B.3: YOLO: image detection architectures: Difference between revisions
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• '''Reference, URL''': https://pytorch.org/hub/datvuthanh_hybridnets/ | • '''Reference, URL''': https://pytorch.org/hub/datvuthanh_hybridnets/ | ||
• ''' | • ''''Applicable Business Category''': Manufacturing (WEEE & Battery) | ||
• '''Application in relevant Projects/Initiatives''': N.A. | • '''Application in relevant Projects/Initiatives''': N.A. | ||
• '''Type''': ML Model | • '''Asse Type''': ML Model | ||
• '''AI Breadth''': Deep Learning, Computer Vision | • '''AI Breadth''': Deep Learning, Computer Vision | ||
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• '''Related technologies''': Pytorch | • '''Related technologies''': Pytorch | ||
• '''Applicable Research Area''': Physical AI | |||
• '''Applicable Technical Category''': Computer Vision | |||
• '''License Information''': MIT license (MIT) | • '''License Information''': MIT license (MIT) | ||
Latest revision as of 09:31, 27 December 2024
• Short Description: YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
• Reference, URL: https://pytorch.org/hub/datvuthanh_hybridnets/
• 'Applicable Business Category: Manufacturing (WEEE & Battery)
• Application in relevant Projects/Initiatives: N.A.
• Asse Type: ML Model
• AI Breadth: Deep Learning, Computer Vision
• Learning Ability: Supervised Learning
• Related technologies: Pytorch
• Applicable Research Area: Physical AI
• Applicable Technical Category: Computer Vision
• License Information: MIT license (MIT)
• Related to circularity and sustainability: No
• Audience: Manufacturers
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