---
license: mit
datasets:
- uoft-cs/cifar10
base_model:
- microsoft/resnet-18
pipeline_tag: image-classification
---
tags:
- image-classification
- pytorch
- resnet
- cifar10
license: apache-2.0  # Choose an appropriate license (e.g., apache-2.0, mit, etc.)
---

# ResNet-18 for CIFAR-10 Image Classification

This is a ResNet-18 model fine-tuned on the CIFAR-10 dataset for image classification.

## Model Details:

- **Architecture:** ResNet-18, pre-trained on ImageNet.
- **Dataset:** Fine-tuned on the CIFAR-10 dataset.
- **Task:** Image Classification.
- **Classes:** Classifies images into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
- **Framework:** PyTorch.

## How to Use:

```python
import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# Load the model architecture (you need to define your CIFAR10_ResNet class, or adapt a standard ResNet)
model = models.resnet18(num_classes=10)  # Or however you adapted ResNet for 10 classes
model.load_state_dict(torch.load('resnet18_cifar10.pth'))
model.eval()

# Image preprocessing (use the same transforms as during training)
preprocess = transforms.Compose([
    transforms.Resize(256), # Or whatever input size your model expects
    transforms.CenterCrop(224), # Or input crop size
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # ImageNet normalization
])

# Load and preprocess an example image (replace 'your_image.jpg')
image_path = 'your_image.jpg'
img = Image.open(image_path)
img_t = preprocess(img)
batch_t = torch.unsqueeze(img_t, 0)

# Inference
with torch.no_grad():
    output = model(batch_t)
    probabilities = torch.nn.functional.softmax(output[0], dim=0)
    _, predicted_class = torch.max(probabilities, 0)
    # ... (Code to map predicted_class index to class name if you have class names) ...
    print(f"Predicted class index: {predicted_class.item()}")
    print(f"Probabilities: {probabilities.tolist()}")