Resources for Multiple Object Tracking (MOT)

The official Roboflow Python package. Manage your datasets, models, and deployments. Roboflow has everything you need to build a computer vision application.

Ultralytics YOLO11 🚀

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

Interactive Widgets for the Jupyter Notebook

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

Unofficial implementation of PatchCore anomaly detection

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

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Korean GPT-2 pretrained cased (KoGPT2)

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

The fastai deep learning library

Code release for "Cut and Learn for Unsupervised Object Detection and Instance Segmentation" and "VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation"

Reference models and tools for Cloud TPUs.

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

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

High-Resolution Image Synthesis with Latent Diffusion Models

[TIP 2022] CBNetV2: A Composite Backbone Network Architecture for Object Detection

Composite Backbone Network (AAAI20)

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

Painter & SegGPT Series: Vision Foundation Models from BAAI

my useful torch lightning training template

95.47% on CIFAR10 with PyTorch

mixup: Beyond Empirical Risk Minimization

Pretty Tensor: Fluent Networks in TensorFlow

Graph prediction using diverse networks in the OGB(Open Graph Benchmark)'s HIV dataset. The HIV dataset tested the ability to inhibit HIV replication for over 40 000(classication task between inact…

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time