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AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
The PASS dataset: pretrained models and how to get the data
基于 PatchCore 的工业异常检测项目，研究 layer2/layer3 分数融合、敏感性引导收缩与风险受控门控，并提供 Streamlit 工程化 Demo。
Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark
Personal project: where shoppers look, estimated from an ordinary security camera and open source AI (YOLO11, Gaze-LLE, Depth Anything V2)
PyTorch Connectomics: segmentation toolbox for EM connectomics
Automated Testing for Set-Top Boxes and Smart TVs
A simple, flexible, and extensible template for PyTorch. It's beautiful.
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Agent skill: outline, mask and count objects in photos with Meta's SAM 3 on your own NVIDIA GPU, with review, exports and a viewer. Works with Claude Code, Codex, Cursor, Pi and other Agent Skills clients.
A Tensorfflow implementation of Attend, Infer, Repeat
An Easy To Use PyTorch Computer Vision Library
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources