> ## Documentation Index
> Fetch the complete documentation index at: https://docs.visual-layer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Exploration Recipes

> Step-by-step guides for common dataset curation workflows, from defect detection to training set cleanup.

Effective dataset curation requires combining multiple search and filter tools in a specific sequence. These recipes provide step-by-step workflows for common platform outcomes, helping you achieve high-quality results efficiently.

## Recipe 1: Finding Diverse Examples of a Specific Pattern

**Objective:** Isolate a diverse set of examples for a specific visual pattern, ensuring you capture rare variations without filling the dataset with repetitive images.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="Manufacturing">
    Surface damage patterns for defect detection training
  </Card>

  <Card title="Medical Imaging">
    Specific pathology presentations across patient populations
  </Card>

  <Card title="Retail/Insurance">
    Product damage types for claims processing
  </Card>

  <Card title="Defense & Intelligence">
    Specific threat or anomaly patterns in surveillance data
  </Card>
</CardGroup>

<Steps>
  <Step title="Broad Search">
    Start with **Semantic Search**.

    * **Query:** Describe the pattern in natural language (e.g., "surface damage," "cracked glass," "skin lesion")
    * **Result:** This returns a broad set of candidates, likely including some irrelevant images (false positives).
  </Step>

  <Step title="Visual Refinement">
    Switch to **Visual Search**.

    * Find a clear, high-quality example of the specific pattern you want in the search results.
    * **Crop** the image to isolate just the pattern (excluding background or irrelevant context).
    * Run the visual search to find visually similar patterns.
  </Step>

  <Step title="Ensure Diversity">
    Apply the **Uniques Filter**.

    * Set the **Uniqueness Threshold** to **High**.
    * **Why:** This hides repetitive examples of the same common pattern, surfacing visually distinct variations and edge cases.
  </Step>

  <Step title="Final Polish">
    Clean up the selection.

    * Use **Duplicate Detection** to remove near-identical frames.
    * **Save as View** with a descriptive name (e.g., "Distinctive Scratch Patterns," "Rare Lesion Variants") for your labeling team.
  </Step>
</Steps>

## Recipe 2: Cleaning Raw Data for Labeling

**Objective:** Rapidly prepare a messy, raw dataset for labeling by removing low-quality data that would waste annotator time and budget.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="Manufacturing">
    Raw production line footage with lighting issues and redundant frames
  </Card>

  <Card title="Medical Imaging">
    Scans from multiple sources with varying quality standards
  </Card>

  <Card title="Retail">
    User-generated product photos with technical failures
  </Card>

  <Card title="Defense & Intelligence">
    Surveillance footage with motion blur and poor lighting
  </Card>

  <Card title="Research">
    Web-scraped images with inconsistent quality
  </Card>
</CardGroup>

<Steps>
  <Step title="Remove Technical Failures">
    Filter by **Quality Issues**.

    * Set **Blurry** to `IS NOT`.
    * Set **Dark** to `IS NOT`.
    * **Result:** Removes unreadable or low-information images immediately.
  </Step>

  <Step title="Remove Annotation Errors">
    Filter by **Mislabels**.

    * Set **Mislabels** to `IS NOT`.
    * **Result:** Excludes images where existing metadata likely conflicts with visual content, preventing bad ground truth from entering the pipeline.
  </Step>

  <Step title="Reduce Redundancy">
    Apply **Select Uniques**.

    * Set threshold to **Medium**.
    * **Result:** If the ingest contains burst-mode photos or video sequences, this keeps only representative frames, significantly reducing the total count sent to labeling.
  </Step>

  <Step title="Export for Labeling">
    * Select all remaining items.
    * **Export** the cleaned list to JSON/CSV to hand off to your annotation workforce.
  </Step>
</Steps>

## Recipe 3: Balancing Common Scenarios with Rare Edge Cases

**Objective:** Curate a dataset that captures both typical scenarios and rare edge cases while managing storage volumes efficiently.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="Autonomous Vehicles">
    Common driving conditions vs. rare weather, road signs, or obstacles
  </Card>

  <Card title="Manufacturing">
    Standard production vs. unusual failure modes or material variations
  </Card>

  <Card title="Medical Imaging">
    Common presentations vs. rare complications or co-morbidities
  </Card>

  <Card title="Defense & Intelligence">
    Normal activity patterns vs. anomalous events requiring investigation
  </Card>

  <Card title="Retail">
    Standard product views vs. unusual angles or lighting conditions
  </Card>
</CardGroup>

<Steps>
  <Step title="Reduce Storage Costs">
    Apply **Duplicate Detection**.

    * **Action:** Review duplicate clusters from video sequences or burst captures.
    * **Select:** Keep one representative frame per scenario.
    * **Result:** Often reduces dataset size by 30-40% without losing scenario coverage.
  </Step>

  <Step title="Surface Rare Cases">
    Apply the **Outliers Filter**.

    * **Action:** Sort by high confidence outliers.
    * **Result:** Surfaces rare variations that are critical for model robustness but easy to miss in manual review.
  </Step>

  <Step title="Categorize Challenging Conditions">
    Filter by **Quality Issues**.

    * **Filter:** `Dark` and `Bright`.
    * **Action:** Instead of deleting these, tag them with a descriptive name (e.g., "Challenging Lighting," "Low Visibility").
    * **Result:** Creates specific subsets for testing model performance in adverse conditions.
  </Step>

  <Step title="Validate Coverage">
    Use **Cluster View** to verify distribution.

    * Review cluster sizes to ensure no single scenario dominates the dataset.
    * Use **Select Uniques** within overrepresented clusters to balance the distribution.
  </Step>
</Steps>

## Recipe 4: Managing Large Visual Catalogs

**Objective:** Consolidate duplicate assets, enforce quality standards, and organize large collections of visual content.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="E-commerce">
    Multi-vendor product catalogs with duplicate stock photos
  </Card>

  <Card title="Real Estate">
    Property listings with redundant images from different agents
  </Card>

  <Card title="Manufacturing">
    Parts catalogs with multiple photos of the same component
  </Card>

  <Card title="Media/Creative">
    Stock photo libraries with similar compositions
  </Card>

  <Card title="Digital Asset Management">
    Corporate image libraries across departments
  </Card>
</CardGroup>

<Steps>
  <Step title="Consolidate Duplicate Assets">
    Apply **Duplicate Detection**.

    * **Scenario:** Multiple sources upload the same or nearly identical images.
    * **Action:** Identify duplicate groups and link them to a single master asset.
    * **Result:** Prevents search results from being flooded with identical or near-identical images.
  </Step>

  <Step title="Enforce Quality Standards">
    Filter by **Quality Issues**.

    * **Filter:** `Blurry` OR `Dark` OR `Bright`.
    * **Action:** Flag these images for review, replacement, or auto-rejection.
    * **Result:** Ensures only professional-quality images remain in the catalog.
  </Step>

  <Step title="Organize Unlabeled Content">
    Filter by **Labels**.

    * **Filter:** `Labels` IS `Unlabeled`.
    * **Action:** Isolate unlabeled content and use **Semantic Search** to bulk-select and categorize items (e.g., "red sneakers," "two-bedroom apartments," "hydraulic fittings").
  </Step>

  <Step title="Create Curated Collections">
    * Use **Save as View** to create themed collections (e.g., "Hero Images," "Seasonal Products," "Premium Listings").
    * Share views with relevant teams to ensure everyone works from the same quality-controlled subset.
  </Step>
</Steps>

## Recipe 5: Identifying Annotation Inconsistencies

**Objective:** Find and fix labeling errors or inconsistencies across your dataset to improve model training quality.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="Manufacturing">
    Mixed defect categories or mislabeled quality grades
  </Card>

  <Card title="Medical Imaging">
    Inconsistent diagnostic labels across radiologists
  </Card>

  <Card title="Retail">
    Product category errors or attribute mismatches
  </Card>

  <Card title="Defense & Intelligence">
    Misclassified threat levels or event types
  </Card>

  <Card title="Autonomous Vehicles">
    Inconsistent object classifications across annotators
  </Card>
</CardGroup>

<Steps>
  <Step title="Find Visual-Label Mismatches">
    Apply the **Mislabels Filter**.

    * **Action:** Sort by high confidence mislabels.
    * **Result:** Surfaces images where the visual content doesn't align with the assigned label.
  </Step>

  <Step title="Review Class Outliers">
    Apply the **Outliers Filter** and filter by specific labels.

    * **Action:** Review images flagged as outliers within their assigned class.
    * **Result:** Finds images that are technically correct but visually anomalous for that category (e.g., drawings in a photo dataset).
  </Step>

  <Step title="Validate with Visual Search">
    Select a flagged image and run **Visual Search**.

    * **Action:** See what other images visually match this item.
    * **Result:** If all visual matches have a different label, this confirms a likely mislabel.
  </Step>

  <Step title="Bulk Correction">
    * Tag all confirmed errors with "Needs Relabeling."
    * **Export** this view to CSV/JSON for your annotation team to correct.
    * Track corrections by saving views before and after relabeling.
  </Step>
</Steps>

## Recipe 6: Creating Balanced Training Sets

**Objective:** Build a training dataset with appropriate class distribution and representation across important variations.

**Example scenarios:**

<CardGroup cols={2}>
  <Card title="Manufacturing">
    Equal representation of defect types and severity levels
  </Card>

  <Card title="Medical Imaging">
    Balanced demographics and presentation variations
  </Card>

  <Card title="Retail">
    Proportional product categories and seasonal coverage
  </Card>

  <Card title="Defense & Intelligence">
    Representative samples of normal and anomalous events
  </Card>

  <Card title="Autonomous Vehicles">
    Balanced weather, lighting, and scenario types
  </Card>
</CardGroup>

<Steps>
  <Step title="Assess Current Distribution">
    Use **Cluster View** and group by labels.

    * **Action:** Review the distribution of images across classes.
    * **Result:** Identify overrepresented and underrepresented categories.
  </Step>

  <Step title="Reduce Overrepresented Classes">
    For dominant classes, apply **Select Uniques**.

    * Set threshold to **High** to keep only the most distinctive examples.
    * **Result:** Reduces redundancy while preserving diversity within that class.
  </Step>

  <Step title="Augment Underrepresented Classes">
    For rare classes, use **Semantic Search** to find more examples.

    * **Query:** Describe the underrepresented category in detail.
    * Review results and tag valid examples to expand that class.
  </Step>

  <Step title="Validate Diversity">
    Within each class, check cluster distribution.

    * Use **Visual Search** from different cluster centers to ensure visual variety.
    * Apply **Select Uniques** to prevent any single visual pattern from dominating.
  </Step>

  <Step title="Export Balanced Set">
    * Save the final balanced distribution as a view.
    * **Export** with stratified sampling to maintain proportions in train/validation splits.
  </Step>
</Steps>

## Additional Tips for Recipe Success

### Combine Filters Strategically

Most recipes work best when you apply filters in a specific order:

1. **Start broad** with semantic or visual search to establish scope.
2. **Remove obvious problems** with quality filters early.
3. **Refine for diversity** with uniqueness and outlier filters.
4. **Final polish** with duplicate detection and targeted tagging.

### Save Intermediate Steps

Save views at each major step in your recipe:

* Enables you to backtrack if a filter removes too much.
* Creates audit trail for dataset curation decisions.
* Allows different team members to review at different stages.

### Iterate and Adjust

These recipes are starting points, not rigid procedures:

* Adjust thresholds based on your dataset characteristics.
* Add custom metadata filters for domain-specific criteria.
* Combine multiple recipes for complex curation workflows.

## Related Resources

<CardGroup cols={2}>
  <Card title="Exploring Datasets" icon="search" href="/docs/quick-start/dataset-exploration-ux">
    The core Find-Narrow-Refine workflow philosophy
  </Card>

  <Card title="Using Search & Filter" icon="sliders-horizontal" href="/docs/explore-and-search/using-search-filter">
    Detailed guide to every filter operator and option
  </Card>

  <Card title="Understanding Clusters" icon="grid-3x3" href="/docs/quick-start/understanding-clusters">
    How similarity clustering powers many recipes
  </Card>

  <Card title="Saved Views" icon="blend" href="/docs/collab-and-downstream/saved-views">
    Saving and sharing curated datasets
  </Card>
</CardGroup>