---
name: fiftyone-find-duplicates
description: Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.
---

# Find Duplicates in FiftyOne Datasets

## Key Directives

**ALWAYS follow these rules:**

### 1. Set context first
```python
set_context(dataset_name="my-dataset")
```

### 2. Launch FiftyOne App
Brain operators are delegated and require the app:
```python
launch_app()
```
Wait 5-10 seconds for initialization.

### 3. Discover operators dynamically
```python
# List all brain operators
list_operators(builtin_only=False)

# Get schema for specific operator
get_operator_schema(operator_uri="@voxel51/brain/compute_similarity")
```

### 4. Compute embeddings before finding duplicates
```python
execute_operator(
    operator_uri="@voxel51/brain/compute_similarity",
    params={"brain_key": "img_sim", "model": "mobilenet-v2-imagenet-torch"}
)
```

### 5. Close app when done
```python
close_app()
```

## Complete Workflow

### Step 1: Setup
```python
# Set context
set_context(dataset_name="my-dataset")

# Launch app (required for brain operators)
launch_app()
```

### Step 2: Verify Brain Plugin
```python
# Check if brain plugin is available
list_plugins(enabled=True)

# If not installed:
download_plugin(
    url_or_repo="voxel51/fiftyone-plugins",
    plugin_names=["@voxel51/brain"]
)
enable_plugin(plugin_name="@voxel51/brain")
```

### Step 3: Discover Brain Operators
```python
# List all available operators
list_operators(builtin_only=False)

# Get schema for compute_similarity
get_operator_schema(operator_uri="@voxel51/brain/compute_similarity")

# Get schema for find_duplicates
get_operator_schema(operator_uri="@voxel51/brain/find_duplicates")
```

### Step 4: Compute Similarity
```python
# Execute operator to compute embeddings
execute_operator(
    operator_uri="@voxel51/brain/compute_similarity",
    params={
        "brain_key": "img_duplicates",
        "model": "mobilenet-v2-imagenet-torch"
    }
)
```

### Step 5: Find Near Duplicates
```python
execute_operator(
    operator_uri="@voxel51/brain/find_near_duplicates",
    params={
        "similarity_index": "img_duplicates",
        "threshold": 0.3
    }
)
```

**Threshold guidelines (distance-based, lower = more similar):**
- `0.1` = Very similar (near-exact duplicates)
- `0.3` = Near duplicates (recommended default)
- `0.5` = Similar images
- `0.7` = Loosely similar

This operator creates two saved views automatically:
- `near duplicates`: all samples that are near duplicates
- `representatives of near duplicates`: one representative from each group

### Step 6: View Duplicates in App

After finding duplicates, use `set_view` to display them in the FiftyOne App:

**Option A: Filter by near_dup_id field**
```python
# Show all samples that have a near_dup_id (all duplicates)
set_view(exists=["near_dup_id"])
```

**Option B: Show specific duplicate group**
```python
# Show samples with a specific duplicate group ID
set_view(filters={"near_dup_id": 1})
```

**Option C: Load saved view (if available)**
```python
# Load the automatically created saved view
set_view(view_name="near duplicates")
```

**Option D: Clear filter to show all samples**
```python
clear_view()
```

The `find_near_duplicates` operator adds a `near_dup_id` field to samples. Samples with the same ID are duplicates of each other.

### Step 7: Delete Duplicates

**Option A: Use deduplicate operator (keeps one representative per group)**
```python
execute_operator(
    operator_uri="@voxel51/brain/deduplicate_near_duplicates",
    params={}
)
```

**Option B: Manual deletion from App UI**
1. Use `set_view(exists=["near_dup_id"])` to show duplicates
2. Review samples in the App at http://localhost:5151/
3. Select samples to delete
4. Use the delete action in the App

### Step 8: Clean Up
```python
close_app()
```

## Available Tools

### Session View Tools

| Tool | Description |
|------|-------------|
| `set_view(exists=[...])` | Filter samples where field(s) have non-None values |
| `set_view(filters={...})` | Filter samples by exact field values |
| `set_view(tags=[...])` | Filter samples by tags |
| `set_view(sample_ids=[...])` | Select specific sample IDs |
| `set_view(view_name="...")` | Load a saved view by name |
| `clear_view()` | Clear filters, show all samples |

### Brain Operators for Duplicates

Use `list_operators()` to discover and `get_operator_schema()` to see parameters:

| Operator | Description |
|----------|-------------|
| `@voxel51/brain/compute_similarity` | Compute embeddings and similarity index |
| `@voxel51/brain/find_near_duplicates` | Find near-duplicate samples |
| `@voxel51/brain/deduplicate_near_duplicates` | Delete duplicates, keep representatives |
| `@voxel51/brain/find_exact_duplicates` | Find exact duplicate media files |
| `@voxel51/brain/deduplicate_exact_duplicates` | Delete exact duplicates |
| `@voxel51/brain/compute_uniqueness` | Compute uniqueness scores |

## Common Use Cases

### Use Case 1: Remove Exact Duplicates
For accidentally duplicated files (identical bytes):
```python
set_context(dataset_name="my-dataset")
launch_app()

execute_operator(
    operator_uri="@voxel51/brain/find_exact_duplicates",
    params={}
)

execute_operator(
    operator_uri="@voxel51/brain/deduplicate_exact_duplicates",
    params={}
)

close_app()
```

### Use Case 2: Find and Review Near Duplicates
For visually similar but not identical images:
```python
set_context(dataset_name="my-dataset")
launch_app()

# Compute embeddings
execute_operator(
    operator_uri="@voxel51/brain/compute_similarity",
    params={"brain_key": "near_dups", "model": "mobilenet-v2-imagenet-torch"}
)

# Find duplicates
execute_operator(
    operator_uri="@voxel51/brain/find_near_duplicates",
    params={"similarity_index": "near_dups", "threshold": 0.3}
)

# View duplicates in the App
set_view(exists=["near_dup_id"])

# After review, deduplicate
execute_operator(
    operator_uri="@voxel51/brain/deduplicate_near_duplicates",
    params={}
)

# Clear view and close
clear_view()
close_app()
```

### Use Case 3: Sort by Similarity
Find images similar to a specific sample:
```python
set_context(dataset_name="my-dataset")
launch_app()

execute_operator(
    operator_uri="@voxel51/brain/compute_similarity",
    params={"brain_key": "search"}
)

execute_operator(
    operator_uri="@voxel51/brain/sort_by_similarity",
    params={
        "brain_key": "search",
        "query_id": "sample_id_here",
        "k": 20
    }
)

close_app()
```

## Troubleshooting

**Error: "No executor available"**
- Cause: Delegated operators require the App executor for UI triggers
- Solution: Direct user to App UI to view results and complete deletion manually
- Affected operators: `find_near_duplicates`, `deduplicate_near_duplicates`

**Error: "Brain key not found"**
- Cause: Embeddings not computed
- Solution: Run `compute_similarity` first with a `brain_key`

**Error: "Operator not found"**
- Cause: Brain plugin not installed
- Solution: Install with `download_plugin()` and `enable_plugin()`

**Error: "Missing dependency" (e.g., torch, tensorflow)**
- The MCP server detects missing dependencies automatically
- Response includes `missing_package` and `install_command`
- Example response:
  ```json
  {
    "error_type": "missing_dependency",
    "missing_package": "torch",
    "install_command": "pip install torch"
  }
  ```
- Offer to run the install command for the user
- After installation, restart MCP server and retry

**Similarity computation is slow**
- Use faster model: `mobilenet-v2-imagenet-torch`
- Use GPU if available
- Process large datasets in batches

## Best Practices

1. **Discover dynamically** - Use `list_operators()` and `get_operator_schema()` to get current operator names and parameters
2. **Start with default threshold** (0.3) and adjust as needed
3. **Review before deleting** - Direct user to App to inspect duplicates
4. **Store embeddings** - Reuse for multiple operations via `brain_key`
5. **Handle executor errors gracefully** - Guide user to App UI when needed

## Performance Notes

**Embedding computation time:**
- 1,000 images: ~1-2 minutes
- 10,000 images: ~10-15 minutes
- 100,000 images: ~1-2 hours

**Memory requirements:**
- ~2KB per image for embeddings
- ~4-8KB per image for similarity index

## Resources

- [FiftyOne Brain Documentation](https://docs.voxel51.com/user_guide/brain.html)
- [Brain Plugin Source](https://github.com/voxel51/fiftyone-plugins/tree/main/plugins/brain)