Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use dennisjooo/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dennisjooo/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dennisjooo/emotion_classification") 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("dennisjooo/emotion_classification") model = AutoModelForImageClassification.from_pretrained("dennisjooo/emotion_classification") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): a8e1506
Update README.md
Browse filesMe dumb, thought there are only seven labels but it turns out there are 8 labels (index 0 to 7)
README.md
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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)
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on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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In theory, the accuracy for a random guess on this dataset is 0.
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It achieves the following results on the evaluation set:
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- Loss: 1.0511
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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)
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on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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In theory, the accuracy for a random guess on this dataset is 0.125 (8 labels and you need to choose one).
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It achieves the following results on the evaluation set:
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- Loss: 1.0511
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