Instructions to use TirathP/vit-base-patch16-224-finetuned-customData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TirathP/vit-base-patch16-224-finetuned-customData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TirathP/vit-base-patch16-224-finetuned-customData") 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("TirathP/vit-base-patch16-224-finetuned-customData") model = AutoModelForImageClassification.from_pretrained("TirathP/vit-base-patch16-224-finetuned-customData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 15
Browse files
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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- Train Loss: 0.
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- Validation Loss: 0.
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- Validation Accuracy: 0.8571
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- Epoch:
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## Model description
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| 0.2227 | 0.2993 | 0.8571 | 11 |
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| 0.2860 | 0.3299 | 0.8571 | 12 |
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| 0.2081 | 0.3553 | 0.8571 | 13 |
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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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- Train Loss: 0.2246
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- Validation Loss: 0.2942
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- Validation Accuracy: 0.8571
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- Epoch: 15
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## Model description
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| 0.2227 | 0.2993 | 0.8571 | 11 |
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| 0.2860 | 0.3299 | 0.8571 | 12 |
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| 0.2081 | 0.3553 | 0.8571 | 13 |
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| 0.2243 | 0.3360 | 0.8571 | 14 |
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| 0.2246 | 0.2942 | 0.8571 | 15 |
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### Framework versions
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logs/train/events.out.tfevents.1691658382.1f568bba7c99.380.0.v2
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logs/validation/events.out.tfevents.1691658411.1f568bba7c99.380.1.v2
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tf_model.h5
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