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README.md

@@ -4,6 +4,7 @@ tags:
 - image-classification
 - food-recognition
 - raw-food
+- multilabel-classification
 - pytorch
 - resnet
 - se-resnet
@@ -11,28 +12,41 @@ tags:
 - model-comparison
 datasets:
 - ibrahimdaud/raw-food-recognition
+- ibrahimdaud/multi-label-food-recognition
 metrics:
 - accuracy
+- mean-average-precision
+- f1-score
 ---
 
-# Raw Food Recognition Models: ResNet-50 vs SE-ResNet-50
+# Raw Food Recognition Models: Single-Class and Multi-Label
 
-This repository contains both ResNet-50 and SE-ResNet-50 models trained for raw food ingredient recognition using the merged raw food recognition dataset.
+This repository contains both single-class and multi-label classification models trained for raw food ingredient recognition.
 
-## Model Comparison
+## Single-Class Classification Models
 
-| Model | Parameters | Validation Accuracy | Architecture |
-|-------|-----------|-------------------|--------------|
-| ResNet-50 | ~25.6M | 97.84% | Standard residual network |
-| SE-ResNet-50 | ~26.0M | 95.72% | ResNet-50 with SE attention |
 
-## Dataset
+| Model        | Parameters | Validation Accuracy | Architecture                |
+| ------------ | ---------- | ------------------- | --------------------------- |
+| ResNet-50    | ~25.6M    | 97.84%              | Standard residual network   |
+| SE-ResNet-50 | ~26.0M    | 95.72%              | ResNet-50 with SE attention |
+
 
 Both models were trained on the [ibrahimdaud/raw-food-recognition](https://huggingface.co/datasets/ibrahimdaud/raw-food-recognition) dataset, which contains 90+ raw food categories.
 
+
+## Multi-Label Classification Models
+
+| Model | Training Mode | Parameters | Best mAP | Architecture |
+|-------|--------------|------------|----------|--------------|
+| Multi-Label ResNet-50 | Freeze Encoder | ~24,656,463 | 0.3747 | ResNet-50 encoder (frozen) + classifier |
+
+
+Multi-label models were trained on the [ibrahimdaud/multi-label-food-recognition](https://huggingface.co/datasets/ibrahimdaud/multi-label-food-recognition) dataset for recognizing multiple ingredients in a single image.
+
 ## Usage
 
-### Download Both Models
+### Download Single-Class Models
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -55,7 +69,7 @@ se_resnet_path = hf_hub_download(
 se_resnet_checkpoint = torch.load(se_resnet_path, map_location='cpu')
 ```
 
-### Load ResNet-50
+### Load Single-Class Models
 
 ```python
 # Create ResNet-50 model
@@ -65,11 +79,7 @@ resnet_model = create_resnet50(
 )
 resnet_model.load_state_dict(resnet_checkpoint['model_state_dict'])
 resnet_model.eval()
-```
 
-### Load SE-ResNet-50
-
-```python
 # Create SE-ResNet-50 model
 se_resnet_model = create_se_resnet50(
     num_classes=90,
@@ -80,7 +90,98 @@ se_resnet_model.load_state_dict(se_resnet_checkpoint['model_state_dict'])
 se_resnet_model.eval()
 ```
 
-### Compare Predictions
+### Download Multi-Label Models
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from models.multilabel_resnet50 import create_multilabel_resnet50
+
+# Download Freeze Encoder model
+freeze_path = hf_hub_download(
+    repo_id="ibrahimdaud/raw-food-recognition-models",
+    filename="multilabel_freeze_pytorch_model.bin"
+)
+freeze_checkpoint = torch.load(freeze_path, map_location='cpu')
+
+# Download Full Training model
+full_path = hf_hub_download(
+    repo_id="ibrahimdaud/raw-food-recognition-models",
+    filename="multilabel_full_pytorch_model.bin"
+)
+full_checkpoint = torch.load(full_path, map_location='cpu')
+
+# Download Fine-Tuning model
+finetune_path = hf_hub_download(
+    repo_id="ibrahimdaud/raw-food-recognition-models",
+    filename="multilabel_finetune_pytorch_model.bin"
+)
+finetune_checkpoint = torch.load(finetune_path, map_location='cpu')
+```
+
+
+### Load Multi-Label Models
+
+```python
+# Load Freeze Encoder model
+freeze_model = create_multilabel_resnet50(
+    num_classes=freeze_checkpoint['num_classes'],
+    pretrained=False
+)
+freeze_model.load_state_dict(freeze_checkpoint['model_state_dict'])
+freeze_model.eval()
+
+# Load Full Training model
+full_model = create_multilabel_resnet50(
+    num_classes=full_checkpoint['num_classes'],
+    pretrained=False
+)
+full_model.load_state_dict(full_checkpoint['model_state_dict'])
+full_model.eval()
+
+# Load Fine-Tuning model
+finetune_model = create_multilabel_resnet50(
+    num_classes=finetune_checkpoint['num_classes'],
+    pretrained=False
+)
+finetune_model.load_state_dict(finetune_checkpoint['model_state_dict'])
+finetune_model.eval()
+```
+
+### Multi-Label Inference
+
+```python
+import torch
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Preprocess image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                        std=[0.229, 0.224, 0.225])
+])
+
+image = Image.open('path/to/image.jpg').convert('RGB')
+image_tensor = transform(image).unsqueeze(0)
+
+# Get multi-label predictions
+with torch.no_grad():
+    logits = freeze_model(image_tensor)  # or full_model, finetune_model
+    probs = torch.sigmoid(logits)  # Multi-label probabilities
+    
+    # Get top-k predictions
+    top_k = 5
+    top_probs, top_indices = torch.topk(probs[0], top_k)
+    
+    # Assuming you have class names
+    for prob, idx in zip(top_probs, top_indices):
+        print(f"Class {{idx.item()}}: {{prob.item():.4f}}")
+```
+
+
+### Compare Single-Class Predictions
 
 ```python
 import torch
@@ -126,21 +227,41 @@ print(f"  Confidence: {se_resnet_confidence*100:.2f}%")
 
 ## Model Details
 
-### ResNet-50
+### Single-Class Models
+
+#### ResNet-50
 - **Architecture**: Standard residual network with bottleneck blocks
 - **Parameters**: ~25.6M
 - **Pretrained**: ImageNet weights
 - **Best Validation Accuracy**: 97.84%
 
-### SE-ResNet-50
+#### SE-ResNet-50
 - **Architecture**: ResNet-50 with Squeeze-and-Excitation attention blocks
 - **Parameters**: ~26.0M
 - **Pretrained**: ImageNet weights (excluding SE blocks)
 - **SE Reduction Ratio**: 16
 - **Best Validation Accuracy**: 95.72%
 
+### Multi-Label Models
+
+#### Freeze Encoder Mode
+- **Training Strategy**: Encoder frozen, only classifier trained
+- **Use Case**: Fast training, preserves encoder features
+- **Best for**: When you have limited data or want quick results
+
+#### Full Training Mode
+- **Training Strategy**: Both encoder and classifier trained from scratch
+- **Use Case**: Maximum flexibility, learns task-specific features
+- **Best for**: When you have sufficient data and compute
+
+#### Fine-Tuning Mode
+- **Training Strategy**: Encoder trained with lower learning rate, classifier with higher rate
+- **Use Case**: Balanced approach, preserves some encoder knowledge while adapting
+- **Best for**: General-purpose multi-label classification
+
 ## Training Details
 
+### Single-Class Models
 - **Dataset**: ibrahimdaud/raw-food-recognition
 - **Number of Classes**: 90
 - **Image Size**: 224x224
@@ -148,12 +269,25 @@ print(f"  Confidence: {se_resnet_confidence*100:.2f}%")
 - **Learning Rate**: 0.001
 - **Batch Size**: 32
 
+### Multi-Label Models
+- **Dataset**: ibrahimdaud/multi-label-food-recognition
+- **Image Size**: 224x224
+- **Optimizer**: Adam
+- **Loss Function**: BCEWithLogitsLoss
+- **Evaluation Metrics**: Mean Average Precision (mAP), F1-Score, Hamming Loss
+
 ## Files in Repository
 
+### Single-Class Models
 - `resnet50_pytorch_model.bin` - ResNet-50 model weights
 - `se_resnet50_pytorch_model.bin` - SE-ResNet-50 model weights
 - `resnet50_metadata.json` - ResNet-50 metadata
 - `se_resnet50_metadata.json` - SE-ResNet-50 metadata
+
+### Multi-Label Models
+- `multilabel_freeze_pytorch_model.bin` - Multi-label ResNet-50 (Freeze Encoder)
+- `multilabel_freeze_metadata.json` - Freeze Encoder metadata
+
 - `README.md` - This file
 
 ## Citation
@@ -162,7 +296,7 @@ If you use these models, please cite:
 
 ```bibtex
 @model{raw_food_recognition_models_2024,
-  title={Raw Food Recognition Models: ResNet-50 and SE-ResNet-50},
+  title={Raw Food Recognition Models: Single-Class and Multi-Label Classification},
   author={Ibrahim Daud},
   year={2024},
   publisher={HuggingFace},