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;
Tian Zhi, Chunhua Shen, Hao Chen, and Tong He;
arXiv preprint arXiv:1904.01355 (2019).

The full paper is available at: https://arxiv.org/abs/1904.01355.

Highlights

• Totally anchor-free: FCOS completely avoids the complicated computation related to anchor boxes and all hyper-parameters of anchor boxes.
• Memory-efficient: FCOS uses 2x less training memory footprint than its anchor-based counterpart RetinaNet.
• Better performance: The very simple detector achieves better performance (37.1 vs. 36.8) than Faster R-CNN.
• Faster training and inference: With the same hardwares, FCOS also requires less training hours (6.5h vs. 8.8h) and faster inference speed (71ms vs. 126 ms per im) than Faster R-CNN.
• State-of-the-art performance: Without bells and whistles, FCOS achieves state-of-the-art performances. It achieves 41.5% (ResNet-101-FPN) and 43.2% (ResNeXt-64x4d-101) in AP on coco test-dev.

Updates

3 July 2019

• FCOS with HRNet backbones is available at HRNet-FCOS.

30 June 2019

• FCOS with AutoML searched FPN (R50, R101, ResNeXt101 and MobileNetV2 backbones) is available at NAS-FCOS.

17 May 2019

• FCOS has been implemented in mmdetection. Many thanks to @yhcao6 and @hellock.

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

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.

A quick demo

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/dDeDPBLEAt19Xrl/download -O FCOS_R_50_FPN_1x.pth
python demo/fcos_demo.py

Inference

The inference command line on coco minival split:

python tools/test_net.py \
    --config-file configs/fcos/fcos_R_50_FPN_1x.yaml \
    MODEL.WEIGHT FCOS_R_50_FPN_1x.pth \
    TEST.IMS_PER_BATCH 4    

Please note that:

1. If your model's name is different, please replace FCOS_R_50_FPN_1x.pth with your own.
2. If you enounter out-of-memory error, please try to reduce TEST.IMS_PER_BATCH to 1.
3. If you want to evaluate a different model, please change --config-file to its config file (in configs/fcos) and MODEL.WEIGHT to its weights file.

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	Total training mem (GB)	Multi-scale training	Testing time / im	AP (minival)	AP (test-dev)	Link
FCOS_R_50_FPN_1x	29.3	No	71ms	37.1	37.4	download
FCOS_R_101_FPN_2x	44.1	Yes	74ms	41.4	41.5	download
FCOS_X_101_32x8d_FPN_2x	72.9	Yes	122ms	42.5	42.7	download
FCOS_X_101_64x4d_FPN_2x	77.7	Yes	140ms	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	19ms	30.9	download
FCOS_syncbn_bs32_MNV2_FPN_1x	32	No	59ms	33.1	download
FCOS_bn_bs16_MNV2_FPN_1x	16	No	59ms	31.0	download

[1] 1x and 2x mean the model is trained for 90K and 180K iterations, respectively.
[2] We report total training memory footprint on all GPUs instead of the memory footprint per GPU as in maskrcnn-benchmark.
[3] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[4] Our results have been improved since our initial release. If you want to check out our original results, please checkout commit f4fd589.
[5] c128 denotes the model has 128 (instead of 256) channels in towers (i.e., MODEL.RESNETS.BACKBONE_OUT_CHANNELS in config).

Training

The following command line will train FCOS_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 \
    --skip-test \
    --config-file configs/fcos/fcos_R_50_FPN_1x.yaml \
    DATALOADER.NUM_WORKERS 2 \
    OUTPUT_DIR training_dir/fcos_R_50_FPN_1x

Note that:

1. If you want to use fewer GPUs, please change --nproc_per_node to the number of GPUs. No other settings need to be changed. The total batch size does not depends on nproc_per_node. If you want to change the total batch size, please change SOLVER.IMS_PER_BATCH in configs/fcos/fcos_R_50_FPN_1x.yaml.
2. The models will be saved into OUTPUT_DIR.
3. If you want to train FCOS with other backbones, please change --config-file.
4. The link of ImageNet pre-training X-101-64x4d in the code is invalid. Please download the model here.
5. If you want to train FCOS on your own dataset, please follow this instruction #54.

Contributing to the project

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{tian2019fcos,
  title   =  {{FCOS}: Fully Convolutional One-Stage Object Detection},
  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  journal =  {arXiv preprint arXiv:1904.01355},
  year    =  {2019}
}

License

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.