Updated README.md for image decode changes

README.md

@@ -33,7 +33,7 @@ dataset_info:
   - name: images
     list:
       image:
-        decode: false
+        decode: true
   - name: id
     dtype: string
   - name: messages
@@ -52,13 +52,14 @@ dataset_info:
     dtype: string
   splits:
   - name: train
+    num_bytes: 47646918572
     num_examples: 535881
 - config_name: test
   features:
   - name: images
     list:
       image:
-        decode: false
+        decode: true
   - name: id
     dtype: string
   - name: messages
@@ -77,13 +78,14 @@ dataset_info:
     dtype: string
   splits:
   - name: test
+    num_bytes: 13564137567
     num_examples: 152921
 - config_name: validation
   features:
   - name: images
     list:
       image:
-        decode: false
+        decode: true
   - name: id
     dtype: string
   - name: messages
@@ -102,13 +104,14 @@ dataset_info:
     dtype: string
   splits:
   - name: validation
+    num_bytes: 6913653907
     num_examples: 76384
 - config_name: default
   features:
   - name: images
     list:
       image:
-        decode: false
+        decode: true
   - name: id
     dtype: string
   - name: messages
@@ -127,11 +130,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
+    num_bytes: 47646918572
     num_examples: 535881
   - name: test
+    num_bytes: 13564137567
     num_examples: 152921
   - name: validation
+    num_bytes: 6913653907
     num_examples: 76384
+  download_size: 67867722531
+  dataset_size: 68124710046
 ---
 
 # PlantExpertVQA
@@ -197,17 +205,11 @@ ds = load_dataset("Project-AgML/PlantExpertVQA")   # equivalent to "default"
 
 first = ds["train"][0] if "train" in ds else ds[0]
 
-# Access an image — bytes are embedded; decode manually or via cast_column
-from datasets import Image
-img_bytes = first["images"][0]["bytes"]
+# Access an image — decoded to PIL automatically
+img = first["images"][0]
+img.show()
 ```
 
-> **Note:** images are stored with `decode=False` for efficient storage and transfer.
-> Cast the column to `Image(decode=True)` after loading if you want PIL objects directly:
-> ```python
-> ds = ds.cast_column("images", Image(decode=True))
-> ```
-
 ## Schema
 
 Every record shares the SAME columns so heterogeneous AgML datasets concatenate cleanly: