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Datasets:
imadhavan
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FER2025

Modalities:
Image
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Formats:
webdataset
Size:
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Tags:
facial-emotion
FER
emotion-recognition
deep-learning
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FER2025
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---

name: "FER2025"
tags: ["facial-emotion", "FER", "emotion-recognition", "deep-learning"]
license: "cc-by-nc-4.0"

---

# FER2025 – Facial Expression Recognition Dataset

[![License: Research Use Only](https://img.shields.io/badge/License-Research%20Use%20Only-lightgrey.svg)](#license--attribution)
[![Total Images](https://img.shields.io/badge/Total%20Images-1.59M-blue)](#overview)
[![Classes](https://img.shields.io/badge/Classes-7-green)](#overview)

---

## Overview

**FER2025** is a **large-scale, balanced facial emotion dataset** designed for **deep learning and computer vision research**. It contains **1,589,810 images** across **7 emotion classes**:

| Class      | Images  |
|------------|---------|
| Angry      | 224,624 |
| Disgust    | 239,366 |
| Fear       | 223,466 |
| Happy      | 222,082 |
| Neutral    | 234,230 |
| Sad        | 217,884 |
| Surprise   | 228,158 |

**Image formats:** jpg, jpeg, png
**Balanced:** Maximum class difference ≈ 1.3%  

FER2025 is suitable for **feature extraction, model training, and benchmarking**and**Training deep learning model**.

---

## Dataset Structure

FER2025/ ➡️ Angry.tar | Disgust.tar | Fear.tar | Happy.tar | Neutral.tar | Sad.tar | Surprise.tar

Each TAR contains **images + corresponding `.cls` label files** for efficient streaming.

---

## Recommended Usage

- **Feature Extraction:** ResNet, EfficientNet, ViT embeddings  
- **Training & Evaluation:** Balanced classes remove need for oversampling or class weighting  
- **Large-Scale Training:** Use TAR/WebDataset format for GPU-efficient streaming  

---

## Example: Loading FER2025 with PyTorch + WebDataset

```python
import webdataset as wds
from torchvision import transforms
import torch

transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
])

dataset = (
    wds.WebDataset("FER2025/{Angry,Disgust,Fear,Happy,Neutral,Sad,Surprise}.tar")
    .decode("pil")
    .to_tuple("jpg", "cls")
    .map_tuple(transform, int)
)

loader = torch.utils.data.DataLoader(dataset, batch_size=64, num_workers=4, shuffle=True)

for images, labels in loader:
    print(images.shape, labels.shape)
    break
```

---

## License & Ethical Use

**License:** CC BY-NC 4.0 – Attribution required, non-commercial use

**Ethical Use:** Images are sourced from publicly available data for research. Users must respect privacy and avoid commercial misuse.

---

## Citations

@dataset
{FER2025,
  author = {Adhavan M},
  title = {FER2025: Large-Scale Balanced Facial Expression Dataset},
  year = {2025},
  url = https://huggingface.co/datasets/imadhavan/FER2025
}