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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). ## 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 - 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. You may also want to see the original [README.md](MASKRCNN_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/ZSAqNJB96hA71Yf/download -O FCOS_imprv_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_imprv_R_50_FPN_1x.yaml \ MODEL.WEIGHT FCOS_imprv_R_50_FPN_1x.pth \ TEST.IMS_PER_BATCH 4 Please note that: 1) If your model's name is different, please replace `FCOS_imprv_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](configs/fcos)) and `MODEL.WEIGHT` to its weights file. 4) Multi-GPU inference is available, please refer to [#78](https://github.com/tianzhi0549/FCOS/issues/78#issuecomment-526990989). 5) We improved the postprocess efficiency by using multi-label nms (see [#165](https://github.com/tianzhi0549/FCOS/pull/165)), which saves 18ms on average. The inference metric in the following tables has been updated accordingly. ## Models 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](https://github.com/facebookresearch/maskrcnn-benchmark)).* Model | Multi-scale training | Testing time / im | AP (minival) | Link --- |:---:|:---:|:---:|:---: FCOS_imprv_R_50_FPN_1x | No | 44ms | 38.7 | [download](https://cloudstor.aarnet.edu.au/plus/s/ZSAqNJB96hA71Yf/download) FCOS_imprv_dcnv2_R_50_FPN_1x | No | 54ms | 42.3 | [download](https://cloudstor.aarnet.edu.au/plus/s/plKgHuykjiilzWr/download) FCOS_imprv_R_101_FPN_2x | Yes | 57ms | 43.0 | [download](https://cloudstor.aarnet.edu.au/plus/s/hTeMuRa4pwtCemq/download) FCOS_imprv_dcnv2_R_101_FPN_2x | Yes | 73ms | 45.6 | [download](https://cloudstor.aarnet.edu.au/plus/s/xq2Ll7s0hpaQycO/download) FCOS_imprv_X_101_32x8d_FPN_2x | Yes | 110ms | 44.0 | [download](https://cloudstor.aarnet.edu.au/plus/s/WZ0i7RZW5BRpJu6/download) FCOS_imprv_dcnv2_X_101_32x8d_FPN_2x | Yes | 143ms | 46.4 | [download](https://cloudstor.aarnet.edu.au/plus/s/08UK0OP67TogLCU/download) FCOS_imprv_X_101_64x4d_FPN_2x | Yes | 112ms | 44.7 | [download](https://cloudstor.aarnet.edu.au/plus/s/rKOJtwvJwcKVOz8/download) FCOS_imprv_dcnv2_X_101_64x4d_FPN_2x | Yes | 144ms | 46.6 | [download](https://cloudstor.aarnet.edu.au/plus/s/jdtVmG7MlugEXB7/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 | 45ms | 37.1 | 37.4 | [download](https://cloudstor.aarnet.edu.au/plus/s/dDeDPBLEAt19Xrl/download) FCOS_R_101_FPN_2x | Yes | 59ms | 41.4 | 41.5 | [download](https://cloudstor.aarnet.edu.au/plus/s/vjL3L0AW7vnhRTo/download) FCOS_X_101_32x8d_FPN_2x | Yes | 110ms | 42.5 | 42.7 | [download](https://cloudstor.aarnet.edu.au/plus/s/U5myBfGF7MviZ97/download) FCOS_X_101_64x4d_FPN_2x | Yes | 113ms | 43.0 | 43.2 | [download](https://cloudstor.aarnet.edu.au/plus/s/wpwoCi4S8iajFi9/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 | 26ms | 30.9 | [download](https://cloudstor.aarnet.edu.au/plus/s/3GKwaxZhDSOlCZ0/download) FCOS_syncbn_bs32_MNV2_FPN_1x | 32 | No | 33ms | 33.1 | [download](https://cloudstor.aarnet.edu.au/plus/s/OpJtCJLj104i2Yc/download) FCOS_bn_bs16_MNV2_FPN_1x | 16 | No | 44ms | 31.0 | [download](https://cloudstor.aarnet.edu.au/plus/s/B6BrLAiAEAYQkcy/download) [1] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively