README.md

---
license: apache-2.0
tags:
  - emotion-detection
  - affective-computing
  - classification
  - cnn
datasets:
  - custom
model-index:
  - name: AffectSense
    results: []
---

# 🧠 AffectSense

**AffectSense** is a Convolutional Neural Network (CNN)-based model designed for emotion and affect recognition from visual or image-based data. The model leverages a pre-trained **ResNet-50** backbone and has been fine-tuned for affective computing tasks such as emotion classification and mood detection.

## 🚀 Usage

You can load a model like this:

```python
import torch
from torchvision import models

# Load the model (example if using torch.load)
model = torch.load("path_to_checkpoint.pth")
model.eval()
```

> Or, if packaged in a model class:

```python
from affectsense import AffectSenseModel

model = AffectSenseModel.from_pretrained("tawheed-tariq/AffectSense")
```

## 📊 Intended Uses & Limitations

### Use Cases
- Emotion recognition from facial images
- Affective content tagging in videos
- Visual mood estimation
- Human-computer interaction systems

### Limitations
- May not generalize well across unseen demographics or lighting conditions
- Not suitable for clinical diagnosis
- Accuracy depends on the diversity of training data

## 🏗️ Model Architecture

- **Backbone**: ResNet-50 (pre-trained on ImageNet)
- **Modified Head**: Custom classification head for emotion categories
- **Input Size**: Typically 224×224 RGB images

## 📁 Training Data

The models were trained on custom-curated datasets with emotion-labeled visual data. Examples include facial emotion datasets or affective scene datasets.

## 📜 License

This model is licensed under the Apache 2.0 License.

## ✍️ Citation

If you use this model in your research, please cite:

```
@misc{affectsense2025,
  title={AffectSense: CNN-based Emotion Recognition Model using ResNet-50},
  author={Tariq, Tavaheed},
  year={2025},
  howpublished={\url{https://huggingface.co/tawheed-tariq/AffectSense}},
}
```

---