PINTO0309/PINTO_model_zoo

PINTO_model_zoo – A Community‑Curated Model Hub

What it is

• A public GitHub repository that stores a large collection of pre‑converted deep‑learning models. The models are provided in many deployment formats – TensorFlow, TensorFlow Lite, PyTorch, ONNX, OpenVINO, CoreML, TensorFlow.js, TensorFlow‑TRT, EdgeTPU, etc. – so developers can pick the one that matches their target hardware or framework.

Why it matters

• Moving a model from the framework it was trained in (e.g., PyTorch) to the format required for an edge device (e.g., TensorFlow Lite INT8 for a Coral Edge TPU) normally involves a chain of conversion steps and fiddly quantisation settings. This repo does the heavy lifting by already providing the converted artefacts, together with the original licence information.
• It also documents which quantisation schemes are available for each model (weight‑only, dynamic‑range, full‑integer, float‑16, Edge‑TPU) and which inference engines can consume them.

What you’ll find

• A hierarchical folder layout – each model lives in its own sub‑directory (e.g., 004_efficientnet, 018_EfficientDet). Inside you’ll see the various files (.tflite, .onnx, .xml/.bin, .mlmodel, etc.) and a LICENSE file that mirrors the original model’s licence.
• A giant table in the README that lists every model, its type (image classification, 2‑D object detection, etc.), and which formats are available (FP32, FP16, INT8, etc.). Icons (⚫) indicate presence.
• Links to the author’s articles and tools – the README points to blog posts on quantisation techniques, conversion scripts (openvino2tensorflow, tflite2tensorflow, onnx2tf, etc.), and small utilities for ONNX manipulation (size‑shrink, node deletion, model merging).

Typical workflow for a user

1. Pick a model – e.g., you need a lightweight face‑recognition network for a Raspberry Pi. Find 194_face_recognizer_fast in the table.
2. Choose the format – if you run TensorFlow Lite on the Pi, download the *.tflite file (FP16 or INT8 if you need speed).
3. Check the licence – open the LICENSE file in that folder to ensure the original model’s terms are compatible with your project.
4. Run inference – the repo does not ship inference code, but you can use the standard TensorFlow Lite interpreter or OpenVINO runtime, as appropriate.

Limitations

• The repository does not include sample inference scripts; the author explicitly invites contributions for that.
• Some models (e.g., MobileNetV3 backbone, full‑integer quantisation) are noted as not returning correct results as of early 2020.
• Quantisation quality can vary; you may need to fine‑tune or re‑quantise for your specific hardware.

Who maintains it

• The primary maintainer is the GitHub user PINTO0309. Contributions are welcomed, and a contributors badge shows community activity.

Bottom line

• PINTO_model_zoo is a genuine, practical resource for engineers who need ready‑to‑deploy deep‑learning models across a wide range of edge and cloud runtimes. It saves time on format conversion and quantisation, letting you focus on building the surrounding application.