Instructions to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jvadlamudi2/convnext-tiny-224-jvadlamudi2") 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("jvadlamudi2/convnext-tiny-224-jvadlamudi2") model = AutoModelForImageClassification.from_pretrained("jvadlamudi2/convnext-tiny-224-jvadlamudi2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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update model card README.md
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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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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| No log | 1.0 | 7 | 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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5780
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- Accuracy: 0.7946
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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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| No log | 1.0 | 7 | 0.5882 | 0.8036 |
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| 0.6213 | 2.0 | 14 | 0.5821 | 0.7857 |
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| 0.6123 | 3.0 | 21 | 0.5780 | 0.7946 |
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### Framework versions
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