coco-viewer
imageset-viewer
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| coco-viewer | imageset-viewer | |
|---|---|---|
| 1 | 1 | |
| 159 | 63 | |
| - | - | |
| 0.0 | 4.4 | |
| 6 days ago | 10 months ago | |
| Python | Python | |
| MIT License | MIT License |
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coco-viewer
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Any GUI to load labels and the images to verify if the bounding boxes are right?
If your dataset is in a coco format, I had a lot of success with this : https://github.com/trsvchn/coco-viewer
imageset-viewer
What are some alternatives?
cocojson - Utility scripts for COCO json annotation format
review_object_detection_metrics - Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
chitra - A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
labelCloud - A lightweight tool for labeling 3D bounding boxes in point clouds.
globox - A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
pycococreator - Helper functions to create COCO datasets
bbox-visualizer - Make drawing and labeling bounding boxes easy as cake
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots