Instructions to use luciagil/Human-action-convnext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luciagil/Human-action-convnext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="luciagil/Human-action-convnext") 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("luciagil/Human-action-convnext") model = AutoModelForImageClassification.from_pretrained("luciagil/Human-action-convnext", 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 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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### 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: 0.7181
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- Accuracy: 0.7917
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.9287 | 0.99 | 78 | 0.8094 | 0.7726 |
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| 0.8263 | 1.99 | 157 | 0.7395 | 0.7877 |
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| 0.7472 | 2.97 | 234 | 0.7181 | 0.7917 |
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### Framework versions
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