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Interact with your documents using the power of GPT, 100% privately, no data leaks
The world's simplest facial recognition api for Python and the command line
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
A collection of design patterns/idioms in Python
Making large AI models cheaper, faster and more accessible
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
DALL·E Mini - Generate images from a text prompt
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
End-to-End Object Detection with Transformers
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Geometric Computer Vision Library for Spatial AI
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
Code for the paper Hybrid Spectrogram and Waveform Source Separation
text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
High accuracy RAG for answering questions from scientific documents with citations
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
Google Drive Public File Downloader when Curl/Wget Fails