HardlyHumans/Facial-expression-detection

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.

• Developed by: Hardly Humans club, IIT Dharwad

• Model type: Vision transformer

• License: MIT

• Finetuned from model: google/vit-base-patch16-224-in21k

• Hardware Type: T4

• Hours used: 8+27

• Cloud Provider: Google collabotary service

• Compute Region: South asia-1

• Carbon Emitted: 8.82

Model Architecture and Objective