This repository offers a comprehensive research platform for YOLO-based object detection, segmentation, classification, and pose estimation, targeting researchers and developers seeking to experiment with and customize state-of-the-art models. It integrates YOLOv5, YOLOv7, and YOLOv8, incorporating advanced architectures like Swin Transformer V2 and attention mechanisms, with a focus on practical training skills and deployment.

How It Works

The project consolidates various YOLO architectures (v5, v7, v8) into a unified codebase, allowing for flexible model customization via YAML configurations. It integrates advanced components like Swin Transformer V2 and attention modules (e.g., GAM) to enhance model performance. The architecture supports multi-GPU training using DistributedDataParallel for improved efficiency and offers a modular structure for easy experimentation with different detection, segmentation, classification, and keypoint detection tasks.

Quick Start & Requirements

• Install dependencies via pip install -r requirements.txt.
• For YOLOv8, pip install ultralytics is recommended, allowing direct use of official YOLOv8 commands.
• Requires Python and PyTorch. GPU acceleration is highly recommended for training.
• Official YOLOv5, YOLOv7, and YOLOv8 documentation can be referenced for specific usage patterns.

Highlighted Details

• Integrates YOLOv5, YOLOv7, and YOLOv8 cores with support for detection, segmentation, classification, and keypoint detection.
• Incorporates advanced architectural elements like Swin Transformer V2 and various attention mechanisms.
• Includes an auto-labeling tool "Prompt-Can-Anything" for batch annotation.
• Provides detailed historical updates and experimental results, including comparisons of attention mechanisms and model variants.

Maintenance & Community

The project is actively maintained by the author, with frequent updates and bug fixes noted in the changelog. The author encourages feedback and issue reporting. Links to CSDN blogs are provided for detailed explanations and updates.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification on the specific license terms.

Limitations & Caveats

The README mentions that some features, particularly custom network structures with official weights, may require temporary workarounds like installing ultralytics or manual weight name remapping. Deepstream deployment is noted as being based on older versions (5.1) and requires Linux. The project is described as a research platform, implying potential for ongoing development and changes.