facial_emotion

Facial Emotion Recognition – SimpleCNN

A lightweight convolutional neural network (CNN) for facial emotion recognition.
The model is trained to classify grayscale facial images into 7 emotion categories.

Model Details

• Model type: SimpleCNN (custom lightweight CNN)
• Framework: PyTorch
• Task: Image Classification
• Input shape: 1 × 48 × 48 (grayscale image)
• Number of classes: 7
• Classes:
  • 0 → Angry
  • 1 → Disgust
  • 2 → Fear
  • 3 → Happy
  • 4 → Sad
  • 5 → Surprise
  • 6 → Neutral

Intended Uses & Limitations

• ✅ Educational projects, demos, prototypes.
• ❌ Not suitable for medical, psychological, or safety-critical applications.
• ❌ May not generalize well outside datasets like FER2013.

How to Use

from huggingface_hub import hf_hub_download
import torch
import json

from facial_emotion import SimpleCNN  # your model class

# Load config
config = json.load(open("config.json"))

# Build model
model = SimpleCNN(num_classes=config["num_classes"], in_channels=config["in_channels"])

# Load weights
checkpoint = hf_hub_download(repo_id="sreenathsree1578/facial_emotion", filename="pytorch_model.bin")
model.load_state_dict(torch.load(checkpoint, map_location="cpu"))
model.eval()

# Example inference
dummy = torch.randn(1, 1, 48, 48)  # dummy grayscale image
with torch.no_grad():
    out = model(dummy)
    pred = torch.argmax(out, dim=1).item()
    print("Predicted emotion:", config["labels"][str(pred)])