README.md

metadata

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

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