review_object_detection_metrics
chitra
review_object_detection_metrics | chitra | |
---|---|---|
2 | 1 | |
1,007 | 223 | |
- | 0.4% | |
0.0 | 3.2 | |
4 months ago | 29 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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)?
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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!
chitra
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Answer: Resizing image and its bounding box
Another way of doing this is to use CHITRA
What are some alternatives?
globox - A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
tf-keras-vis - Neural network visualization toolkit for tf.keras
imageset-viewer - Pascal VOC BBox Viewer
img2dataset - Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
examples - Learn to create a desktop app with Python and Qt
gallery - BentoML Example Projects 🎨
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
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.
pytest-visual - A visual testing framework for ML with automated change detection
pytorch-toolbelt - PyTorch extensions for fast R&D prototyping and Kaggle farming
Text2Poster-ICASSP-22 - Official implementation of the ICASSP-2022 paper "Text2Poster: Laying Out Stylized Texts on Retrieved Images"