Preparing & Uploading Data
Overview
A good place to start when creating and training a new model is with a dataset containing at least 500 images. Include diverse lighting, angles, and contexts for real-world applicability. The dataset should also include negative images. Negative images do not include the target object, and are helpful to reduce false positives in your trained model. With a beginning dataset of 500 images, aim for 400 images (or 80%) showing your object, and 100 (20%) negative images.
What Are Negative Images?
Negative images are photos that do NOT contain your target object. These images are crucial for training your model to distinguish between what you want to detect and what you don't.
Why Use Negative Images?
Reduce false positives: Help your model learn what NOT to classify as your target object
Improve accuracy: Train the model to be more discriminating
Handle similar objects: Teach the model to distinguish between similar-looking items
Examples of Negative Images:
If training a door detection model: Include images of windows, archways, or openings that are NOT doors
If training a car detection model: Include images of trucks, motorcycles, or street scenes without cars
If training a person detection model: Include images of mannequins, statues, or scenes without people
How to Organize Your Dataset
Folder Structure
Organize your images into clear folder structures before uploading:
your-dataset/
├── positive-images/
│ ├── door_001.jpg
│ ├── door_002.jpg
│ └── ... (400 images of doors)
└── negatives/
├── window_001.jpg
├── archway_001.jpg
└── ... (100 images without doors)
Important: The folder containing negative images must be named "negatives" (all lowercase, plural).
How to Upload Negative Images
Step-by-Step Process:
Prepare your negative images folder
Create a folder named exactly
negatives
(lowercase, plural)Add approximately 100 images that do NOT contain your target object
Ensure images show similar contexts or potentially confusing objects
Navigate to the upload interface
Go to the Models tab in the sidebar
Find your Image Collection
Click the three dots (...) menu next to your dataset
Select Upload
Upload the negatives folder
On the upload screen, locate the "add more images" tile
Drag and drop or upload your entire
negatives
folder onto this tileThe system will automatically recognize this as negative training data
Upload Tips:
You can drag and drop the entire folder at once
The upload area will accept the folder and process all images inside
Make sure your folder is named exactly "negatives" for proper recognition
Best Practices for Negative Images
Quality Guidelines:
Similar contexts: Use images from similar environments where your target object might appear
Potential confusers: Include objects that might be mistaken for your target (like windows when training for doors)
Varied conditions: Include different lighting, angles, and backgrounds
High quality: Use clear, well-lit images similar to your positive examples
Common Mistakes to Avoid:
Don't use completely unrelated images (random landscapes for a door model)
Avoid low-quality or blurry negative images
Don't include images that partially show your target object
Don't forget to name the folder exactly "negatives"
Recommended Dataset Ratios
500 images
400 images
100 images
80/20
1000 images
800 images
200 images
80/20
2000+ images
1600+ images
400+ images
80/20
Frequently Asked Questions
Q: How do I upload negative images?
A: Create a folder named "negatives" with ~100 images that don't contain your target object. Go to Models → click the three dots (...) on your dataset → select Upload → drag and drop the "negatives" folder onto the "add more images" tile.
Q: What should negative images contain?
A: Images that are similar to where your target object appears, but without the target object itself. For example, if training a door model, include images of windows, archways, or wall sections that might be confused for doors.
Q: How many negative images do I need?
A: Start with about 20% of your total dataset as negative images. For a 500-image dataset, use approximately 100 negative images.
Q: Can I add negative images after initial training?
A: Yes, you can upload additional negative images to improve your model's performance and reduce false positives.
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