File size: 2,348 Bytes
a9e745b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
license: mit
tags:
- image-classification
- pytorch
- convnext
- dog-breeds
datasets:
- custom
language:
- en
metrics:
- accuracy
library_name: timm
---
# 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
```python
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}}
}
```
|