Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Takekazuchi/Caracam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Takekazuchi/Caracam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Takekazuchi/Caracam") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Takekazuchi/Caracam") model = AutoModelForImageClassification.from_pretrained("Takekazuchi/Caracam") - Notebooks
- Google Colab
- Kaggle
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README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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## Intended uses & limitations
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## Training and evaluation data
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# Caracam (gen 1)
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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## Intended uses & limitations
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***NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP***
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## Training and evaluation data
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