Instructions to use karim155/convnext-tiny-224-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karim155/convnext-tiny-224-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="karim155/convnext-tiny-224-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("karim155/convnext-tiny-224-finetuned") model = AutoModelForImageClassification.from_pretrained("karim155/convnext-tiny-224-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Logloss: 1.
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- Accuracy: {'accuracy': 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------------------:|
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| No log | 0.
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### Framework versions
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3130
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- Logloss: 1.3130
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- Accuracy: {'accuracy': 0.43}
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------------------:|
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| No log | 0.96 | 6 | 1.4663 | 1.4663 | {'accuracy': 0.42} |
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| 1.4937 | 1.92 | 12 | 1.3507 | 1.3507 | {'accuracy': 0.445} |
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| 1.4937 | 2.88 | 18 | 1.3130 | 1.3130 | {'accuracy': 0.43} |
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### Framework versions
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