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Complete-IoU Loss and Cluster-NMS for Improving Object Detection and Instance Segmentation.

Our paper is accepted by IEEE Transactions on Cybernetics (TCYB).

This repo is based on YOLACT++.

This is the code for our papers:

• Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
• Enhancing Geometric Factors into Model Learning and Inference for Object Detection and Instance Segmentation

@Inproceedings{zheng2020diou,
  author    = {Zheng, Zhaohui and Wang, Ping and Liu, Wei and Li, Jinze and Ye, Rongguang and Ren, Dongwei},
  title     = {Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression},
  booktitle = {The AAAI Conference on Artificial Intelligence (AAAI)},
  pages     = {12993--13000},
  year      = {2020}
}

@Article{zheng2021ciou,
  author    = {Zheng, Zhaohui and Wang, Ping and Ren, Dongwei and Liu, Wei and Ye, Rongguang and Hu, Qinghua and Zuo, Wangmeng},
  title     = {Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation},
  journal   = {IEEE Transactions on cybernetics},
  volume    = {52},
  number    = {8},
  pages     = {8574--8586},
  year      = {2021},
  publisher = {IEEE}
}

Description of Cluster-NMS and Its Usage

An example diagram of our Cluster-NMS, where X denotes IoU matrix which is calculated by X=jaccard(boxes,boxes).triu_(diagonal=1) > nms_thresh after sorted by score descending. (Here use 0,1 for visualization.)

The inputs of NMS are boxes with size [n,4] and scores with size [80,n]. (take coco as example)

There are two ways for NMS. One is that all classes have the same number of boxes. First, we use top k=200 to select the top 200 detections for every class. Then boxes will be [80,200,4]. Do Cluster-NMS and keep the boxes with scores>0.01. Finally, return top 100 boxes across all classes.

The other approach is that different classes have different numbers of boxes. First, we use a score threshold (e.g. 0.01) to filter out most low score detection boxes. It results in the number of remaining boxes in different classes may be different. Then put all the boxes together and sorted by score descending. (Note that the same box may appear more than once, because its scores of multiple classes are greater than the threshold 0.01.) Adding offset for all the boxes according to their class labels. (use torch.arange(0,80).) For example, since the coordinates (x1,y1,x2,y2) of all the boxes are on interval (0,1). By adding offset, if a box belongs to class 61, its coordinates will on interval (60,61). After that, the IoU of boxes belonging to different classes will be 0. (because they are treated as different clusters.) Do Cluster-NMS and return top 100 boxes across all classes. (For this method, please refer to another our repository https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/detection/detection.py)

Getting Started

1) New released! CIoU and Cluster-NMS

1. YOLACT (See YOLACT)

2. YOLOv3-pytorch https://github.com/Zzh-tju/ultralytics-YOLOv3-Cluster-NMS

3. YOLOv5 (Support batch mode Cluster-NMS. It will speed up NMS when turning on test-time augmentation like multi-scale testing.) https://github.com/Zzh-tju/yolov5

4. SSD-pytorch https://github.com/Zzh-tju/DIoU-SSD-pytorch

2) DIoU and CIoU losses into Detection Algorithms

DIoU and CIoU losses are incorporated into state-of-the-art detection algorithms, including YOLO v3, SSD and Faster R-CNN. The details of implementation and comparison can be respectively found in the following links.

1. YOLO v3 https://github.com/Zzh-tju/DIoU-darknet

2. SSD https://github.com/Zzh-tju/DIoU-SSD-pytorch

3. Faster R-CNN https://github.com/Zzh-tju/DIoU-pytorch-detectron

4. Simulation Experiment https://github.com/Zzh-tju/DIoU

YOLACT

Codes location and options

Please take a look at ciou function of layers/modules/multibox_loss.py for our CIoU loss implementation in PyTorch.

Currently, NMS surports two modes: (See eval.py)

1. Cross-class mode, which ignores classes. (cross_class_nms=True, faster than per-class mode but with a slight performance drop.)

2. Per-class mode. (cross_class_nms=False)

Currently, NMS supports fast_nms, cluster_nms, cluster_diounms, spm, spm_dist, spm_dist_weighted.

See layers/functions/detection.py for our Cluster-NMS implementation in PyTorch.

Installation

In order to use YOLACT++, make sure you compile the DCNv2 code.

• Clone this repository and enter it:

  git clone https://github.com/Zzh-tju/CIoU.git
  cd yolact

• Set up the environment using one of the following methods:
  • Using Anaconda
    • Run conda env create -f environment.yml
  • Manually with pip
    • Set up a Python3 environment (e.g., using virtenv).
    • Install Pytorch 1.0.1 (or higher) and TorchVision.
    • Install some other packages:

      # Cython needs to be installed before pycocotools
      pip install cython
      pip install opencv-python pillow pycocotools matplotlib 

• If you'd like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into ./data/coco.

  sh data/scripts/COCO.sh

• If you'd like to evaluate YOLACT on test-dev, download test-dev with this script.

  sh data/scripts/COCO_test.sh

• If you want to use YOLACT++, compile deformable convolutional layers (from DCNv2). Make sure you have the latest CUDA toolkit installed from NVidia's Website.

  cd external/DCNv2
  python setup.py build develop

Evaluation

Here are our YOLACT models (released on May 5th, 2020) along with their FPS on a GTX 1080 Ti and mAP on coco 2017 val:

The training is carried on two GTX 1080 Ti with command: python train.py --config=yolact_base_config --batch_size=8

Image Size	Backbone	Loss	NMS	FPS	box AP	mask AP	Weights
550	Resnet101-FPN	SL1	Fast NMS	30.6	31.5	29.1	SL1.pth
550	Resnet101-FPN	CIoU	Fast NMS	30.6	32.1	29.6	CIoU.pth

To evalute the model, put the corresponding weights file in the ./weights directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., yolact_base for yolact_base_54_800000.pth).

Quantitative Results on COCO

# Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.

# Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.
# This command will create './results/bbox_detections.json' and './results/mask_detections.json' for detection and instance segmentation respectively.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json

# You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.
python run_coco_eval.py

# To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset

Qualitative Results on COCO

# Display qualitative results on COCO. From here on I'll use a confidence threshold of 0.15.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display

Cluster-NMS Using Benchmark on COCO

python eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark

Hardware

• 1 GTX 1080 Ti
• Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz

Image Size	Backbone	Loss	NMS	FPS	box AP	box AP75	box AR100	mask AP	mask AP75	mask AR100
550	Resnet101-FPN	CIoU	Fast NMS	30.6	32.1	33.9	43.0	29.6	30.9	40.3
550	Resnet101-FPN	CIoU	Original NMS	11.5	32.5	34.1	45.1	29.7	31.0	41.7
550	Resnet101-FPN	CIoU	Cluster-NMS	28.8	32.5	34.1	45.2	29.7	31.0	41.7
550	Resnet101-FPN	CIoU	SPM Cluster-NMS	28.6	33.1	35.2	48.8	30.3	31.7	43.6
550	Resnet101-FPN	CIoU	SPM + Distance Cluster-NMS	27.1	33.2	35.2	49.2	30.2	31.7	43.8
550	Resnet101-FPN	CIoU	SPM + Distance + Weighted Cluster-NMS	26.5	33.4	35.5	49.1	30.3	31.6	43.8

The following table is evaluated by using their pretrained weight of YOLACT. (yolact_resnet50_54_800000.pth)

Image Size	Backbone	Loss	NMS	FPS	box AP	box AP75	box AR100	mask AP	mask AP75	mask AR100
550	Resnet50-FPN	SL1	Fast NMS	41.6	30.2	31.9	42.0	28.0	29.1	39.4
550	Resnet50-FPN	SL1	Original NMS	12.8	30.7	32.0	44.1	28.1	29.2	40.7
550	Resnet50-FPN	SL1	Cluster-NMS	38.2	30.7	32.0	44.1	28.1	29.2	40.7
550	Resnet50-FPN	SL1	SPM Cluster-NMS	37.7	31.3	33.2	48.0	28.8	29.9	42.8
550	Resnet50-FPN	SL1	SPM + Distance Cluster-NMS	35.2	31.3	33.3	48.2	28.7	29.9	42.9
550	Resnet50-FPN	SL1	SPM + Distance + Weighted Cluster-NMS	34.2	31.8	33.9	48.3	28.8	29.9	43.0

The following table is evaluated by using their pretrained weight of YOLACT. (yolact_base_54_800000.pth)

Image Size	Backbone	Loss	NMS	FPS	box AP	box AP75	box AR100	mask AP	mask AP75	mask AR100
550	Resnet101-FPN	SL1	Fast NMS	30.6	32.5	34.6	43.9	29.8	31.3	40.8
550	Resnet101-FPN	SL1	Original NMS	11.9	32.9	34.8	45.8	29.9	31.4	42.1
550	Resnet101-FPN	SL1	Cluster-NMS	29.2	32.9	34.8	45.9	29.9	31.4	42.1
550	Resnet101-FPN	SL1	SPM Cluster-NMS	28.8	33.5	35.9	49.7	30.5	32.1	44.1
550	Resnet101-FPN	SL1	SPM + Distance Cluster-NMS	27.5	33.5	35.9	50.2	30.4	32.0	44.3
550	Resnet101-FPN	SL1	SPM + Distance + Weighted Cluster-NMS	26.7	34.0	36.6	49.9	30.5	32.0	44.3

The following table is evaluated by using their pretrained weight of YOLACT++. (yolact_plus_base_54_800000.pth)

Image Size	Backbone	Loss	NMS	FPS	box AP	box AP75	box AR100	mask AP	mask AP75	mask AR100
550	Resnet101-FPN	SL1	Fast NMS	25.1	35.8	38.7	45.5	34.4	36.8	42.6
550	Resnet101-FPN	SL1	Original NMS	10.9	36.4	39.1	48.0	34.7	37.1	44.1
550	Resnet101-FPN	SL1	Cluster-NMS	23.7	36.4	39.1	48.0	34.7	37.1	44.1
550	Resnet101-FPN	SL1	SPM Cluster-NMS	23.2	36.9	40.1	52.8	35.0	37.5	46.3
550	Resnet101-FPN	SL1	SPM + Distance Cluster-NMS	22.0	36.9	40.2	53.0	34.9	37.5	46.3
550	Resnet101-FPN	SL1	SPM + Distance + Weighted Cluster-NMS	21.7	37.4	40.6	52.5	35.0	37.6	46.3

Note:

• Things we did but did not appear in the paper: SPM + Distance + Weighted Cluster-NMS. Here the box coordinate weighted average is only performed in IoU> 0.8. We searched that IoU>0.5 is not good for YOLACT and IoU>0.9 is almost same to SPM + Distance Cluster-NMS. (Refer to CAD for the details of Weighted-NMS.)

• The Original NMS implemented by YOLACT is faster than ours, because they firstly use a score threshold (0.05) to get the set of candidate boxes, then do NMS will be faster (taking YOLACT ResNet101-FPN as example, 22 ~ 23 FPS with a slight performance drop). In order to get the same result with our Cluster-NMS, we modify the process of Original NMS.

• Note that Torchvision NMS has the fastest speed, that is owing to CUDA implementation and engineering accelerations (like upper triangular IoU matrix only). However, our Cluster-NMS requires less iterations for NMS and can also be further accelerated by adopting engineering tricks.

• Currently, Torchvision NMS use IoU as criterion, not DIoU. However, if we directly replace IoU with DIoU in Original NMS, it will costs much more time due to the sequence operation. Now, Cluster-DIoU-NMS will significantly speed up DIoU-NMS and obtain exactly the same result.

• Torchvision NMS is a function in Torchvision>=0.3, and our Cluster-NMS can be applied to any projects that use low version of Torchvision and other deep learning frameworks as long as it can do matrix operations. No other import, no need to compile, less iteration, fully GPU-accelerated and better performance.

Images

# Display qualitative results on the specified image.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --ima


ge=my_image.png

# Process an image and save it to another file.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png

# Process a whole folder of images.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder

Video

# Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance.
# If you want, use "--display_fps" to draw the FPS directly on the frame.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4

# Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0

# Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast!
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4

As you can tell, eval.py can do a ton of stuff. Run the --help command to see everything it can do.

python eval.py --help

Training

By default, we train on COCO. Make sure to download the entire dataset using the commands above.

• To train, grab an imagenet-pretrained model and put it in ./weights.
  • For Resnet101, download resnet101_reducedfc.pth from here.
  • For Resnet50, download resnet50-19c8e357.pth from here.
  • For Darknet53, download darknet53.pth from here.
• Run one of the training commands below.
  • Note that you can press ctrl+c while training and it will save an *_interrupt.pth file at the current iteration.
  • All weights are saved in the ./weights directory by default with the file name <config>_<epoch>_<iter>.pth.

# Trains using the base config with a batch size of 8 (the default).
python train.py --config=yolact_base_config

# Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.
python train.py --config=yolact_base_config --batch_size=5

# Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name.
python train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1

# Use the help option to see a description of all available command line arguments
python train.py --help

Multi-GPU Support

YOLACT now supports multiple GPUs seamlessly during training:

• Before running any of the scripts, run: export CUDA_VISIBLE_DEVICES=[gpus]
  • Where you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).
  • You should still do this if only using 1 GPU.
  • You can check the indices of your GPUs with nvidia-smi.
• Then, simply set the batch size to 8*num_gpus with the training commands above. The training script will automatically scale the hyperparameters to the right values.
  • If you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you're using.
  • If you want to allocate the images per GPU specific for different GPUs, you can use --batch_alloc=[alloc] where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to batch_size.

Acknowledgments

Thank you to Daniel Bolya for his fork of YOLACT & YOLACT++, which is an exellent work for real-time instance segmentation.