Instructions to use darshanz/occupation-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darshanz/occupation-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="darshanz/occupation-prediction") 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("darshanz/occupation-prediction") model = AutoModelForImageClassification.from_pretrained("darshanz/occupation-prediction") - Notebooks
- Google Colab
- Kaggle
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# darshanz/
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This model is a
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0378
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- Train Accuracy: 0.9953
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- Train Top-3-accuracy: 1.0
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- Validation Loss: 0.4967
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- Validation Accuracy: 0.8433
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- Validation Top-3-accuracy: 0.9800
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- Epoch: 6
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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# darshanz/occupation-prediction
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This model is ViT base patch16. Which is pretrained on imagenet dataset, then trained on our custom dataset which is based on occupation prediction. This dataset contains facial images of Indian people which are labeled by occupation. This model predicts the occupation of a person from the facial image of a person. This model gives an accuracy of 84.43%.
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### Training hyperparameters
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