Recipes for shrinking, optimizing, customizing cutting edge vision models. 💜

An open-source implementaion for fine-tuning Qwen2-VL series by Alibaba Cloud.

Making LLaVA Tiny via MoE-Knowledge Distillation

Official Pytorch Implementation of Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

LLM101n: Let's build a Storyteller

AI Toolkit for Healthcare Imaging

Dataset for Detection of White Blood Cells of the Juvenile Visayan Warty Pig in Blood Smear Images

Collection of awesome medical dataset resources.

Blood Cell Foundation Model based on DINOv2

Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.

We present a comprehensive and deep review of the HFM in challenges, opportunities, and future directions. The released paper: https://arxiv.org/abs/2404.03264

[NeurIPS 2024 Oral][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-sim…

Machine learning driven bibliography parser for finding the source for things you know.

Code for https://arxiv.org/abs/2305.19643

PyTorch code and models for the DINOv2 self-supervised learning method.

A Survey on CLIP in Medical Imaging

PyTorch implementation of Semi-supervised Vision Transformers

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

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

Official Code for Stable Cascade

Image Generator for Tabular Data (IGTD): Converting Tabular Data to Images for Deep Learning Using Convolutional Neural Networks

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

A general fine-tuning kit geared toward diffusion models.

Deep learning-based outlier/anomaly detection

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.