Dc-4nderson/vit-emotion-classifier

🤖 ViT Emotion Classifier

This is a lightweight Vision Transformer (ViT) model fine-tuned to classify emotions from facial images using a custom dataset of school-aged individuals. It supports 8 emotional categories and is designed to work well on small datasets and limited compute.

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🧠 Supported Emotions

The model predicts one of the following emotional states:

Label ID	Emotion
0	anxious-fearful
1	bored
2	confused
3	discouraged
4	frustrated
5	neutral
6	positive
7	suprised

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📦 Model Details

• Model Type: ViTForImageClassification
• Backbone: vit-small-patch16-224
• Dataset: Dc-4nderson/feelings_classfication_dataset
• Framework: PyTorch
• Labels: 8 emotions (defined in config.json)
• Trained on: Google Colab with < 600 images

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🧪 Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load model + processor
processor = AutoImageProcessor.from_pretrained("Dc-4nderson/vit-emotion-classifier")
model = AutoModelForImageClassification.from_pretrained("Dc-4nderson/vit-emotion-classifier")

# Load image and preprocess
image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    pred = torch.argmax(outputs.logits, dim=1).item()
    label = model.config.id2label[str(pred)]

print("🧠 Predicted Emotion:", label)