Update README.md

README.md

@@ -1,10 +1,53 @@
 ---
-license: apache-2.0
+language: en
+license: mit
+tags:
+- vision
+- image-classification
+- resnet
+- onnx
+- cifar10
+framework:
+- pytorch
+- onnx
 datasets:
-- uoft-cs/cifar10
-language:
-- en
-metrics:
-- accuracy
-pipeline_tag: image-classification
----
+- cifar10
+---
+
+# ResNet-18 trained on CIFAR-10 (ONNX)
+
+This is a ResNet-18 model trained on the CIFAR-10 dataset, exported to the **ONNX** format for easy deployment across different platforms.
+
+## Model Details
+- **Architecture:** ResNet-18 (modified for CIFAR-10 input size)
+- **Framework:** PyTorch → ONNX export
+- **Input size:** `3 × 224 × 224` RGB images
+- **Number of classes:** 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
+
+## Intended Use
+This model is designed for educational purposes, demos, and quick prototyping of ONNX-based image classification workflows.
+
+## How to Use
+
+```python
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+# Load model
+session = ort.InferenceSession("resnet18_cifar10.onnx")
+
+# Preprocess image
+def preprocess(img_path):
+    img = Image.open(img_path).convert("RGB").resize((32, 32))
+    img_data = np.array(img).astype(np.float32) / 255.0
+    img_data = np.transpose(img_data, (2, 0, 1))  # CHW format
+    img_data = np.expand_dims(img_data, axis=0)   # Batch dimension
+    return img_data
+
+input_data = preprocess("example.jpg")
+
+# Run inference
+outputs = session.run(None, {"input": input_data})
+pred_class = np.argmax(outputs[0])
+print("Predicted class:", pred_class)