Update README.md

README.md

@@ -1,20 +1,53 @@
----
-license: cc-by-nc-4.0
-dataset_info:
-  features:
-  - name: image
-    dtype: string
-  - name: label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 17286021131
-    num_examples: 405055
-  download_size: 17266005314
-  dataset_size: 17286021131
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: cc-by-nc-4.0
+dataset_info:
+  features:
+  - name: image
+    dtype: string
+  - name: label
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 17286021131
+    num_examples: 405055
+  download_size: 17266005314
+  dataset_size: 17286021131
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+
+### Install datasets package
+First, make sure you have the datasets library installed. If not, you can install it using:
+
+```bash
+pip install datasets
+```
+
+### Load Dataset from Arrow File
+Download all arrow files to local_path. The follow is how to load arrow files and decode image:
+
+```python
+from datasets import load_from_disk
+from io import BytesIO
+import base64
+from PIL import Image
+import mmengine
+
+
+# Path to your Arrow dataset directory
+arrow_dataset_path = 'path_to_your_arrow_dataset_directory'
+
+# Load the dataset
+dataset = load_from_disk(arrow_dataset_path)
+cat_tree = mmengine.load('v3det_2023_v1_category_tree.json')
+
+# Each dataset entry is composed of an image in the format of base64 string and its corresponding imagenet label id
+# Here is an example of how to decode image, and convert imagenet label id to v3det class name
+# You can download v3det_2023_v1_category_tree.json here: https://v3det.openxlab.org.cn/download
+image = Image.open(BytesIO(base64.b64decode(dataset[0]['image'])))
+cat_name = cat_tree['id2name'][dataset[0]['label']]
+```