Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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Datasets, Transforms and Models specific to Computer Vision
An open source implementation of CLIP.
Low-code framework for building custom LLMs, neural networks, and other AI models
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
A python library for self-supervised learning and latent world models on images.
OpenMMLab Foundational Library for Training Deep Learning Models
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
Upscale an image by a factor of 4, while generating photo-realistic details.
Build fully-functioning computer vision models with PyTorch
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
A Tensorfflow implementation of Attend, Infer, Repeat
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources