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---
license: mit
datasets:
- MichaelMM2000/animals10
---
# AnimalNet18
**AnimalNet18** is an animal image classification model trained on the [Animals-10](https://huggingface.co/datasets/MichaelMM2000/animals10) dataset.
The goal of the model is to classify images into common animal categories in the dataset.
---
## Dataset
- **Source**: [MichaelMM2000/animals10](https://huggingface.co/datasets/MichaelMM2000/animals10)
- **Number of classes**: 10 (e.g., dog, cat, horse, elephant, butterfly, …)
---
## Architecture
- Backbone: **ResNet-18** (PyTorch)
- Input size: `224x224`
- Optimizer: Adam
- Loss: CrossEntropy
---
## Usage
### 1. Load the model from Hugging Face
```python
import torch, torch.nn as nn
from torchvision import models, transforms
from PIL import Image
from huggingface_hub import hf_hub_download
# Load model
path = hf_hub_download("CatHann/AnimalNet18", "AnimalNet18.pth")
model = models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, 10)
model.load_state_dict(torch.load(path, map_location="cpu"))
model.eval()
# Transform & predict
tfm = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
])
img = tfm(Image.open("test.jpg")).unsqueeze(0)
pred = model(img).argmax(1).item()
print("Predicted class:", pred)
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