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> NSFW filter for detecting inappropriate content in images using CLIP embeddings and MLP architecture

# NSFW Filter

The NSFW (Not Safe For Work) Filter detects the likelihood that an image contains explicit or unsafe content. It outputs a probability score from 0 (safe) to 1 (NSFW), helping you filter or flag images in your datasets.

## Model Details

* **Architecture:** MLP trained on CLIP ViT-L/14 image embeddings
* **Source**: [CLIP-based NSFW Detector](https://github.com/LAION-AI/CLIP-based-NSFW-Detector)
* **Output Field:** `nsfw_score`
* **Score Range:** 0–1 (higher scores show NSFW content)
* **Embeddings:** Requires CLIP ViT-L/14 (see [Image embeddings](/curate-images/process-data/embeddings))

## How It Works

The filter takes pre-computed normalized image embeddings from a previous pipeline stage and predicts the probability of NSFW content. The lightweight model processes batches of embeddings efficiently on the GPU.

## Prerequisites

Before using the `ImageNSFWFilterStage`, ensure you have:

### Model Setup

The NSFW detector model weights are automatically downloaded from the LAION repository on first use. The stage will:

1. Download the CLIP-based NSFW detector model (\~20MB) to the specified `model_dir`
2. Cache the model for subsequent runs
3. Load the model onto GPU (or CPU if GPU unavailable)

**First-time setup:** The initial model download is quick (under 1 minute on most connections). Subsequent runs will use the cached model.

### Required Input

* **CLIP Embeddings:** Images must have embeddings already generated by `ImageEmbeddingStage`
* **Embedding Format:** CLIP ViT-L/14 768-dimensional vectors stored in `ImageObject.embedding`

## Usage

```python
from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.file_partitioning import FilePartitioningStage
from nemo_curator.stages.image.io.image_reader import ImageReaderStage
from nemo_curator.stages.image.embedders.clip_embedder import ImageEmbeddingStage
from nemo_curator.stages.image.filters.nsfw_filter import ImageNSFWFilterStage

# Create pipeline
pipeline = Pipeline(name="nsfw_filtering", description="Filter NSFW content from images")

# Stage 1: Partition tar files
pipeline.add_stage(FilePartitioningStage(
    file_paths="/path/to/tar_dataset",
    files_per_partition=1,
    file_extensions=[".tar"],
))

# Stage 2: Read images
pipeline.add_stage(ImageReaderStage(
    dali_batch_size=100,
    num_gpus_per_worker=0.25,
))

# Stage 3: Generate CLIP embeddings
pipeline.add_stage(ImageEmbeddingStage(
    model_dir="/path/to/models",
    model_inference_batch_size=32,
    num_gpus_per_worker=0.25,
))

# Stage 4: Apply NSFW filtering
pipeline.add_stage(ImageNSFWFilterStage(
    model_dir="/path/to/models",
    score_threshold=0.5,
    model_inference_batch_size=32,
    num_gpus_per_worker=0.25,
))

# Run the pipeline (uses XennaExecutor by default)
results = pipeline.run()
```

## Parameters

| Parameter                    | Type  | Default | Description                                                              |
| ---------------------------- | ----- | ------- | ------------------------------------------------------------------------ |
| `model_dir`                  | str   | None    | Path to directory containing model weights                               |
| `score_threshold`            | float | 0.5     | NSFW score threshold for filtering (filters out images above this value) |
| `model_inference_batch_size` | int   | 32      | Batch size for model inference                                           |
| `num_gpus_per_worker`        | float | 0.25    | GPU allocation per worker (0.25 = 1/4 GPU)                               |
| `verbose`                    | bool  | False   | Enable verbose logging for debugging                                     |

## Performance Notes

* The small model processes pre-computed embeddings efficiently on the GPU.
* Increase batch size for faster throughput if memory allows.

## Best Practices

* Use CLIP ViT-L/14 embeddings generated by `ImageEmbeddingStage` for best results.
* Run the NSFW filter after embedding generation in the same pipeline to avoid extra I/O.
* The filter requires pre-computed embeddings and cannot extract embeddings from raw images.
* Review a sample of scores to calibrate thresholds for your use case.
* Adjust `model_inference_batch_size` based on available GPU memory.

## Resources

* [Image Curation Tutorial](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/image/getting-started/image_curation_example.py)
* [Image Deduplication Example](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/image/getting-started/image_dedup_example.py)