Update README.md

README.md

@@ -185,31 +185,54 @@ MatSynth is accessible through the datasets python library.
 Following a usage example:
 
 ```python
+import torchvision.transforms.functional as TF
 from datasets import load_dataset
 from torch.utils.data import DataLoader
 
+# image processing function
+def process_img(x):
+    x = TF.resize(x, (1024, 1024))
+    x = TF.to_tensor(x)
+    return x
+
+# item processing function
+def process_batch(examples):
+    examples["basecolor"] = [process_img(x) for x in examples["basecolor"]]
+    return examples
+
 # load the dataset in streaming mode
 ds = load_dataset(
     "gvecchio/MatSynth", 
     streaming = True,
 )
 
-# remove unnecessary columns to reduce downloaded data
+# remove unwanted columns
 ds = ds.remove_columns(["diffuse", "specular", "displacement", "opacity", "blend_mask"])
-# keep only specified columns
-ds = ds.select_columns(["metadata", "basecolor", "normal", "roughness", "metallic"])
+# or keep only specified columns
+ds = ds.select_columns(["metadata", "basecolor"])
+
+# shuffle data
+ds = ds.shuffle(buffer_size=100)
 
 # filter data matching a specific criteria, e.g.: only CC0 materials
 ds = ds.filter(lambda x: x["metadata"]["license"] == "CC0")
+# filter out data from Deschaintre et al. 2018
+ds = ds.filter(lambda x: x["metadata"]["source"] != "deschaintre_2020")
 
-# shuffle data
-ds = ds.shuffle(buffer_size=100)
+# Set up processing
+ds = ds.map(process_batch, batched=True, batch_size=8)
 
 # set format for usage in torch
 ds = ds.with_format("torch")
-dl = DataLoader(ds["test"], batch_size=8)
+
+# iterate over the dataset
+for x in ds:
+    print(x)
+
 ```
 
+ ⚠️ **Note**: Streaming can be slow. We strongly suggest to cache data locally. 
+
 ## 📜 Citation
 
 ```