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Haojun Yu's Projects

auto-gpt icon auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

autogen icon autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

awesome-diffusion-models icon awesome-diffusion-models

A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models

bert icon bert

TensorFlow code and pre-trained models for BERT

best-of-ml-python icon best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

bytetrack icon bytetrack

ByteTrack: multi-object tracking by Associating Every Detection Box

cascade-rcnn icon cascade-rcnn

Caffe implementation of multiple popular object detection frameworks

clip icon clip

Contrastive Language-Image Pretraining

cogqa icon cogqa

Source code and dataset for ACL 2019 paper "Cognitive Graph for Multi-Hop Reading Comprehension at Scale"

dalle-mini icon dalle-mini

DALL·E Mini - Generate images from a text prompt

dalle-pytorch icon dalle-pytorch

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

dalle2-pytorch icon dalle2-pytorch

Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch

defcn icon defcn

End-to-End Object Detection with Fully Convolutional Network

deformable-detr icon deformable-detr

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

detectron icon detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

detectron2 icon detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

diffusers icon diffusers

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

dino icon dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

fairmot icon fairmot

[IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking

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