Image Classification
Transformers
Safetensors
vit
vision
STEM-AI-mtl/City_map
Google
ViT
STEM-AI-mtl
Instructions to use STEM-AI-mtl/City_map-vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STEM-AI-mtl/City_map-vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="STEM-AI-mtl/City_map-vit-base-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("STEM-AI-mtl/City_map-vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("STEM-AI-mtl/City_map-vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Training evaluation results
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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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'eval_runtime': 13.0277,
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'eval_samples_per_second': 4.606,
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'eval_steps_per_second': 0.154,
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'epoch': 2.82}
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## Training evaluation results
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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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'eval_runtime': 13.0277,\
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'eval_samples_per_second': 4.606,\
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'eval_steps_per_second': 0.154,\
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'epoch': 2.82}
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