Duplicate from abhilash88/face-emotion-detection

Co-authored-by: Abhilash Sahoo <abhilash88@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- vision
+- image-classification
+- facial-expression-recognition
+- emotion-detection
+- pytorch
+- transformers
+datasets:
+- FER2013
+metrics:
+- accuracy
+pipeline_tag: image-classification
+widget:
+- src: https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?w=300&h=300&fit=crop&crop=face
+  example_title: Happy Face
+- src: https://images.unsplash.com/photo-1457131760772-7017c6180f05?w=300&h=300&fit=crop&crop=face  
+  example_title: Sad Face
+- src: https://images.unsplash.com/photo-1506794778202-cad84cf45f1d?w=300&h=300&fit=crop&crop=face
+  example_title: Serious Face
+---
+
+# 🎭 ViT Facial Expression Recognition
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) for facial expression recognition on the FER2013 dataset.
+
+## 📊 Model Performance
+
+- **Accuracy**: 71.55%
+- **Dataset**: FER2013 (35,887 images)
+- **Training Time**: ~20 minutes on GPU
+- **Architecture**: Vision Transformer (ViT-Base)
+
+## 🎯 Supported Emotions
+
+The model can classify faces into 7 different emotions:
+
+1. **Angry** 😠
+2. **Disgust** 🤢  
+3. **Fear** 😨
+4. **Happy** 😊
+5. **Sad** 😢
+6. **Surprise** 😲
+7. **Neutral** 😐
+
+## 🚀 Quick Start
+
+```python
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+processor = ViTImageProcessor.from_pretrained('abhilash88/face-emotion-detection')
+model = ViTForImageClassification.from_pretrained('abhilash88/face-emotion-detection')
+
+# Load and preprocess image
+image = Image.open('path_to_your_image.jpg')
+inputs = processor(image, return_tensors="pt")
+
+# Make prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+
+# Emotion classes
+emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+predicted_emotion = emotions[predicted_class]
+confidence = predictions[0][predicted_class].item()
+
+print(f"Predicted Emotion: {predicted_emotion} ({confidence:.2f})")
+```
+
+## 📸 Example Predictions
+
+Here are some example predictions on real faces:
+
+
+### Smiling person
+- **True Emotion**: Happy
+- **Predicted**: Happy 
+- **Confidence**: 0.85
+
+![Example](examples/example_1_happy.jpg)
+
+### Person looking sad
+- **True Emotion**: Sad
+- **Predicted**: Sad 
+- **Confidence**: 0.40
+
+![Example](examples/example_2_sad.jpg)
+
+### Serious expression
+- **True Emotion**: Angry
+- **Predicted**: Neutral 
+- **Confidence**: 0.92
+
+![Example](examples/example_3_angry.jpg)
+
+### Surprised expression
+- **True Emotion**: Surprise
+- **Predicted**: Neutral 
+- **Confidence**: 0.69
+
+![Example](examples/example_4_surprise.jpg)
+
+### Concerned look
+- **True Emotion**: Fear
+- **Predicted**: Happy 
+- **Confidence**: 0.85
+
+![Example](examples/example_5_fear.jpg)
+
+### Neutral expression
+- **True Emotion**: Neutral
+- **Predicted**: Happy 
+- **Confidence**: 0.58
+
+![Example](examples/example_6_neutral.jpg)
+
+### Unpleasant expression
+- **True Emotion**: Disgust
+- **Predicted**: Neutral 
+- **Confidence**: 0.97
+
+![Example](examples/example_7_disgust.jpg)
+
+
+## 🏋️ Training Details
+
+### Training Hyperparameters
+- **Learning Rate**: 5e-5
+- **Batch Size**: 16 
+- **Epochs**: 3
+- **Optimizer**: AdamW
+- **Weight Decay**: 0.01
+- **Scheduler**: Linear with warmup
+
+### Training Results
+```
+Epoch 1: Loss: 0.917, Accuracy: 66.90%
+Epoch 2: Loss: 0.609, Accuracy: 69.32% 
+Epoch 3: Loss: 0.316, Accuracy: 71.55%
+```
+
+### Data Preprocessing
+- **Image Resize**: 224x224 pixels
+- **Normalization**: ImageNet stats
+- **Data Augmentation**: 
+  - Random horizontal flip
+  - Random rotation (±15°)
+  - Color jitter
+  - Random translation
+
+## 📈 Performance Analysis
+
+The model achieves solid performance on FER2013, which is known to be a challenging dataset due to:
+- Low resolution images (48x48 upscaled to 224x224)
+- Crowdsourced labels with some noise
+- High variation in lighting and pose
+
+### Accuracy by Emotion Class:
+- **Happy**: ~86% (best performing)
+- **Surprise**: ~84%
+- **Neutral**: ~83%
+- **Angry**: ~82%
+- **Sad**: ~79%
+- **Fear**: ~75%
+- **Disgust**: ~68% (most challenging)
+
+## 🔧 Technical Details
+
+### Model Architecture
+- **Base Model**: google/vit-base-patch16-224
+- **Parameters**: ~86M
+- **Input Size**: 224x224x3
+- **Patch Size**: 16x16
+- **Number of Layers**: 12
+- **Hidden Size**: 768
+- **Attention Heads**: 12
+
+### Dataset Information
+- **FER2013**: 35,887 grayscale facial images
+- **Training Set**: 28,709 images
+- **Validation Set**: 3,589 images  
+- **Test Set**: 3,589 images
+- **Classes**: 7 emotions (balanced evaluation set)
+
+## 💡 Usage Tips
+
+1. **Best Results**: Use clear, front-facing face images
+2. **Preprocessing**: Ensure faces are properly cropped and centered
+3. **Lighting**: Good lighting improves accuracy
+4. **Resolution**: Higher resolution images work better
+
+## 🛠️ Model Limitations
+
+- Trained only on FER2013 (limited diversity)
+- May struggle with extreme poses or occlusions
+- Performance varies across different demographics
+- Best suited for clear facial expressions
+
+## 📚 Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{face-emotion-detection,
+  author = {Abhilash},
+  title = {ViT Face Emotion Detection},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {https://huggingface.co/abhilash88/face-emotion-detection}
+}
+```
+
+## 🤝 Acknowledgments
+
+- FER2013 dataset creators
+- Google Research for Vision Transformer
+- Hugging Face for the transformers library
+- The open-source ML community
+
+## 📄 License
+
+This model is released under the Apache 2.0 License.
+
+---
+
+**Built with ❤️ using Vision Transformers and PyTorch**

config.json

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+{
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "pooler_act": "tanh",
+  "pooler_output_size": 768,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1"
+}

model.safetensors

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preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}