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---
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}},
}
```
---
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