Source 6fe584013476be2186c5f9d1

---
name: fiftyone-dataset-inference
description: Run ML model inference on FiftyOne datasets. Use when running models for detection, classification, segmentation, or embeddings. Discovers available models dynamically from the Zoo, plugin operators, or custom sources — never assumes a fixed model list.
---

# Run Model Inference on FiftyOne Datasets

## Key Directives

**ALWAYS follow these rules:**

### 1. Check if dataset exists first
```python
list_datasets()
```
If the dataset doesn't exist, use the **fiftyone-dataset-import** skill to load it first.

### 2. Set context before operations
```python
set_context(dataset_name="my-dataset")
```

### 3. Launch App for inference
The App must be running to execute inference operators:
```python
launch_app(dataset_name="my-dataset")
```

### 4. Ask user for field names
Always confirm with the user:
- Which model to use
- Label field name for predictions (e.g., `predictions`, `detections`, `embeddings`)

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

## Workflow

### Step 1: Verify Dataset Exists

```python
list_datasets()
```

If the dataset is not in the list:
- Ask the user for the data location
- **Use the fiftyone-dataset-import skill** to import the data first
- Return to this workflow after import completes

### Step 2: Load Dataset and Review

```python
set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")
```

Review:
- Sample count
- Media type
- Existing label fields

### Step 3: Launch App

```python
launch_app(dataset_name="my-dataset")
```

### Step 4: Discover and Apply Model

Ask the user about the task, model, or type of data they're using (detection, classification, segmentation, embeddings, or a specific model name); note users may give a 'tool name' (see Path B). Then determine the path:

**Path A — Zoo model (most common)**

ALWAYS first fetch the live model list — never assume what's available:
```python
get_operator_schema(operator_uri="@voxel51/zoo/apply_zoo_model")
```
Pick the right model from the schema's model enum, then apply:
```python
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "<model-name-from-schema>",
        "label_field": "predictions"
    }
)
```

**Path B — Plugin operator**

If the user mentions a specific tool (e.g. CLIP similarity, SAM, a third-party model), check installed operators first:
```python
list_operators(builtin_only=False)
```
Find the matching operator, inspect its schema, then execute it:
```python
get_operator_schema(operator_uri="@org/plugin/operator")
execute_operator(operator_uri="@org/plugin/operator", params={...})
```

**Path C — Remote / externally registered model**

Check registered remote sources first:
```python
import fiftyone.zoo as foz
foz.list_zoo_model_sources()
```

If the model comes from a registered remote source (GitHub repo registered via `foz.register_zoo_model_source()`):
```python
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "REMOTE",
        "source": "<github-repo-url>",
        "label_field": "predictions"
    }
)
```

### Step 5: View Results

```python
set_view(exists=["predictions"])
```

### Step 6: Clean Up

```python
close_app()
```

## Model Discovery

**ALWAYS fetch the live model list — never rely on a hardcoded list.**

```python
get_operator_schema(operator_uri="@voxel51/zoo/apply_zoo_model")
```

The schema returns the full set of available models at runtime. Use the model names from there directly.

For plugin-provided models or operators:
```python
list_operators(builtin_only=False)
```

> If a model fails with a dependency error, the response includes `install_command`. Offer to run it for the user.

## Common Use Cases

### Use Case 1: Run Object Detection

```python
# Verify dataset exists
list_datasets()

# Set context and launch
set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

# Apply detection model
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "faster-rcnn-resnet50-fpn-coco-torch",
        "label_field": "predictions"
    }
)

# View results
set_view(exists=["predictions"])
```

### Use Case 2: Run Classification

```python
set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "resnet50-imagenet-torch",
        "label_field": "classification"
    }
)

set_view(exists=["classification"])
```

### Use Case 3: Generate Embeddings

```python
set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "clip-vit-base32-torch",
        "label_field": "clip_embeddings"
    }
)
```

### Use Case 4: Compare Ground Truth with Predictions

If dataset has existing labels:

```python
set_context(dataset_name="my-dataset")
dataset_summary(name="my-dataset")  # Check existing fields

launch_app(dataset_name="my-dataset")

# Run inference with different field name
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "yolov8m-coco-torch",
        "label_field": "predictions"  # Different from ground_truth
    }
)

# View both fields to compare
set_view(exists=["ground_truth", "predictions"])
```

### Use Case 5: Run Multiple Models

```python
set_context(dataset_name="my-dataset")
launch_app(dataset_name="my-dataset")

# Run detection
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "yolov8n-coco-torch",
        "label_field": "detections"
    }
)

# Run classification
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "resnet50-imagenet-torch",
        "label_field": "classification"
    }
)

# Run embeddings
execute_operator(
    operator_uri="@voxel51/zoo/apply_zoo_model",
    params={
        "tab": "BUILTIN",
        "model": "clip-vit-base32-torch",
        "label_field": "embeddings"
    }
)
```

## Troubleshooting

**Error: "Dataset not found"**
- Use `list_datasets()` to see available datasets
- Use the **fiftyone-dataset-import** skill to import data first

**Error: "Model not found"**
- Run `get_operator_schema(operator_uri="@voxel51/zoo/apply_zoo_model")` to get the current live model list and pick the correct name

**Error: "Missing dependency" (e.g., ultralytics, segment-anything)**
- The MCP server detects missing dependencies
- Response includes `missing_package` and `install_command`
- Install the required package: `pip install <package>`
- Restart MCP server after installing

**Inference is slow**
- Use smaller model variant (e.g., `yolov8n` instead of `yolov8x`)
- Use delegated execution for large datasets
- Consider filtering to a view first

**Out of memory**
- Reduce batch size
- Use smaller model variant
- Process dataset in chunks using views

## Best Practices

1. **Use descriptive field names** - `predictions`, `yolo_detections`, `clip_embeddings`
2. **Don't overwrite ground truth** - Use different field names for predictions
3. **Start with fast models** - Use nano/small variants first, upgrade if needed
4. **Check existing fields** - Use `dataset_summary()` before running inference
5. **Filter first for testing** - Test on a small view before processing full dataset

## Resources

- [FiftyOne Model Zoo](https://docs.voxel51.com/model_zoo/index.html)
- [Applying Models Guide](https://docs.voxel51.com/user_guide/applying_models.html)
- [Zoo Models API](https://docs.voxel51.com/api/fiftyone.zoo.models.html)