#YOLOv3

This project delivers an implementation of YOLOv3 using TensorFlow 2.0, ensuring compatibility and improvements over older versions. It facilitates rapid deployment with pre-trained models, supports training with custom datasets, and offers starting options with COCO weights. The well-documented scripts and guides make it accessible for both hobbyists and professionals interested in exploring YOLOv3's potential in TensorFlow.

YOLOv3, an open-source AI by Ultralytics, excels in object detection, segmentation, and classification. It focuses on speed, accuracy, and ease of use, integrating strategies from extensive R&D. New users can explore detailed guides, join a strong community, and leverage enhanced AI platform integrations, benefiting diverse global developers.

The project offers accessible implementations of diverse multi-object tracking algorithms in Python, including tools like CentroidTracker, IOUTracker, CentroidKF_Tracker, and SORT. It integrates with OpenCV-based detectors such as TF_SSDMobileNetV2, Caffe_SSDMobileNet, and YOLOv3. Installation is straightforward using pip or GitHub, with additional guidance provided for GPU use via CUDA-enabled OpenCV. The repository includes practical examples and pre-trained model weights for broader application.

This project delivers 3D bounding box estimation using a PyTorch implementation that incorporates neural networks for precise orientation and dimensional prediction. Utilizing YOLOv3 for 2D detection, it calculates and overlays 3D locations on images. The project demands PyTorch, Cuda, and OpenCV, and provides pre-trained weights for quick setup. It allows batch processing of images and videos, demonstrating detection capabilities. Based on the Kitti dataset, it asserts strong performance. Future objectives include developing a customized YOLO network and implementing pose visualization, enhancing contributions to geometric conversion.