Facial-Expression-Recognition
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the FER 2013 and AffectNet dataset datasets. It achieves the following results on the evaluation set: Accuracy - 0.922 Loss - 0.213
Model Description
The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition.
It is trained on the FER2013and AffectNet datasets, which consist of facial images categorized into eight different emotions: -anger -contempt -sad -happy -neutral -disgust -fear -surprise
Model Details
The model has been fine-tuned using the following hyperparameters:
| Hyperparameter | Value |
|---|---|
| Train Batch Size | 32 |
| Eval Batch Size | 64 |
| Learning Rate | 2e-4 |
| Gradient Accumulation | 2 |
| LR Scheduler | Linear |
| Warmup Ratio | 0.04 |
| Num Epochs | 10 |
How to Get Started with the Model
Example usage:
from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
pipe = pipeline("image-classification", model="HardlyHumans/Facial-expression-detection")
processor = AutoImageProcessor.from_pretrained("HardlyHumans/Facial-expression-detection")
model = AutoModelForImageClassification.from_pretrained("HardlyHumans/Facial-expression-detection")
labels = model.config.id2label
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
predicted_label = labels[predicted_class_idx]
Environmental Impact
The net estimated CO2 emission using the Machine Learning Impact calculator scale is around 8.82kg of CO2.
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Developed by: Hardly Humans club, IIT Dharwad
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Model type: Vision transformer
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License: MIT
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Finetuned from model: google/vit-base-patch16-224-in21k
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Hardware Type: T4
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Hours used: 8+27
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Cloud Provider: Google collabotary service
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Compute Region: South asia-1
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Carbon Emitted: 8.82