review_object_detection_metrics
imageset-viewer
Our great sponsors
| review_object_detection_metrics | imageset-viewer | |
|---|---|---|
| 2 | 1 | |
| 1,007 | 64 | |
| - | - | |
| 0.0 | 4.4 | |
| 4 months ago | 10 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
review_object_detection_metrics
- How to run PyQt5 applications on Ubuntu (WSLg)?
-
Links to papers or books that discuss model evaluation methods for object detection models
This is a good repository for you to start. https://github.com/rafaelpadilla/review_object_detection_metrics Ultimately you would want to compute the precision, recall, average precision, average recall, and mean Average Precision (mAP) that you’ve probably seen in many papers. Good luck!
imageset-viewer
What are some alternatives?
chitra - A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
globox - A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter
examples - Learn to create a desktop app with Python and Qt
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
bbox-visualizer - Make drawing and labeling bounding boxes easy as cake
YOLO-Coco-Dataset-Custom-Classes-Extractor - Get specific classes from the Coco Dataset with annotations for the Yolo Object Detection model for building custom object detection models.