docs: Update README

README.md

@@ -1,3 +1,63 @@
----
-license: bsd-3-clause
----
+---
+license: bsd-3-clause
+base_model:
+- microsoft/resnet-50
+pipeline_tag: image-feature-extraction
+---
+
+# ResNet-50 Embeddings Only
+
+This is a modified version of a standard ResNet-50 architecture, where the final, fully connected layer that does the classification, has been removed.
+
+This effectively gives you the embeddings.
+
+NB: You may want to flatten the embeddings, as it'll be of shape `(1, 20248, 1, 1)` otherwise.
+
+# Example
+
+```python
+import onnxruntime
+from PIL import Image
+from torchvision import transforms
+
+
+def load_and_preprocess_image(image_path):
+    # Define the same preprocessing as used in training
+    preprocess = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+
+    # Open the image file
+    img = Image.open(image_path)
+
+    # Preprocess the image
+    img_preprocessed = preprocess(img)
+
+    # Add batch dimension
+    return img_preprocessed.unsqueeze(0).numpy()
+
+
+onnx_model_path = "resnet50_embeddings.onnx"
+
+session = onnxruntime.InferenceSession(onnx_model_path)
+
+input_name = session.get_inputs()[0].name
+
+# Load and preprocess an image (replace with your image path)
+image_path = "disco-ball.jpg"
+input_data = load_and_preprocess_image(image_path)
+
+# Run inference
+outputs = session.run(None, {input_name: input_data})
+
+# The output should be a single tensor (the embeddings)
+embeddings = outputs[0]
+
+# Flatten the embeddings
+embeddings = embeddings.reshape(embeddings.shape[0], -1)
+```