Update README.md

README.md

@@ -40,7 +40,61 @@ datasets:
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+```python
+import numpy as np
+from datasets import load_dataset
+from matplotlib import cm
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModel
+
+model = AutoModel.from_pretrained("RGBD-SOD/bbsnet", trust_remote_code=True)
+image_processor = AutoImageProcessor.from_pretrained(
+    "RGBD-SOD/bbsnet", trust_remote_code=True
+)
+dataset = load_dataset("RGBD-SOD/test", "v1", split="train", cache_dir="data")
+
+index = 0
+
+"""
+Get a specific sample from the dataset
+
+sample = {
+    'depth': <PIL.PngImagePlugin.PngImageFile image mode=L size=640x360>, 
+    'rgb': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x360>, 
+    'gt': <PIL.PngImagePlugin.PngImageFile image mode=L size=640x360>, 
+    'name': 'COME_Train_5'
+}
+"""
+sample = dataset[index]
+
+
+"""
+preprocessed_sample = {
+    'rgb': tensor([[[[-0.8507, ....0365]]]]), 
+    'gt': tensor([[[[0., 0., 0...., 0.]]]]), 
+    'depth': tensor([[[[0.9529, 0....3490]]]])
+}
+"""
+preprocessed_sample = image_processor.preprocess(sample)
+
+"""
+output = {
+    'logits': tensor([[[[-5.1966, ...ackward0>)
+}
+"""
+output = model(preprocessed_sample["rgb"], preprocessed_sample["depth"])
+
+postprocessed_sample: np.ndarray = image_processor.postprocess(
+    output["logits"], [sample["gt"].size[1], sample["gt"].size[0]]
+)
+prediction = Image.fromarray(np.uint8(cm.gist_earth(postprocessed_sample) * 255))
+
+"""
+Show the predicted salient map and the corresponding ground-truth(GT)
+"""
+prediction.show()
+sample["gt"].show()
+```
 
 ### Downstream Use [optional]