Instructions to use Piro17/13E-affecthq-fer-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Piro17/13E-affecthq-fer-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Piro17/13E-affecthq-fer-balanced") 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("Piro17/13E-affecthq-fer-balanced") model = AutoModelForImageClassification.from_pretrained("Piro17/13E-affecthq-fer-balanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# 13E-affecthq-fer-balanced
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on
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It achieves the following results on the evaluation set:
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- Loss: 1.0526
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- Accuracy: 0.6225
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# 13E-affecthq-fer-balanced
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on Piro17/balancednumber-affecthqnet-fer2013 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0526
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- Accuracy: 0.6225
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