---
license: mit
task_categories:
  - object-detection
tags:
  - robotics
  - simulation
  - atec2026
  - yolo
size_categories:
  - 1K<n<10K
---

# ATEC2026 Object Detection Dataset

Simulated RGB images with YOLO-format bounding box labels for the ATEC2026 Simulation Challenge.

## Dataset Details

- **Total images**: 1000 (850 train / 150 val)
- **Image size**: 640x480
- **Classes**: banana, box (sugar), mustard
- **Cameras**: ee_camera, head_camera
- **Annotation format**: YOLO (normalized center_x, center_y, width, height)

## Usage

```python
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
model.train(data="yolo_dataset/dataset.yaml", epochs=100)
```

## Directory Structure

```
├── metadata.json          # Dataset statistics and config
├── images/
│   ├── train/             # 850 training images
│   └── val/               # 150 validation images
├── yolo_dataset/
│   ├── dataset.yaml       # YOLO dataset config
│   └── labels/            # YOLO format labels
└── label/                 # Raw label archive
```