Mamd1/emotion-detection-model

Facial Emotion Recognition — Model

Detects the emotion expressed in a photo of a human face.

Facial emotion classification model in Keras 3 format (best_model.keras). Given a cropped face image it outputs probabilities over 7 emotion classes.

• Maintainer of this repository: Mamd1
• Repository: Mamd1/emotion-detection-model
• Demo Space: Mamd1/emotion-detection-app
• License: Apache-2.0

Attribution

These weights were not trained by the maintainer of this repository. They are redistributed from the original Apache-2.0 licensed release by Ali Ahmed (ali-ahmed-ai-developer/emotion_detection_model on the Hugging Face Hub). Apache-2.0 permits redistribution and modification provided the license and attribution are preserved — this notice satisfies that requirement. Do not remove it.

Classes

Index order matters — the output vector is in exactly this order:

Index	Class
0	angry
1	disgust
2	fear
3	happy
4	neutral
5	sad
6	surprise

Model details

• Backbone: EfficientNetV2-S (ImageNet pre-trained)
• Head: GlobalAveragePooling2D → BatchNorm → Dropout → Dense(512) → BatchNorm → Dropout → Dense(7)
• Input shape: (380, 380, 3), float32, values in [0, 255] (EfficientNetV2 rescales internally)
• Parameters: ~21M
• File: best_model.keras (~343 MB, stored via Git LFS)

Preprocessing

The reference application converts the image to grayscale, then back to RGB (so R=G=B across 3 channels), resizes to 380x380, and passes raw [0, 255] float32 values. Matching this preprocessing is required for accurate results.

Usage

import numpy as np
import tensorflow as tf
from PIL import Image
from huggingface_hub import hf_hub_download

CLASSES = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']

path = hf_hub_download(
    repo_id="Mamd1/emotion-detection-model",
    filename="best_model.keras",
)
model = tf.keras.models.load_model(path)

image = Image.open("face.jpg").convert("L").convert("RGB").resize((380, 380))
x = np.expand_dims(np.array(image, dtype=np.float32), axis=0)

probs = model.predict(x)[0]
print(dict(zip(CLASSES, probs.tolist())))

Limitations

• Trained on tightly cropped faces. Wide shots, small faces, or busy backgrounds degrade accuracy significantly — crop to the face first.
• Facial expression recognition is not a reliable measure of a person's actual internal emotional state. Do not use for consequential decisions about people (hiring, security screening, medical or psychological assessment).
• Accuracy varies across demographic groups and lighting conditions.