Vision
pytorch
Datasets, Transforms and Models specific to Computer Vision
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Results: 18
pytorch
Datasets, Transforms and Models specific to Computer Vision
microsoft
Best Practices, code samples, and documentation for Computer Vision.
huggingface
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and traini…
kyegomez
Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT a…
jacobgil
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
roboflow
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge model…
kmario23
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
dusty-nv
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
labmlai
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (ad…
SwanHubX
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl /…
NirantK
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
OlafenwaMoses
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
kyegomez
Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch
Owner-curated external sources. Not filtered by the scores or compatibility controls above; excluded from GitHub rankings and automatic installation.
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