Transfer Learning
Showing 21 of 21 projects
A comprehensive collection of PyTorch image models, layers, utilities, and training scripts for computer vision research and applications.
A PyTorch library providing datasets, model architectures, and image transformations for computer vision tasks.
A collection of pre-trained machine learning models ported to TensorFlow.js for use directly in the browser or Node.js.
A curated collection of papers, code, and resources for domain adaptation in machine learning.
A hands-on beginner's guide to machine learning and image classification using Caffe and DIGITS with neural networks.
Official repository for Big Transfer (BiT) models, providing pre-trained visual representations for efficient transfer learning across computer vision tasks.
An open-source library providing chest X-ray datasets, pre-trained models, and tools for medical imaging research and analysis.
High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.
A transfer learning-based evaluation metric for Natural Language Generation that scores text fluency and meaning.
Pre-trained biomedical language representation model for biomedical text mining tasks like named entity recognition and relation extraction.
Build fully-functioning computer vision and object detection models with PyTorch in just 5 lines of code.
A BERT model pre-trained on PubMed abstracts and clinical notes for biomedical natural language processing tasks.
A deep learning system that classifies food images into 230 categories and retrieves matching recipes using convolutional neural networks.
A PyTorch implementation of TResNet, a high-performance convolutional neural network architecture optimized for GPU training and inference.
A reinforcement learning framework for de novo drug design that generates novel molecular structures with desired properties.
TensorFlow implementation of weakly-supervised object localization using only image-level labels, without bounding box annotations.
A Python library that simplifies using, finetuning, and deploying state-of-the-art machine learning models for various AI tasks.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A deep learning model for classifying image aesthetic quality using Inception modules and fine-tuned connected layers.
A Python package providing popular computer vision model architectures built with Equinox for JAX.
A collection of Google Colab tutorials teaching biologists how to apply deep learning with Keras to real-world biological and agricultural problems.
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