Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use pratapaadii/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratapaadii/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pratapaadii/image_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("pratapaadii/image_classification") model = AutoModelForImageClassification.from_pretrained("pratapaadii/image_classification") - Notebooks
- Google Colab
- Kaggle
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Model description
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Hey everyone! This is the first model I’ve deployed :D. This emotion recognition model is a fine-tuned version of google/vit-base-patch16-224-in21k, trained on the ImageFolder dataset. As a first-timer, I’m proud that this model has achieved such accuracy. I plan to further train it to improve its accuracy. Wish me luck!
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## Intended uses & limitations
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I strongly suggest using an input picture with a clear indication of emotion, as I’ve found that the model can sometimes misinterpret the output. Additionally, this model seems to lack confidence in identifying emotions, as evidenced by the slightly varying scores.
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## Training and evaluation data
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