Where can I find open source pre-trained deep learning models(source code optional)?
Discover open-source deep learning code and pre-trained models.
Model Zoo curates and provides a platform for deep learning researchers to easily find pre-trained models for a variety of platforms and uses. We regularly update the site and provide filtering functionality for users to find models that they need, either for educational purposes, transfer learning, or other uses.
For more pre-trained models check the
Official pre-trained models thread thread on each competition discussion form.
This directory contains code for training and evaluating several widely used Convolutional Neural Network (CNN) image classification models using tf_slim. It contains scripts that allow you to train models from scratch or fine-tune them from pre-trained network weights. It also contains code for downloading standard image datasets, converting them to TensorFlow's native TFRecord format and reading them in using TF-Slim's data reading and queueing utilities. You can easily train any model on any of these datasets, as we demonstrate below. We've also included a jupyter notebook, which provides working examples of how to use TF-Slim for image classification. For developing or modifying your own models, see also the main TF-Slim page.
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools.
The ONNX Model Zoo is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members like you. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture.