STEM-AI-mtl
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README.md
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datasets:
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- STEM-AI-mtl/City_map
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widget:
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- image: https://cdn.britannica.com/50/69550-050-B9DA3DCA/Central-New-York-City-borough-Manhattan-Park.jpg
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output:
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text: NYC
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metrics:
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- accuracy
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---
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# The fine-tuned ViT model that beats [Google's state-of-the-art model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's famous GPT4
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### How to use:
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[Inference script](https://github.com/STEM-ai/Vision/
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
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## Training evaluation results
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The most accurate output model was obtained from a learning rate of 1e-3. The quality of the training was evaluated with the training dataset and resulted in the following metrics
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{'eval_loss': 1.3691096305847168,\
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'eval_accuracy': 0.6666666666666666,\
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datasets:
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- STEM-AI-mtl/City_map
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---
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# The fine-tuned ViT model that beats [Google's state-of-the-art model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's famous GPT4
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### How to use:
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[Inference script](https://github.com/STEM-ai/Vision/blob/7d92c8daa388eb74e8c336f2d0d3942722fec3c6/ViT_inference.py)
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
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## Training evaluation results
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The most accurate output model was obtained from a learning rate of 1e-3. The quality of the training was evaluated with the training dataset and resulted in the following metrics:
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{'eval_loss': 1.3691096305847168,\
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'eval_accuracy': 0.6666666666666666,\
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