Delete README.md

README.md

@@ -1,135 +0,0 @@
----
-license: mit
-tags:
-- food
-- ingredient-classification
-- computer-vision
-- multi-label-classification
-- pytorch
-library_name: pytorch
----
-
-# Food Ingredient Classifier
-
-A multi-label classification model that identifies ingredients in food images.
-
-## Model Description
-
-This model uses a ResNet-50 backbone fine-tuned on the Food-101 dataset to classify 101 different food ingredients.
-
-## Performance
-
-- **Validation Accuracy**: 99.01%
-- **Architecture**: ResNet-50 with custom classification head
-- **Training Epochs**: 1
-- **Number of Classes**: 101
-
-## Ingredients Classified
-
-apple_pie, baby_back_ribs, baklava, beef_carpaccio, beef_tartare, beet_salad, beignets, bibimbap, bread_pudding, breakfast_burrito, bruschetta, caesar_salad, cannoli, caprese_salad, carrot_cake, ceviche, cheese_plate, cheesecake, chicken_curry, chicken_quesadilla, chicken_wings, chocolate_cake, chocolate_mousse, churros, clam_chowder, club_sandwich, crab_cakes, creme_brulee, croque_madame, cup_cakes, deviled_eggs, donuts, dumplings, edamame, eggs_benedict, escargots, falafel, filet_mignon, fish_and_chips, foie_gras, french_fries, french_onion_soup, french_toast, fried_calamari, fried_rice, frozen_yogurt, garlic_bread, gnocchi, greek_salad, grilled_cheese_sandwich, grilled_salmon, guacamole, gyoza, hamburger, hot_and_sour_soup, hot_dog, huevos_rancheros, hummus, ice_cream, lasagna, lobster_bisque, lobster_roll_sandwich, macaroni_and_cheese, macarons, miso_soup, mussels, nachos, omelette, onion_rings, oysters, pad_thai, paella, pancakes, panna_cotta, peking_duck, pho, pizza, pork_chop, poutine, prime_rib, pulled_pork_sandwich, ramen, ravioli, red_velvet_cake, risotto, samosa, sashimi, scallops, seaweed_salad, shrimp_and_grits, spaghetti_bolognese, spaghetti_carbonara, spring_rolls, steak, strawberry_shortcake, sushi, tacos, takoyaki, tiramisu, tuna_tartare, waffles
-
-## Usage
-
-```python
-import torch
-from torchvision import transforms, models
-from PIL import Image
-import torch.nn as nn
-from huggingface_hub import hf_hub_download
-
-# Model architecture
-class FoodIngredientClassifier(nn.Module):
-    def __init__(self, num_classes=101):
-        super().__init__()
-        self.backbone = models.resnet50(pretrained=False)
-        num_features = self.backbone.fc.in_features
-        self.backbone.fc = nn.Sequential(
-            nn.Dropout(0.5),
-            nn.Linear(num_features, 512),
-            nn.ReLU(),
-            nn.Dropout(0.3),
-            nn.Linear(512, num_classes)
-        )
-    
-    def forward(self, x):
-        return self.backbone(x)
-
-# Load model
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model = FoodIngredientClassifier(num_classes=101).to(device)
-
-# Download checkpoint from Hugging Face
-checkpoint_path = hf_hub_download(
-    repo_id="YOUR_USERNAME/food-ingredient-classifier",
-    filename="best_model.pth"
-)
-checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
-model.load_state_dict(checkpoint['model_state_dict'])
-model.eval()
-
-# Get ingredient labels
-mlb = checkpoint['mlb']
-
-# Prepare image
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
-
-# Inference
-img = Image.open('food_image.jpg').convert('RGB')
-img_tensor = transform(img).unsqueeze(0).to(device)
-
-with torch.no_grad():
-    output = model(img_tensor)
-    probs = torch.sigmoid(output).cpu().numpy()[0]
-
-# Get predictions (threshold at 0.5)
-threshold = 0.5
-pred_indices = (probs > threshold).nonzero()[0]
-ingredients = mlb.classes_[pred_indices]
-confidences = probs[pred_indices]
-
-for ingredient, confidence in zip(ingredients, confidences):
-    print(f"{ingredient}: {confidence:.2%}")
-```
-
-## Training Details
-
-- **Dataset**: Food-101 (101,000 images across 101 categories)
-- **Batch Size**: 64
-- **Optimizer**: AdamW with weight decay (0.01)
-- **Learning Rate**: 0.001 with OneCycleLR scheduler
-- **Data Augmentation**: Random horizontal flip, rotation (15°), color jitter
-- **Mixed Precision Training**: Enabled for faster training
-- **Image Size**: 224x224
-
-## Model Architecture
-
-- **Backbone**: ResNet-50 (pretrained on ImageNet)
-- **Custom Head**:
-  - Dropout (0.5)
-  - Linear (2048 → 512)
-  - ReLU
-  - Dropout (0.3)
-  - Linear (512 → 101)
-- **Output**: Multi-label (sigmoid activation)
-
-## Citation
-
-If you use this model, please cite:
-
-```bibtex
-@misc{food-ingredient-classifier,
-  author = {Your Name},
-  title = {Food Ingredient Classifier},
-  year = {2025},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/food-ingredient-classifier}}
-}
-```
-
-## License
-
-MIT License