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Image Classification Computer Vision with Hugging Face Transformers -Google ViT - Python ML Tutorial
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In this Python tutorial, You'll learn how to use the very latest Hugging Face model (on Model Hub)- Computer Vision Vision Transfomers (ViT Model from Google) to do Image Classification using Hugging Face Transformers (all in Transformers api). Hugging Face is quite popular for NLP but this time it's about Computer Vision and Image Classification.
This video introduces the latest ViT Model from Hugging Face Model Hub
Demonstrates ViT Image Classification with Hosted Inference API
Google Colab Python Code Walk through for Image Classification with Hugging Face Transformers
With Computer Vision from Hugging Face Transformers, I hope more things
to come!
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
This video introduces the latest ViT Model from Hugging Face Model Hub
Demonstrates ViT Image Classification with Hosted Inference API
Google Colab Python Code Walk through for Image Classification with Hugging Face Transformers
With Computer Vision from Hugging Face Transformers, I hope more things
to come!
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
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