Update README.md

README.md

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----
-license: mit
----
+---
+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)