PINTO_model_zoo: a collection of pre-converted and pre-quantized models for cross-framework deployment on edge devices

What it solves

PINTO_model_zoo provides a centralized collection of pre-converted and pre-quantized AI models. It solves the difficulty of manually converting models between different deep learning frameworks and optimizing them for edge devices (like Raspberry Pi or EdgeTPU) by providing ready-to-use versions of popular models in multiple formats.

How it works

The project maintains a "zoo" of models that have been inter-converted across various frameworks. It supports a wide range of formats including TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlow Lite (Float32, Float16, and INT8), EdgeTPU, and CoreML. The repository includes models for image classification, 2D object detection, and other vision tasks, often providing multiple quantization levels (Weight, Integer, Full Integer, and Dynamic Range) to balance performance and accuracy.

Who it’s for

Developers and researchers working on edge AI and mobile deployment who need optimized models in specific formats without having to perform the complex conversion and quantization process themselves.

Highlights

• Broad Framework Support: Supports almost all major AI frameworks and deployment formats (TFLite, ONNX, CoreML, OpenVINO, etc.).
• Extensive Model Library: Includes a vast array of pre-quantized models for image classification and object detection.
• Edge-Optimized: Specifically targets performance on hardware like Raspberry Pi 4/3 and EdgeTPU.
• Quantization Variety: Offers models in FP32, FP16, INT8, and Dynamic Range quantization to suit different hardware constraints.