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README.md

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+# Dog Breeds Classifier (AlexNet Fine-tuned Model)
+
+## Model Description
+This is a fine-tuned version of the **AlexNet** model, designed to classify images into one of 10 different dog breeds. The original AlexNet architecture was pre-trained on the ImageNet dataset, and this version has been specifically adapted to classify dog breeds based on a custom dataset containing images of various dogs.
+
+## Model Details
+### Architecture
+- **Base Model**: AlexNet
+- **Final Layer**: The final fully connected layer has been modified to classify 10 dog breeds.
+  - Input to the last fully connected layer: 4096 features
+  - Output: 10 classes (one for each breed)
+  
+### Dataset
+The dataset used for fine-tuning consists of images of 10 different dog breeds, organized into training, validation, and test sets.
+
+#### Dog Breeds Included:
+1. Beagle
+2. Chihuahua
+3. Corgi
+4. Dalmation
+5. Doberman
+6. Golden Retriever
+7. Maltese
+8. Poodle
+9. Shiba Inu
+10. Siberian Husky
+
+#### Data Format
+- **Image Format**: JPG
+- **Resolution**: The images were resized to 227x227 pixels to match AlexNet’s input requirements.
+- **Dataset Structure**:
+  - `train/`: Contains the training images of dog breeds.
+  - `valid/`: Contains the validation images of dog breeds.
+  - `test/`: Contains the test images of dog breeds.
+
+#### Source:
+The dataset was obtained from Kaggle. You can access it [here](https://www.kaggle.com/datasets/gpiosenka/70-dog-breedsimage-data-set/data?select=dogs.csv).
+
+## Training
+The AlexNet model was fine-tuned using the following setup:
+- **Optimizer**: SGD
+- **Learning Rate**: 0.001
+- **Momentum**: 0.9
+- **Loss Function**: CrossEntropyLoss
+- **Training Epochs**: 10
+- **Batch Size**: 32
+
+## Usage
+You can load and use this model for inference by following the code snippet below.
+
+```python
+# Install Required Libraries
+pip install torch torchvision huggingface_hub
+
+import torch
+import torch.nn as nn
+from torchvision import models
+from huggingface_hub import hf_hub_download
+
+# Load the fine-tuned AlexNet model
+model = models.alexnet(pretrained=False)
+num_features = model.classifier[6].in_features
+model.classifier[6] = nn.Linear(num_features, 10)
+
+# Download model weights from Hugging Face Hub
+model_path = hf_hub_download(repo_id="pramudyalyza/dog-breeds-alexnet", filename="alexnet_model.bin")
+
+# Load the model state
+model.load_state_dict(torch.load(model_path))
+model.eval()
+
+# Example inference
+from PIL import Image
+from torchvision import transforms
+
+# Define transformation
+transform = transforms.Compose([
+    transforms.Resize((227, 227)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load and preprocess an example image
+image = Image.open("path_to_image.jpg")
+image = transform(image).unsqueeze(0)
+
+# Perform inference
+with torch.no_grad():
+    output = model(image)
+    predicted_class = output.argmax(dim=1)
+    print(f"Predicted Dog Breed: {predicted_class.item()}")
+```