Added model usage and included number of trainable parameters

README.md

@@ -11,5 +11,39 @@ This is the repo for a custom-made neural network based on ResNet architecture t
 - **Architecture:** ResNet
 - **Input shape:** 3x32x32
 - **Output classes:** 10
+- **Parameters:** 272,474
 - **Dataset:** CIFAR-10
-> More details can be found in the `config.json` file inside this repository.
+
+More details can be found in the `config.json` file inside this repository and the original [git repository](https://github.com/spolivin/cifar10-website/blob/master/nn_dev/pytorch_models/architectures.py) from which the model originated.
+
+## Model usage
+
+Since the model for which the weights loaded in this repository are intended is a part of a custom Python package, one needs to firstly clone the project locally:
+
+```bash
+git clone https://github.com/spolivin/cifar10-website.git
+cd cifar10-website/nn_dev
+```
+
+Next, in a Python script we can make imports and load the weights:
+
+```python
+import torch
+
+from pytorch_models import resnet20
+
+# URL from which to load the weights
+URL = "https://huggingface.co/spolivin/cnn-cifar10/resolve/main/resnet20_weights.pth"
+
+# Building the ResNet-20 model
+resnet20_model = resnet20()
+
+# Loading the pretrained weights to the model
+resnet20_model.load_state_dict(
+        torch.hub.load_state_dict_from_url(
+            url=URL,
+            weights_only=True,
+            map_location=torch.device("cpu"),
+        )
+    )
+```

config.json

@@ -7,6 +7,7 @@
           ],
           "num_layers": 20,
           "num_classes": 10,
+          "num_trainable_parameters": 272474,
           "architecture": "ResNet"
      },
      "training_parameters": {
@@ -53,4 +54,4 @@
           "version": "v1.0",
           "purpose": "Image classification on CIFAR-10"
      }
-}
+}