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
• Output Field: nsfw_score
• Score Range: 0–1 (higher scores show NSFW content)
• Embeddings: Requires CLIP ViT-L/14 (see Image 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

1
from nemo_curator.pipeline import Pipeline
2
from nemo_curator.stages.file_partitioning import FilePartitioningStage
3
from nemo_curator.stages.image.io.image_reader import ImageReaderStage
4
from nemo_curator.stages.image.embedders.clip_embedder import ImageEmbeddingStage
5
from nemo_curator.stages.image.filters.nsfw_filter import ImageNSFWFilterStage
6
7
# Create pipeline
8
pipeline = Pipeline(name="nsfw_filtering", description="Filter NSFW content from images")
9
10
# Stage 1: Partition tar files
11
pipeline.add_stage(FilePartitioningStage(
12
    file_paths="/path/to/tar_dataset",
13
    files_per_partition=1,
14
    file_extensions=[".tar"],
15
))
16
17
# Stage 2: Read images
18
pipeline.add_stage(ImageReaderStage(
19
    dali_batch_size=100,
20
    num_gpus_per_worker=0.25,
21
))
22
23
# Stage 3: Generate CLIP embeddings
24
pipeline.add_stage(ImageEmbeddingStage(
25
    model_dir="/path/to/models",
26
    model_inference_batch_size=32,
27
    num_gpus_per_worker=0.25,
28
))
29
30
# Stage 4: Apply NSFW filtering
31
pipeline.add_stage(ImageNSFWFilterStage(
32
    model_dir="/path/to/models",
33
    score_threshold=0.5,
34
    model_inference_batch_size=32,
35
    num_gpus_per_worker=0.25,
36
))
37
38
# Run the pipeline (uses XennaExecutor by default)
39
results = pipeline.run()

Parameters

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
• Image Deduplication Example