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license: mit
datasets:
- Dc-4nderson/feelings_classfication_dataset
language:
- en
base_model:
- google/vit-base-patch16-224-in21k
pipeline_tag: image-classification
library_name: transformers
tags:
- emotions
- school
metrics:
- accuracy
---
# 🤖 ViT Emotion Classifier
This is a lightweight [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/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.
---
## 🧠 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 |
---
## 📦 Model Details
- **Model Type**: `ViTForImageClassification`
- **Backbone**: `vit-small-patch16-224`
- **Dataset**: [`Dc-4nderson/feelings_classfication_dataset`](https://huggingface.co/datasets/Dc-4nderson/feelings_classfication_dataset)
- **Framework**: PyTorch
- **Labels**: 8 emotions (defined in `config.json`)
- **Trained on**: Google Colab with < 600 images
---
## 🧪 Usage
```python
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) |