README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: sample_id
    dtype: string
  - name: layers
    list:
      image:
        decode: false
  - name: preview
    dtype:
      image:
        decode: false
  - name: rendered
    dtype:
      image:
        decode: false
  - name: boundingbox
    struct:
    - name: format
      dtype: string
    - name: boxes
      list:
        list: float32
    - name: meta
      dtype: string
  splits:
  - name: train
    num_bytes: 32521424020
    num_examples: 19479
  download_size: 31847217475
  dataset_size: 32521424020
---
---
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
pretty_name: DLCV Final Dataset
size_categories:
- medium
---

# DLCV Final Dataset

This dataset is used for the **Deep Learning for Computer Vision (DLCV) final project**.  
It contains ground-truth layers organized per sample and is designed for training and evaluating computer vision models.

---

## 📂 Dataset Structure

The dataset is organized as follows:


dlcv_final/

├── gt_layers/

│ ├── sample_0000/

│ │ ├── layer_0.png

│ │ ├── layer_1.png

│ │ └── ...

│ ├── sample_0001/

│ ├── sample_0002/

│ └── ...

└── README.md

- Each `sample_xxxx` directory corresponds to **one data sample**
- Files inside each sample directory represent **ground-truth layers**
- Folder structure is preserved to simplify indexing and loading

---

## 🚀 How to Use

You can access this dataset using the 🤗 `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("dereklin1205/dlcv_final_dataset")