review_object_detection_metrics
yolo-tf2
| review_object_detection_metrics | yolo-tf2 | |
|---|---|---|
| 2 | 1 | |
| 1,007 | 747 | |
| - | - | |
| 0.0 | 7.6 | |
| 4 months ago | almost 2 years 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)?
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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!
yolo-tf2
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How to write a resume for python / ML jobs?
my most useful project is yolo object detector implementation in tf2 and I'm currently working on 2 other projects, one of which is the implementation of various drl algorithms in tf and the other project will be based on the latter and it's concerned with trading. The rest are more of scripts rather than projects ex: web scraping, file management, programming challenges ...
What are some alternatives?
chitra - A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
globox - A package to read and convert object detection datasets (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
imageset-viewer - Pascal VOC BBox Viewer
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3 - This is a python program using YOLO and OpenCV to detect traffic lights. Works in The Netherlands, possibly other countries
examples - Learn to create a desktop app with Python and Qt
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
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.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
onnx-tensorflow - Tensorflow Backend for ONNX