| --- |
| license: apache-2.0 |
| library_name: keras |
| pipeline_tag: image-classification |
| tags: |
| - emotion-detection |
| - facial-expression-recognition |
| - image-classification |
| - keras |
| - tensorflow |
| - efficientnetv2 |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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. |
|
|
