Instructions to use CVPROJ25/FINETUNED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CVPROJ25/FINETUNED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CVPROJ25/FINETUNED") 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("CVPROJ25/FINETUNED") model = AutoModelForImageClassification.from_pretrained("CVPROJ25/FINETUNED", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training complete
Browse files- README.md +5 -6
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README.md
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.2122 | 4.0 | 64 | 1.1594 | 0.7235 |
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### Framework versions
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8523
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- Accuracy: 0.785
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.7260 | 1.0 | 16 | 0.8205 | 0.789 |
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| 0.3611 | 2.0 | 32 | 0.8372 | 0.784 |
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| 0.2573 | 3.0 | 48 | 0.8523 | 0.785 |
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### Framework versions
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model.safetensors
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