(Refer to CAD for the details of Weighted-NMS.) It just use score vector for weighted coordinates, not combine score and IoU. Merge NMS is a simplified version of Weighted-NMS. Intel(R) Core(TM) i7-6850K CPU 3.60GHzĮvaluation command: python3 test.py -cfg yolov3-spp.cfg -weights yolov3-spp-ultralytics.ptĪP reports on coco 2014 minival.Docker requirements are:ĬOCO run at -iou-thr 0.5, run at -iou-thr 0.7 Docker images come with all dependencies preinstalled.
#NMS CADMIUM INSTALL#
Python 3.7 or later with all pip install -U -r requirements.txt packages including torch >= 1.5. Training is done on the COCO dataset by default.
#NMS CADMIUM CODE#
The code works on Linux, MacOS and Windows. The repo contains inference and training code for YOLOv3 in PyTorch.
#NMS CADMIUM SOFTWARE#
This directory contains PyTorch YOLOv3 software developed by Ultralytics LLC, and is freely available for redistribution under the GPL-3.0 license. See non_max_suppression function of utils/utils.py for our Cluster-NMS implementation. This repo only focuses on NMS improvement based on. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT), and object detection (e.g., YOLO v3, SSD and Faster R-CNN) models. Cluster-NMS is very efficient due to its pure GPU implementation, and geometric factors can be incorporated to improve both AP and AR. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires less iterations. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted Ln-norm loss and IoU-based loss. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases.
In particular, we consider three geometric factors, i.e., overlap area, normalized central point distance and aspect ratio, which are crucial for measuring bounding box regression in object detection and instance segmentation. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
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