labelme2coco
How to create custom COCO data set for instance segmentation (by Tony607)
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. (by rafaelpadilla)
| labelme2coco | review_object_detection_metrics | |
|---|---|---|
| 1 | 2 | |
| 175 | 1,015 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 5 months ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
labelme2coco
Posts with mentions or reviews of labelme2coco.
We have used some of these posts to build our list of alternatives
and similar projects.
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Trying to run python script in terminal but getting ImportError for module that is definitely installed.
I'm working on a computer vision image segmentation project and I used LabelMe to annotate some data and am using this script to convert them to coco format.
review_object_detection_metrics
Posts with mentions or reviews of review_object_detection_metrics.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-15.
- How to run PyQt5 applications on Ubuntu (WSLg)?
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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!
What are some alternatives?
When comparing labelme2coco and review_object_detection_metrics you can also consider the following projects:
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.
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.
imageset-viewer - Pascal VOC BBox Viewer
examples - Learn to create a desktop app with Python and Qt
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
labelme2coco vs YOLO-Coco-Dataset-Custom-Classes-Extractor
review_object_detection_metrics vs chitra
review_object_detection_metrics vs globox
review_object_detection_metrics vs imageset-viewer
review_object_detection_metrics vs examples
review_object_detection_metrics vs yolo-tf2
review_object_detection_metrics vs YOLO-Coco-Dataset-Custom-Classes-Extractor