# FAQ This FAQ addresses common questions about creating high-quality datasets for object detection models, with specific focus on state-based detection (like Door Open/Closed) in real-world environments. Learn best practices for data collection, labeling, and avoiding common pitfalls that can impact model performance. *Example: Door Open / Door Closed Detection in Schools* *** #### Q: What kinds of images should we include in our dataset? Your dataset should reflect the environments where your model will actually run. For a Door Open/Closed model in schools, that means: **Include:** * School hallways, classrooms, gymnasiums, entrances, restrooms, janitorial spaces * Images taken from realistic camera placements (wall cameras, ceiling cameras, door-frame cameras) * Variations in lighting, angle, and usage (doors ajar, propped open, partially blocked) **Avoid:** homes, garages, random internet images, architectural photos irrelevant to the target setting. #### Q: What types of doors should we include? Include only *doors or doorways relevant to your use case*. For schools, that **excludes:** ornate residential doors, garage doors, barn doors, purely decorative doors, or gates. **Focus on:** * Standard swinging doors * Push bar doors * Double doors * Doors with windows * Entryways and propped doors *** #### Q: How should we handle images that contain no doors? Include a *reasonable number* of negatives (images with no doors) that reflect the operational setting (school hallways, walls, entryways). **Do not include** irrelevant negatives (bedrooms, outdoor scenes, kitchens, etc.). **Balance:** \~20-25% negatives is typically enough. Too many will skew model behavior. #### Q: How do I upload negative images? **Step-by-step process:** 1. **Prepare your negative images folder** * Create a folder named exactly `negatives` (lowercase, plural) * Add approximately 100 images that do NOT contain your target object 2. **Navigate to upload** * Go to the **Models** tab in the sidebar * Find your dataset and click the **three dots (...)** menu * Select **Upload** 3. **Upload the folder** * Drag and drop your entire `negatives` folder onto the "add more images" tile * The system will automatically recognize this as negative training data *** Q: How precise do our bounding boxes need to be? Bounding boxes should be **tight around the door edges or doorway edges**. Loose boxes can confuse the model, especially when subtle cues like open/closed gaps are important. **Tip:** Propped doors and open doorways should be consistently handled — include guidance for your labelers. #### Q: What is the definition of "door" vs. "doorway"? You should define this clearly for your team: * Is an empty doorway without a physical door considered "open"? * Is a glass door treated differently? * How do you handle doors with windows, or doors partially blocked? **Answer these questions in a shared Labeling Guide to ensure consistency.** #### Q: What is the risk of missing labels? If an image contains a door of interest that is not labeled, the model may treat it as a negative — this can lead to severe failure modes. **Recommendation:** Carefully audit your dataset and label *every instance* of the object in scope, or remove the image. *** #### Q: How diverse should our data be? Diversity is good — but *within your use case*. You want: * Different lighting conditions * Different door types within your target environment * Occlusions (people passing through, signs on doors) * Partial views of doors **Avoid** artificial diversity from irrelevant sources (non-school doors, home interiors, stock photos). #### Q: How many images do we need? It depends on your deployment goals, but a general rule: * **500-1000 images per class** as a starting point * More is better if your environment has high variability * Quality matters more than quantity — a smaller, well-curated dataset will outperform a large, noisy one *** ### Common Mistakes & Best Practices #### Q: What common mistakes should we avoid? **Top mistakes that hurt model performance:** * Including doors that won't be seen in production (wrong door types) * Loose or imprecise bounding boxes * Poor balance of negative images * Missing labels for objects present in images * Too much "diverse" data that is not actually relevant (houses, internet photos) * Inconsistent definitions of what constitutes a door or an open/closed state #### Q: What's the most important step before training? **Conduct a dataset audit pass** — this step dramatically improves model quality. Review your dataset for: * Consistent labeling across all images * Proper representation of your target environment * Correct balance of positive and negative examples * Quality and relevance of all included images **Final Tip:** EyePop.ai offers tools to help with dataset review and validation before training begins. *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.eyepop.ai/developer-documentation/faq.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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