CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Google Brain AutoML

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Acceptance rates for the major AI conferences

Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

Simple image captioning model

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

Simple implementation of OpenAI CLIP model in PyTorch.

Official ImageNet Model repository

Official pytorch implementation of paper "Dual-Level Collaborative Transformer for Image Captioning" (AAAI 2021).

[NeurIPS'22] Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

A multi-modal CLIP model trained on the medical dataset ROCO

[ECCV 2020] Official code for "Comprehensive Image Captioning via Scene Graph Decomposition"

IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition

Implementation of 'End-to-End Transformer Based Model for Image Captioning' [AAAI 2022]

The official implementation of "Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification"

Notebook for BERT medical named entity recognition

Multi-label, multi-class classification of chest X-ray images using PyTorch

Source code for the paper "A Medical Semantic-Assisted Transformer for Radiographic Report Generation"

ChestXRay Disease Diagnosis using CheXpert dataset

Implementation of some traditional methods in PR.