| Name | Version | Summary | date |
| ultralytics |
8.2.8 |
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-05-03 21:07:36 |
| tensorrt-yolo |
3.0.1 |
TensorRT-YOLO: Support YOLOv5, YOLOv8, YOLOv9, PP-YOLOE using TensorRT acceleration with EfficientNMS! |
2024-04-23 08:34:08 |
| phenocv |
0.1.4 |
Rice High Throughput Phenotyping Computer Vision Toolkit |
2024-04-23 05:27:13 |
| ultralytics-nogui |
8.2.2 |
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-04-22 20:51:35 |
| syml-ultralytics |
8.2.2 |
SyML Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-04-22 13:18:13 |
| open-metric-learning |
2.1.9 |
OML is a PyTorch-based framework to train and validate the models producing high-quality embeddings. |
2024-04-22 12:15:35 |
| yolov8-pose-triton |
8.2.0 |
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-04-18 20:35:05 |
| wpodnet-pytorch |
1.0.3 |
The implementation of ECCV 2018 paper "License Plate Detection and Recognition in Unconstrained Scenarios" in PyTorch |
2024-04-10 09:38:47 |
| hub-sdk |
0.0.8 |
Ultralytics HUB Client SDK. |
2024-04-09 10:22:46 |