Apple on a shelf Detection Dataset
Generate AI-labeled apple detection images on a shelf. Ready for YOLO, COCO, and Pascal VOC — no manual labeling required.
How to generate a apple dataset
Describe your object
Enter "apple" as your target object and describe the environment: "on a shelf".
Choose format & quantity
Select YOLO, COCO, or Pascal VOC. Generate 10 to 5,000 images per batch.
Download & train
Get a .zip with images and auto-labeled bounding boxes. Ready for Ultralytics, PyTorch, or any framework.
What's in the dataset
Images
- AI-generated images of apple on a shelf
- Varied lighting, angles, and compositions
- High resolution suitable for model training
- 10 to 5,000 images per job
Labels
- Auto-generated bounding box annotations
- Available in YOLO (.txt), COCO (.json), or Pascal VOC (.xml)
- Python visualizer script included
- Failed labels automatically refunded
Use cases for apple detection
A apple detection dataset is useful for training object detection models that need to identify and locate apple instances on a shelf. Common applications include real-time monitoring, automated counting, safety compliance, quality inspection, and autonomous systems.
Using synthetic data lets you generate edge cases and rare scenarios that are difficult to capture in the real world. Need apple on a shelf at different times of day, weather conditions, or angles? AI generation gives you infinite variety without the cost of manual photography and labeling.
Pricing
- No subscriptions — prepaid wallet, pay only for what you generate
- Failed images and labels automatically refunded
- Minimum deposit: $5 (that's 50 images)
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