Update README.md

README.md

@@ -103,6 +103,77 @@ The stored epoch is zero-indexed, so `epoch = 99` corresponds to the completion
 
 The checkpoint includes training state such as optimizer and scheduler information in addition to model parameters.
 
+## Loading the Checkpoint
+
+The checkpoint can be loaded by reconstructing the ResNet-18 backbone and SimCLR projection head used during training.
+
+```python
+import torch
+import torch.nn as nn
+import torchvision
+from lightly.models.modules import heads
+
+
+class SimCLRResNet18(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        resnet = torchvision.models.resnet18(weights=None)
+        feature_dim = resnet.fc.in_features  # 512
+
+        # Remove the classification head
+        self.backbone = nn.Sequential(
+            *list(resnet.children())[:-1]
+        )
+
+        # SimCLR projection head: 512 -> 512 -> 128
+        self.projection_head = heads.SimCLRProjectionHead(
+            feature_dim,
+            feature_dim,
+            128,
+        )
+
+    def forward(self, x):
+        features = self.backbone(x).flatten(start_dim=1)
+        projections = self.projection_head(features)
+        return projections
+
+
+checkpoint = torch.load(
+    "checkpoint_100-resnet18-simclr-imagenet1k.ckpt",
+    map_location="cpu",
+    weights_only=False,
+)
+
+model = SimCLRResNet18()
+model.load_state_dict(checkpoint["state_dict"], strict=True)
+model.eval()
+```
+
+### Extracting Backbone Features
+
+For most downstream applications, the 512-dimensional ResNet-18 representation can be extracted without using the SimCLR projection head:
+
+```python
+with torch.no_grad():
+    features = model.backbone(images).flatten(start_dim=1)
+
+print(features.shape)
+# [batch_size, 512]
+```
+
+The 128-dimensional SimCLR projection can instead be obtained with:
+
+```python
+with torch.no_grad():
+    projections = model(images)
+
+print(projections.shape)
+# [batch_size, 128]
+```
+
+Input tensors should have shape `[batch_size, 3, 224, 224]`.
+
 ### Comparative Evaluation Results
 
 The table below reproduces the summary metrics reported in the associated paper across all VEDB-trained conditions and **reference models**. **Rows corresponding to this repository's ImageNet-1K checkpoint are bolded.**
@@ -177,4 +248,14 @@ If you use this checkpoint or representations derived from it in academic work,
   address   = {New York, NY, USA},
   year      = {2026},
   doi       = {10.32470/0416gfsq}
-}
+}
+```
+
+**Proceedings:** [Diaz & Henderson (2026)](https://doi.org/10.32470/0416gfsq)<br>
+**Preprint:** [arXiv:2607.19316](https://arxiv.org/abs/2607.19316)
+
+## License
+
+The released checkpoint and repository materials are provided under the **Apache License 2.0**.
+
+The ImageNet-1K training dataset and third-party software used to produce the model remain subject to their respective licenses, access requirements, and terms of use.