VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Self configuring and adapting vision transformer for segmentation of 3d images

repo for practicing DL/genAI

Pre-training a VisionTransformer with Masked Image Modelling for semantic segmentation

A curated list of foundation models for vision and language tasks

MLX-VLM is a package for running Vision LLMs locally on your Mac using MLX.

Scenic: A Jax Library for Computer Vision Research and Beyond

The official repo for [IJCAI'24] "LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation"

Investigate possibilities for Vision Transformers with multiscale grids

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

[Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)

This is a series of computer vision foundational projects for anyone diving into the field must tackle.

[ICLR 2024 Oral] Less is More: Fewer Interpretable Region via Submodular Subset Selection

The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍

Pytorch implementation of the models RT-1-X and RT-2-X from the paper: "Open X-Embodiment: Robotic Learning Datasets and RT-X Models"

Video Foundation Models & Data for Multimodal Understanding

EfficientViT is a new family of vision models for efficient high-resolution vision.

OpenMMLab Detection Toolbox and Benchmark

Optimized local inference for LLMs with HuggingFace-like APIs for quantization, vision/language models, multimodal agents, speech, vector DB, and RAG.

A tool for classifying an image into a disaster type, utilizing Python