Alamgirapi/dog-breed-convnext-classifier

Dog Breed Classifier - ConvNeXt Base

This model is a fine-tuned ConvNeXt-Base model for classifying dog breeds among 7 different classes.

Model Details

• Model Architecture: ConvNeXt-Base
• Framework: PyTorch + timm
• Task: Image Classification
• Classes: 7 dog breeds
• Input Size: 224x224 RGB images

Classes

The model can classify the following dog breeds:

• Beagle
• Bulldog
• Dalmatian
• German Shepherd
• Husky
• Poodle
• Rottweiler

Usage

import torch
import timm
from torchvision import transforms
from PIL import Image

# Load model
model = timm.create_model('convnext_base', pretrained=False)
model.head = torch.nn.Sequential(
    torch.nn.AdaptiveAvgPool2d(1),
    torch.nn.Flatten(),
    torch.nn.Linear(model.head.in_features, 7)
)

# Load weights
model.load_state_dict(torch.load('model.pth', map_location='cpu'))
model.eval()

# Preprocessing
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])
])

# Inference
image = Image.open('dog_image.jpg')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    outputs = model(input_tensor)
    probabilities = torch.nn.functional.softmax(outputs[0], dim=0)

Model Performance

• Training accuracy: [Add your metrics]
• Validation accuracy: [Add your metrics]

Training Details

• Base model: ConvNeXt-Base (pretrained on ImageNet)
• Fine-tuning approach: [Add details]
• Dataset: Custom dog breed dataset
• Epochs: [Add number]
• Optimizer: [Add optimizer details]

Limitations

• The model is trained on a specific set of 7 dog breeds
• Performance may vary on images outside the training distribution
• Best results with clear, well-lit images of single dogs

Citation

If you use this model, please cite:

@misc{dog-breed-convnext-2024,
  title={Dog Breed Classification with ConvNeXt},
  author={Alamgirapi},
  year={2024},
  howpublished={\url{https://huggingface.co/Alamgirapi/dog-breed-convnext-classifier}}
}