# Deep Dives (FAQ)

**Good Negative Examples: What They Are and Why They Matter**

When collecting data for your computer vision model on EyePop.ai, it’s important to include **negative examples** in your dataset. Negative examples are images that **do not contain the object or feature you want the model to detect or classify.** These examples are critical for training a robust model that minimizes false positives.

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**1. What Are Good Negative Examples?**

* **Definition:** Images that lack the object(s) or feature(s) you are training your model to detect.
* **Purpose:** Teach the model to distinguish between relevant and irrelevant features.
* **Characteristics of Good Negative Examples:**
  * Represent the same environment or context where your target object might appear.
  * Include objects or features that could be mistaken for the target.
  * Have similar lighting, angles, or backgrounds as your positive examples.

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**2. Why Are Negative Examples Important?**

* **Reduce False Positives:** Helps the model avoid misidentifying unrelated objects as the target.
* **Improve Generalization:** Ensures the model performs well on real-world data by understanding both relevant and irrelevant cases.
* **Enhance Model Confidence:** Strengthens the model’s ability to differentiate between target and non-target data.

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**3. Examples of Good Negative Examples**

* **Context-Dependent Negatives:**
  * For a model detecting dogs, include images of other animals (e.g., cats, foxes) in similar settings.
  * For detecting medical sample bags, include images of hands, tables, and other lab equipment without the bags.
* **Similar Backgrounds Without the Target Object:**
  * Street scenes without cars for a vehicle detection model.
  * Office settings without laptops for a laptop detection model.
* **Challenging Distractors:**
  * Objects or patterns that resemble the target but are not.
  * Shadows, reflections, or partial objects that might confuse the model.

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**4. Common Mistakes in Choosing Negative Examples**

* **Irrelevant Negatives:**
  * Images that are completely unrelated to your target domain (e.g., beach scenes for a warehouse inventory model).
* **Overly Simplistic Examples:**
  * Plain backgrounds or empty images that don’t reflect the operational context.
* **Lack of Diversity:**
  * Using only a narrow set of negatives that don’t cover the variety of distractions your model may encounter.

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**5. Tips for Collecting High-Quality Negative Examples**

* Use the same sources as your positive examples for consistency.
* Ensure a mix of backgrounds, objects, and lighting conditions.
* Include ambiguous cases to challenge the model (e.g., objects partially occluded or in unusual orientations).

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6. **If not all the pictures have been fully trained (i.e., don’t have the green check box), can we still train the model, or will that cause issues?** 

* Yes, you can start training whenever you'd like. Any un-labelled images are ignored.

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7. **After images are uploaded to Eyepop.ai and someone has arranged the boxes over the desired objects for only a portion of the total uploaded images (e.g., 3,000 images uploaded, but only 500 are boxed), can the AI be re-trained on just those 500 boxed images to improve its accuracy when reviewing the remaining 2,500?** 

* Yes, as soon as you finish training a model we automatically re-autolabel all the images to make the remaining easier to label.

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8. **Can two people be logged in and working on labelling at the same time?** 

* Yes, just be careful to make sure two people aren't working on the same image simultaneously.