Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

[arXiv] The official code for "UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation".

Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™

📚 A collection of papers about Referring Image Segmentation.

The collection of pre-trained, state-of-the-art AI models for ailia SDK

Annotate-lab is an open-source image annotation tool for efficient dataset creation. With an intuitive interface and flexible export options, it streamlines your machine learning workflow. 🖼️✏️📑

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

Segmentation Tools for Remotely Sensed RPAS Imagery

Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)

u-net model for mri image segmentation

The project involves using a dataset of 100 high-resolution retinal fundus images for blood vessel segmentation to aid in early detection of retinal pathologies | Implemented U-Net architecture from scratch, known for its efficiency in semantic segmentation with limited data | The final model achieved 86% IoU score | PyTorch Lightning

Valor is a centralized evaluation store which makes it easy to measure, explore, and rank model performance.

《基于多特征融合的稻叶病图像识别系统》 基于机器学习和深度学习算法开发了一个稻叶病图像识别系统，基于颜色特征分割提取稻叶病图像病斑，提取颜色、形状、纹理等多个病斑图像特征维度，基于MCCA多视图典型相关分析进行特征融合得到集成特征，基于CNN、SVM进行稻叶病图像分类，进行了多个分类器对比实验，提高了稻叶病识别准确率和速度；校重点大学生科创基金项目，项目获第十一届“挑战杯”校大学生课外学术科技作品竞赛三等奖。

Open source Python library for building bioimage analysis pipelines

MIST: A simple, scalable, and end-to-end framework for 3D medical imaging segmentation.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Tree detection from aerial imagery in Python

Major Research Paper for M.Sc. in Data Science at TMU

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.

PyTorch extensions for fast R&D prototyping and Kaggle farming