen batch normalization (i.e., consistent with models in maskrcnn_benchmark).
| Model | Multi-scale training | Testing time / im | AP (minival) | Link |
|---|---|---|---|---|
| FCOS_imprv_R_50_FPN_1x | No | 62ms | 38.7 | download |
| FCOS_imprv_dcnv2_R_50_FPN_1x | No | 70ms | 42.3 | download |
| FCOS_imprv_R_101_FPN_2x | Yes | 76ms | 43.0 | download |
| FCOS_imprv_dcnv2_R_101_FPN_2x | Yes | 89ms | 45.6 | download |
| FCOS_imprv_X_101_32x8d_FPN_2x | Yes | 130ms | 44.0 | download |
| FCOS_imprv_dcnv2_X_101_32x8d_FPN_2x | Yes | 157ms | 46.4 | download |
| FCOS_imprv_X_101_64x4d_FPN_2x | Yes | 133ms | 44.7 | download |
| FCOS_imprv_dcnv2_X_101_64x4d_FPN_2x | Yes | 160ms | 46.6 | download |
Note that imprv denotes improvements in our paper Table 3. These almost cost-free changes improve the performance by ~1.5% in total. Thus, we highly recommend to use them. The following are the original models presented in our initial paper.
| Model | Multi-scale training | Testing time / im | AP (minival) | AP (test-dev) | Link |
|---|---|---|---|---|---|
| FCOS_R_50_FPN_1x | No | 64ms | 37.1 | 37.4 | download |
| FCOS_R_101_FPN_2x | Yes | 78ms | 41.4 | 41.5 | download |
| FCOS_X_101_32x8d_FPN_2x | Yes | 132ms | 42.5 | 42.7 | download |
| FCOS_X_101_64x4d_FPN_2x | Yes | 135ms | 43.0 | 43.2 | download |
MobileNets:
We update batch normalization for MobileNet based models. If you want to use SyncBN, please install pytorch 1.1 or later.
| Model | Training batch size | Multi-scale training | Testing time / im | AP (minival) | Link |
|---|---|---|---|---|---|
| FCOS_syncbn_bs32_c128_MNV2_FPN_1x | 32 | No | 45ms | 30.9 | download |
| FCOS_syncbn_bs32_MNV2_FPN_1x | 32 | No | 52ms | 33.1 | download |
| FCOS_bn_bs16_MNV2_FPN_1x | 16 | No | 52ms | 31.0 | download |
[1] 1x and 2x mean the model is trained for 90K and 180K iterations, respectively.
[2] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[3] c128 denotes the model has 128 (instead of 256) channels in towers (i.e., MODEL.RESNETS.BACKBONE_OUT_CHANNELS in config).
[4] dcnv2 denotes deformable convolutional networks v2. Note that for ResNet based models, we apply deformable convolutions from stage c3 to c5 in backbones. For ResNeXt based models, only stage c4 and c5 use deformable convolutions. All models use deformable convolutions in the last layer of detector towers.
[5] The model FCOS_imprv_dcnv2_X_101_64x4d_FPN_2x with multi-scale testing achieves 49.0% in AP on COCO test-dev. Please use TEST.BBOX_AUG.ENABLED True to enable multi-scale testing.
The following command line will train FCOS_imprv_R_50_FPN_1x on 8 GPUs with Synchronous Stochastic Gradient Descent (SGD):
python -m torch.distributed.launch \
--nproc_per_node=8 \
--master_port=$((RANDOM + 10000)) \
tools/train_net.py \
--config-file configs/fcos/fcos_imprv_R_50_FPN_1x.yaml \
DATALOADER.NUM_WORKERS 2 \
OUTPUT_DIR training_dir/fcos_imprv_R_50_FPN_1x
Note that:
- If you want to use fewer GPUs, please change
--nproc_per_nodeto the number of GPUs. No other settings need to be changed. The total batch size does not depends onnproc_per_node. If you want to change the total batch size, please changeSOLVER.IMS_PER_BATCHin configs/fcos/fcos_R_50_FPN_1x.yaml. - The models will be saved into
OUTPUT_DIR. - If you want to train FCOS with other backbones, please change
--config-file. - If you want to train FCOS on your own dataset, please follow this instruction #54.
- Now, training with 8 GPUs and 4 GPUs can have the same performance. Previous performance gap was because we did not synchronize
num_posbetween GPUs when computing loss.
Any pull requests or issues are welcome.
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
@inproceedings{tian2019fcos,
title = {{FCOS}: Fully Convolutional One-Stage Object Detection},
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
booktitle = {Proc. Int. Conf. Computer Vision (ICCV)},
year = {2019}
}
We would like to thank @yqyao for the tricks of center sampling and GIoU. We also thank @bearcatt for his suggestion of positioning the center-ness branch with box regression (refer to #89).
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact the authors.