Instructions to use SaladSlayer00/image_classification_resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaladSlayer00/image_classification_resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SaladSlayer00/image_classification_resnet") 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("SaladSlayer00/image_classification_resnet") model = AutoModelForImageClassification.from_pretrained("SaladSlayer00/image_classification_resnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 488c541
Training in progress epoch 5
Browse files
README.md
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss:
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- Validation Loss:
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- Validation Accuracy: 0.
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- Epoch:
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## Model description
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| 4.3466 | 4.1339 | 0.1198 | 2 |
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| 3.8826 | 3.6814 | 0.1758 | 3 |
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| 3.4086 | 3.2561 | 0.2531 | 4 |
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### Framework versions
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 2.9790
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- Validation Loss: 2.8971
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- Validation Accuracy: 0.3091
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- Epoch: 5
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## Model description
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| 4.3466 | 4.1339 | 0.1198 | 2 |
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| 3.8826 | 3.6814 | 0.1758 | 3 |
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| 3.4086 | 3.2561 | 0.2531 | 4 |
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| 2.9790 | 2.8971 | 0.3091 | 5 |
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### Framework versions
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logs/validation/events.out.tfevents.1703540000.fv-az847-406.1850.1.v2
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