---
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']]
```