Update README.md

README.md

@@ -1,20 +1,37 @@
 ---
-base_model:
-- resnet-18
+tags:
+- onnx
+- image-classification
+- cifar10
+- dropout
+- aidge
 pipeline_tag: image-classification
-license: cc
 datasets:
 - cifar10
-language:
-- en
 metrics:
 - accuracy
+model-index:
+- name: Custom ResNet-18 with integrated Dropout layers
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: CIFAR-10
+      type: cifar10
+    metrics:
+    - type: accuracy
+      value: 83.96%
+language:
+- en
+base_model:
+- resnet-18
 ---
-This model is designed to test the integration of a custom Dropout operator for the `Aidge platform` and to explore how predictive uncertainty varies between in-distribution (CIFAR-10) and out-of-distribution (GTSRB) samples. By analyzing the model’s predictive uncertainty across both datasets, we can evaluate how effectively the model distinguishes between familiar and unfamiliar inputs.
 
-## Model Details
+# Custom ResNet-18 with Integrated Dropout
+
+This is a **custom ResNet-18** model implemented in **PyTorch** with integrated **Dropout** layers. 
+It was trained on the **CIFAR-10** dataset for image classification tasks. The model has been exported to the **ONNX format** (opset version 15).
+and is fully compatible with the **Aidge** platform.
 
-- **Architecture**: Custom ResNet-18 with all convolutional and fully connected layers configured with `bias=True`
-- **Trained on**: CIFAR-10 
-- **ONNX opset version**: 11
-- **Conversion tool**: PyTorch → ONNX
+---