Annotate For VQA

Format: "How many [objects] are visible?"

Counting questions teach your model to quantify objects or features in images. These are essential for inventory, quality control, and monitoring applications.

Examples:

• "How many defects are present?" → "3"
• "How many people are in the image?" → "7"
• "How many red items are on the shelf?" → "12"
• "How many complete products are visible?" → "5"
• "How many safety violations are shown?" → "0"

Answer format: Use numbers only ("3", "0", "15")

Best practices:

• Be specific about what to count
• Use "0" for none, not "None" or "Zero"
• For large numbers, ensure accuracy by counting carefully
• Consider combining with presence/absence questions for validation

See Write effective questions for clarity guidelines.

Format: "Is [condition] true?"

Yes/No questions are simple binary classifications that help models make quick decisions. These are ideal for quality control, compliance checks, and condition assessment.

Examples:

• "Is the product damaged?" → "Yes"
• "Is the workspace clean?" → "No"
• "Is protective equipment worn?" → "Yes"
• "Is packaging intact?" → "Yes"
• "Is there visible rust?" → "No"

Answer format: Use "Yes" or "No" consistently (not "yes"/"no", "Y"/"N", or "True"/"False")

Best practices:

• Make questions unambiguous — avoid "maybe" situations
• Phrase positively when possible ("Is X present?" vs. "Is X absent?")
• Keep questions focused on one condition
• Add multiple questions for complex scenarios

Pair with attribute questions for more detailed information.

Format: "What [attribute] is the [object]?"

Attribute questions help models identify specific characteristics like color, size, shape, material, or condition. These are valuable for detailed object description and classification.

Examples:

• "What color is the vehicle?" → "Blue"
• "What size is the defect?" → "Large"
• "What material is the surface?" → "Metal"
• "What shape is the container?" → "Rectangular"
• "What is the lighting condition?" → "Bright"

Answer format: Single word or short phrase ("Blue", "Large", "Metal")

Common attribute types:

• Color: "Red", "Blue", "Green", "Silver"
• Size: "Small", "Medium", "Large"
• Shape: "Round", "Square", "Irregular"
• Material: "Metal", "Plastic", "Wood", "Glass"
• Condition: "New", "Worn", "Damaged"

Best practices:

• Use consistent vocabulary across your dataset
• Define size categories clearly for your team (see collaborative annotation)
• Standardize color names (e.g., always "Blue" not "Light blue" or "Navy")
• See Write effective questions for consistency guidelines

Format: "What type of [object] is this?"

Category questions teach models to classify objects or conditions into predefined groups. These are essential for sorting, routing, and decision-making applications.

Examples:

• "What type of defect is visible?" → "Scratch"
• "What category does this product belong to?" → "Electronics"
• "What kind of damage occurred?" → "Water damage"
• "What vehicle type is shown?" → "Sedan"
• "What equipment is this?" → "Forklift"

Answer format: Category name ("Scratch", "Electronics", "Water damage")

Best practices:

• Define your categories clearly before annotating
• Use mutually exclusive categories when possible
• Maintain a category list for consistency
• For overlapping categories, use multiple questions

Category planning:

1. List all possible categories for your use case
2. Create examples of each category
3. Share with your team if annotating collaboratively
4. Review annotation statistics to ensure balanced coverage

Combine with Yes/No questions for hierarchical classification.

Format: "Is [object] present in the image?"

Presence/absence questions focus on detecting specific objects or features regardless of quantity or attributes. Similar to Yes/No questions but specifically about object existence.

Examples:

• "Is a safety helmet visible?" → "Yes"
• "Is packaging intact?" → "No"
• "Is the label present?" → "Yes"
• "Is there any text in the image?" → "Yes"
• "Is liquid leaking?" → "No"

Answer format: "Yes" or "No" consistently

When to use:

• Safety and compliance checks
• Quality control verification
• Completeness validation
• Component detection

Presence/Absence vs. Counting:

• Use presence/absence when you only care if something exists
• Use counting questions when quantity matters
• Combine both for comprehensive coverage

Example combination:

• "Is protective equipment visible?" → "Yes" (presence)
• "How many people are wearing helmets?" → "3" (counting)

See Write effective questions to ensure clarity between presence and counting.

Goal: Detect and classify defects for automated quality inspection

Question strategy:

1. Presence/Absence: "Is this product defective?" → "Yes"/"No"
2. Counting: "How many defects are visible?" → "2"
3. Category: "What type of defect is this?" → "Scratch"
4. Attribute: "What is the defect severity?" → "Minor"
5. Yes/No: "Should this be rejected?" → "Yes"

Why this works: Covers detection → classification → decision-making workflow

Goal: Track products, stock levels, and planogram compliance

Question strategy:

1. Counting: "How many items are on the shelf?" → "15"
2. Category: "What product category is displayed?" → "Beverages"
3. Presence/Absence: "Is the price tag visible?" → "Yes"
4. Yes/No: "Is the shelf fully stocked?" → "No"
5. Attribute: "What is the stock level?" → "Low"

Why this works: Enables inventory tracking and compliance verification

Goal: Verify safety equipment usage and workplace compliance

Question strategy:

1. Counting: "How many people are in the workspace?" → "4"
2. Counting: "How many people are wearing helmets?" → "3"
3. Presence/Absence: "Is safety signage visible?" → "Yes"
4. Yes/No: "Is the area compliant?" → "No"
5. Category: "What type of violation is shown?" → "Missing PPE"

Why this works: Combines counting with compliance assessment

Goal: Assess crop health, growth stage, and pest detection

Question strategy:

1. Attribute: "What is the crop condition?" → "Healthy"
2. Presence/Absence: "Are pests visible?" → "No"
3. Category: "What growth stage is shown?" → "Flowering"
4. Yes/No: "Does the crop need irrigation?" → "Yes"
5. Attribute: "What is the canopy color?" → "Green"

Why this works: Monitors multiple health indicators simultaneously

Possible causes and solutions:

1. Network connectivity — Check your internet connection. Annotations require an active connection to save.

2. Session timeout — If you've been idle, refresh the page and reopen the annotator.

3. Browser issues — Clear cache or try a different browser. Supported browsers: Chrome, Firefox, Safari, Edge.

4. Form incomplete — Ensure both Question and Answer fields are filled before clicking Add.

Verify your annotations saved:

• Check annotation statistics in the dataset Explorer
• Filter annotated images to see which images have annotations

Solution steps:

1. Navigate to your dataset from the main Datasets page

2. Click the Annotator tab in the left sidebar (see Open the annotator)

3. If the tab is missing or annotator won't open, verify:

   • You have uploaded images to the dataset
   • You have permission to annotate (check with your organization admin)
   • Your dataset type supports VQA annotations

The annotator interface shows your image on the left, question-answer pairs on the right, and thumbnails at the bottom.

Performance optimization:

1. Large images — Images over 10 MB may load slowly. Upload optimized images when possible.

2. Browser resources — Close unnecessary tabs and applications.

3. Use keyboard shortcuts — Keyboard navigation is faster than mouse clicks.

4. Batch workflow — Annotate similar images together to reduce context switching.

Recovery steps:

1. Check the image — Ensure you're viewing the correct image. Check the bottom thumbnail strip to confirm which image is selected.

2. Check the right panel — Question-answer pairs appear in the Visual Question Answering panel on the right side. Scroll down if you have many annotations.

3. Check filters — Remove filters that might be hiding annotations.

4. Review edit history — If annotations were deleted, they cannot be recovered. Re-annotate the image.

5. Browser cache — Try refreshing the page or clearing your browser cache.

Prevention: Use annotation statistics to regularly verify your progress. The bottom thumbnail strip shows "Annotated" badges on images with question-answer pairs.

Unfortunately, deletions cannot be undone. Best practices to prevent this:

1. Double-check before deleting — Review annotations carefully before clicking the three-dot menu and selecting Delete

2. Use edit instead — Edit annotations rather than deleting and recreating (edits save automatically)

3. Export backups — Regularly download your dataset as a backup

4. Team coordination — If annotating collaboratively, communicate before bulk deletions

Special characters are supported:

• Standard punctuation — Question marks, commas, periods work normally
• Numbers — Use digits for counting questions ("3", not "three")
• Unicode — Non-English characters and symbols are supported
• Avoid — Excessive special characters may confuse the model

Best practice: Keep answers concise with minimal special characters for better model training.

It depends on your use case:

Same questions — Best when you want the model to answer consistent questions across different images (e.g., "Is this product defective?")

Varied questions — Best when training a general-purpose VQA model that needs to handle diverse questions

Recommended approach: Use a core set of questions for all images, plus additional specific questions where relevant.

Review Question types to include to plan your annotation strategy and Write effective questions for consistency guidelines.

Keep answers concise:

• ✅ Good: "3", "Red", "Damaged", "Top-left corner"
• ❌ Too detailed: "There are three defects visible on the surface, including two scratches and one dent located near the edge"

Why shorter is better:

• Easier for the model to learn
• Faster to annotate
• More consistent across annotators
• Better training results

Monitor answer length:

Use the word and character count displayed on the bottom right of the annotator:

• Click any QA pair to see its individual word count
• Compare counts across your annotations to ensure consistency
• Aim for answers with fewer than 5 words when possible

For specific formats by question type, see Question types to include.

Yes! It's fine (and often correct) to have the same answer for different questions:

• Q: "Is the product damaged?" → A: "Yes"
• Q: "Is this part acceptable?" → A: "No"
• Q: "Should this be rejected?" → A: "Yes"

The model learns to understand different questions that relate to the same visual information. This is common with Yes/No questions and Category questions.

You can add multiple questions to the same image with overlapping answers.

Best practices:

1. Skip unclear images — If you genuinely can't determine the answer, navigate to the next image
2. Use 'Unknown' — For questions where the answer isn't visible, you can use "Unknown" or "Not visible" consistently across your dataset
3. Refine your questions — If many images are unclear, revisit your question writing strategy

Avoid guessing — Incorrect annotations hurt model performance more than missing annotations. Focus on effective questions that have clear, verifiable answers.

Yes! Multiple team members can annotate different images in the same dataset:

• Add team members to your organization
• Assign different images to different annotators using filters
• Use consistent question formats and answer styles across your team
• Reference Question writing guidelines for consistency

Pro tip: Create annotation guidelines for your team based on Question types to include and Write effective questions to ensure consistency.

Track annotation progress across your team using dataset insights.

Yes! You can upload pre-existing VQA annotations:

• Upload VQA annotations in Vi JSONL format
• This is useful if you have annotations from other tools or datasets
• You can then edit or add to these annotations in the annotator

After importing, use Track annotation progress to verify your uploaded annotations.

Approach 1 — General questions:

• Q: "How many defects are visible?" → A: "3" (see Counting questions)
• Q: "What types of defects are present?" → A: "Scratches and dents" (see Category questions)

Approach 2 — Specific questions:

• Q: "Is there a scratch on the left side?" → A: "Yes" (see Yes/No questions)
• Q: "Is there a dent on the right side?" → A: "Yes" (see Presence/Absence questions)
• Q: "What is near the center?" → A: "Paint chip" (see Attribute questions)

Both approaches work — Choose based on your use case. You can add multiple questions per image to combine both approaches.

Review Write effective questions for guidance on clarity and consistency.

• Use the same phrasing for similar questions across images
• Standardize answer formats (e.g., always use "Yes"/"No", not "yes"/"no" or "Y"/"N")
• Create a style guide for your team if annotating collaboratively
• Review Question writing guidelines regularly

• Accurate annotations are more valuable than many low-quality ones
• Take time to write effective questions
• Review and edit annotations when you spot errors
• Use filters to review your work
• Monitor word and character counts (bottom right) to maintain consistent answer length

• Include various question types in your dataset
• Cover different scenarios and edge cases
• Add multiple questions per image when appropriate
• Balance question difficulty (easy, medium, challenging)

• Learn keyboard shortcuts to speed up annotation (use E and Q to navigate)
• Use navigation features to move quickly between images
• Click on QA pairs to check individual word counts and identify verbose answers
• Track your progress to stay motivated
• Take breaks to maintain annotation quality