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Tag: Machine learning
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The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  • pytorch
  • resnet
  • pretrained-models
  • pretrained-weights

Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

  • codebook
  • codeformer
  • face-enhancement
  • face-restoration

Official code for "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

  • gpt-2
  • adaptation
  • language-model
  • gpt-3

TranslatedAI-based tool for removing hard-coded subtitles and text-like watermarks from videos or Pictures. It removes hard-coded subtitles and text watermarks from images/videos and generates subtitle-free, watermark-free image/video files at lossless resolution. No third-party API application is needed; it runs locally.

  • ai
  • deepleanring
  • vsr
  • sub-remove

This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.