This repository serves as a comprehensive model zoo, offering a vast collection of pre-trained models converted and optimized for various deep learning frameworks and hardware accelerators. It aims to simplify the deployment of state-of-the-art models across diverse platforms, including TensorFlow, PyTorch, ONNX, OpenVINO, TensorFlow Lite, EdgeTPU, and CoreML, catering to researchers and developers working on edge devices and performance-critical applications.

How It Works

The core of this repository lies in its extensive collection of conversion scripts and pre-converted models. It leverages a wide array of techniques, including quantization (weight, dynamic range, full integer, float16) and framework-specific optimizations (e.g., TF-TRT, OpenVINO IR), to make models compatible and efficient across different deployment targets. The project supports a broad spectrum of model architectures and tasks, from image classification and object detection to pose estimation and semantic segmentation.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and following specific setup instructions for desired models and frameworks. Many examples utilize Python with TensorFlow and its ecosystem.
• Prerequisites: Python 3.x, TensorFlow (versions vary by example, often 1.x or 2.x), and potentially other libraries like PyTorch, ONNX Runtime, OpenVINO, or specific hardware SDKs (e.g., EdgeTPU). CUDA is often required for GPU acceleration.
• Setup Time: Varies significantly based on the chosen model and framework, ranging from minutes for simple conversions to hours or days for complex training or dataset preparation.
• Resources: Requires sufficient disk space for models and datasets, and potentially significant GPU resources for training or fine-tuning.
• Links:
  • TensorFlow Models Repository (for DeepLab examples)
  • Official TensorFlow Lite Documentation

Highlighted Details

• Extensive Model Coverage: Supports a wide range of computer vision tasks and architectures.
• Multi-Framework Compatibility: Facilitates cross-platform deployment by converting models between TensorFlow, PyTorch, ONNX, and more.
• Quantization Support: Offers various quantization methods for model compression and acceleration, crucial for edge devices.
• Hardware Acceleration: Includes optimizations for specific hardware like EdgeTPU and OpenVINO.

Maintenance & Community

The repository is actively maintained by PINTO0309, with contributions from the community. It appears to be a personal project driven by hobbyist efforts, with a focus on practical model conversion and optimization.

Licensing & Compatibility

• License: Model conversion scripts are MIT licensed. However, the underlying models inherit the licenses of their original providers, which may have restrictions on commercial use or redistribution. Users must check the license for each specific model.
• Commercial Use: Compatibility depends on the original model licenses.

Limitations & Caveats

The repository is a hobby project, and while extensive, it may not cover all edge cases or the latest model architectures. Some conversion scripts might be experimental or require specific environment setups. Users should be prepared to troubleshoot environment-specific issues and verify model performance and licensing.