Property	Details
🏗️ Architecture	CNN (custom)
🎯 Task	Image Classification
😀 Classes	7 emotions
⚙️ Framework	PyTorch
📐 Input size	48 × 48 px (grayscale)
📜 License	MIT

🧠 Emotion Classes

#	Emotion
0	😠 Angry
1	🤢 Disgust
2	😨 Fear
3	😄 Happy
4	😐 Neutral
5	😢 Sad
6	😲 Surprise

🚀 How to Use

python

import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image

# Load model
model = torch.jit.load("model.pt", map_location="cpu")
model.eval()

# Preprocessing
transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((48, 48)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])

EMOTIONS = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]

# Inference
image = Image.open("face.jpg")
tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    probs = F.softmax(model(tensor), dim=1)[0]

predicted = EMOTIONS[probs.argmax()]
confidence = probs.max().item()

print(f"Prediction: {predicted} ({confidence:.0%})")

🗂️ Training Data

Base dataset: FER-2013 (Facial Expression Recognition)
Input format: 48×48 grayscale face images
Classes: 7 universal emotion categories

⚠️ Limitations

Optimized for frontal face images
Performance may degrade with partial occlusion, extreme lighting, or non-frontal poses
Not intended for surveillance or identity recognition — expression classification only