Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use digo-prayudha/vit-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digo-prayudha/vit-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="digo-prayudha/vit-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("digo-prayudha/vit-emotion-classification") model = AutoModelForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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type: accuracy
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value: 0.6125
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pipeline_tag: image-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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type: accuracy
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value: 0.6125
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pipeline_tag: image-classification
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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example_title: Tiger
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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example_title: Teapot
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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