> ## Documentation Index
> Fetch the complete documentation index at: https://docs.labellerr.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Magic Wand Filtering
> Filter and review pre-annotations by confidence levels using the intuitive slider control for efficient quality assurance workflows.
## Overview
The Magic Wand Confidence Filtering feature enables you to upload pre-annotations with confidence scores and dynamically filter them during review. This powerful tool helps your annotation team focus on reviewing predictions that need the most attention while maintaining efficient workflows.
Filter annotations by confidence levels in real-time with an interactive slider
Focus review efforts on low-confidence predictions that need human validation
Accept high-confidence predictions quickly while carefully reviewing uncertain ones
***
## How It Works
When you upload pre-annotations with confidence buckets, the Magic Wand feature automatically appears in the annotation interface, allowing annotators to filter annotations based on their confidence levels.
Upload your pre-annotations using the SDK with a specified confidence bucket parameter
The slider control automatically appears in the annotation interface for files with confidence-tagged annotations
Annotators use the slider to show/hide annotations based on confidence thresholds
Focus on reviewing lower-confidence predictions while quickly accepting high-confidence ones
***
## Visual Workflow
The slider control appears automatically when annotations are uploaded with confidence buckets. Drag the slider to show/hide annotations based on your selected confidence threshold.
***
## SDK Implementation
### Upload Pre-annotations with Confidence Bucket
**Use Case**: Model predictions with low certainty (\< 50% confidence) that require careful human review
```python theme={"dark"}
from labellerr.client import LabellerrClient
from labellerr.core.projects import LabellerrProject
# Initialize client
client = LabellerrClient(
api_key='your_api_key',
api_secret='your_api_secret',
client_id='your_client_id'
)
# Get project instance and upload with low confidence bucket
project = LabellerrProject(client=client, project_id='your_project_id')
result = project.upload_preannotations(
annotation_format='coco_json',
annotation_file='annotations.json',
conf_bucket='low', # Low confidence annotations
_async=False # Default: blocks until completion
)
print(f"Status: {result['response']['status']}")
```
**Best For**: Uncertain predictions, edge cases, or challenging images that need thorough human validation
**Use Case**: Model predictions with moderate certainty (50-80% confidence) that benefit from quick review
```python theme={"dark"}
from labellerr.client import LabellerrClient
from labellerr.core.projects import LabellerrProject
# Initialize client
client = LabellerrClient(
api_key='your_api_key',
api_secret='your_api_secret',
client_id='your_client_id'
)
# Get project instance and upload with medium confidence bucket
project = LabellerrProject(client=client, project_id='your_project_id')
result = project.upload_preannotations(
annotation_format='coco_json',
annotation_file='annotations.json',
conf_bucket='medium', # Medium confidence annotations
_async=False # Default: blocks until completion
)
print(f"Status: {result['response']['status']}")
```
**Best For**: Standard predictions that warrant verification but are likely accurate
**Use Case**: Model predictions with high certainty (> 80% confidence) that may only need spot-checking
```python theme={"dark"}
from labellerr.client import LabellerrClient
from labellerr.core.projects import LabellerrProject
# Initialize client
client = LabellerrClient(
api_key='your_api_key',
api_secret='your_api_secret',
client_id='your_client_id'
)
# Get project instance and upload with high confidence bucket
project = LabellerrProject(client=client, project_id='your_project_id')
result = project.upload_preannotations(
annotation_format='coco_json',
annotation_file='annotations.json',
conf_bucket='high', # High confidence annotations
_async=False # Default: blocks until completion
)
print(f"Status: {result['response']['status']}")
```
**Best For**: Highly confident predictions on clear, unambiguous objects that rarely need correction
***
## Complete Workflow Example
Here's a comprehensive example showing how to segment your model's predictions by confidence and upload them accordingly:
```python theme={"dark"}
import json
from labellerr.client import LabellerrClient
from labellerr.core.projects import LabellerrProject
from labellerr.core.exceptions import LabellerrError
# Initialize client
client = LabellerrClient(
api_key='your_api_key',
api_secret='your_api_secret',
client_id='your_client_id'
)
# Project configuration
project_id = 'your_project_id'
project = LabellerrProject(client=client, project_id=project_id)
# Separate your predictions by confidence (cumulative filtering)
# When slider is at Low: shows ALL annotations (low + medium + high)
# When slider is at Medium: shows medium + high annotations only
# When slider is at High: shows only high confidence annotations
prediction_files = {
'low': 'predictions_low_confidence.json', # < 0.5 confidence
'medium': 'predictions_medium_confidence.json', # >= 0.5 and <= 0.8 confidence
'high': 'predictions_high_confidence.json' # > 0.8 confidence
}
# Upload each confidence bucket
results = {}
for confidence_level, file_path in prediction_files.items():
try:
print(f"Uploading {confidence_level} confidence annotations...")
result = project.upload_preannotations(
annotation_format='coco_json',
annotation_file=file_path,
conf_bucket=confidence_level
)
results[confidence_level] = result
print(f"✓ {confidence_level.capitalize()} confidence upload complete")
# Extract metadata
metadata = result['response'].get('metadata', {})
if 'files_not_updated' in metadata:
print(f" Files processed: {metadata.get('files_not_updated', [])}")
except LabellerrError as e:
print(f"✗ Failed to upload {confidence_level} confidence: {str(e)}")
results[confidence_level] = {'error': str(e)}
# Summary
print("\n" + "="*50)
print("Upload Summary:")
print("="*50)
for level, result in results.items():
status = "Success" if 'error' not in result else "Failed"
print(f"{level.capitalize()}: {status}")
```
***
## Confidence Bucket Guidelines
Choose the appropriate confidence bucket based on your model's prediction confidence scores. The Magic Wand uses **cumulative filtering**, where each threshold level progressively filters annotations:
**How Cumulative Filtering Works**:
* **Low threshold** (slider at lowest): Shows ALL annotations (low + medium + high)
* **Medium threshold** (slider at middle): Shows medium + high annotations only (filters out low)
* **High threshold** (slider at highest): Shows only high confidence annotations (filters out low + medium)
**Confidence Range**: 0% - 50%
**Characteristics**:
* Uncertain predictions
* Multiple possible labels
* Challenging image conditions
* Edge cases
**Slider Behavior**:
* Visible at ALL slider positions
* Always shown regardless of threshold
* Requires thorough manual review
**Confidence Range**: 50% - 80%
**Characteristics**:
* Likely accurate predictions
* Some ambiguity present
* Standard image conditions
* Common object types
**Slider Behavior**:
* Visible at medium and high thresholds
* Hidden when slider filters to low only
* Quick verification recommended
**Confidence Range**: 80% - 100%
**Characteristics**:
* Very confident predictions
* Clear, unambiguous objects
* Ideal image conditions
* Well-trained categories
**Slider Behavior**:
* Visible only at high threshold
* Hidden at low and medium thresholds
* Minimal corrections needed
***
## Magic Wand Usage in Annotation Interface
Once annotations are uploaded with confidence buckets, annotators can leverage the Magic Wand slider:
1. Open a file with uploaded pre-annotations
2. Look for the Magic Wand icon/slider in the left sidebar
3. The tool appears automatically when confidence-tagged annotations exist
1. Drag the slider to adjust the confidence threshold
2. Annotations below the threshold are hidden
3. Annotations above the threshold remain visible
4. Use this to focus on reviewing specific confidence ranges
**Efficient Review Strategy**:
1. Start with low confidence threshold (show all annotations)
2. Review and correct low-confidence predictions first
3. Gradually increase threshold to review medium confidence
4. Quickly verify high-confidence predictions
5. Submit when all confidence levels are validated
***
## Parameter Reference
### `conf_bucket` Parameter
Specifies the confidence level for uploaded pre-annotations
**Valid Values**:
* `'low'` - For predictions with low confidence (typically \< 50%)
* `'medium'` - For predictions with medium confidence (typically 50-80%)
* `'high'` - For predictions with high confidence (typically > 80%)
**Default**: `None` (no confidence filtering applied)
The confidence bucket parameter is **case-sensitive**. Use lowercase values: `'low'`, `'medium'`, `'high'`
***
## Troubleshooting
**Symptom**: The slider control doesn't appear in the annotation interface.
**Resolution Steps**:
* Verify annotations were uploaded with `conf_bucket` parameter specified
* Confirm the confidence bucket value was set to 'low', 'medium', or 'high'
* Check that pre-annotations were successfully applied (no file name mismatches)
* Refresh the annotation interface
* Ensure you're using the latest version of Labellerr
**Symptom**: Slider doesn't filter anything; all annotations appear at same level.
**Resolution Steps**:
* Verify you uploaded different confidence buckets in separate uploads
* Check that you didn't upload all predictions with the same conf\_bucket value
* Ensure your prediction pipeline correctly segments by confidence
* Re-upload with properly categorized confidence levels
**Symptom**: Annotations appear in wrong confidence categories.
**Resolution Steps**:
* Review your confidence score thresholds in preprocessing
* Verify the mapping between confidence scores and bucket labels
* Check for any errors in your prediction segmentation logic
* Consider recalibrating your model if systematic misalignment exists
***
## Related Documentation
Complete guide to uploading pre-annotations via SDK
Set up projects programmatically for annotation workflows
Learn about Meta's SAM integration for one-click segmentation
***
## Support
For technical assistance with the Magic Wand Confidence Filtering feature, contact [support@tensormatics.com](mailto:support@tensormatics.com)
**Pro Tip**: Combine Magic Wand filtering with keyboard shortcuts for maximum annotation efficiency. Low-confidence annotations get thorough review, while high-confidence ones can be quickly validated and accepted.