Nsfw Image Detection 384

🚀 nsfw-image-detection-384

Marqo/nsfw-image-detection-384 is a lightweight image classification model designed to identify NSFW images, offering high accuracy with a relatively small model size.

🚀 Quick Start

Marqo/nsfw-image-detection-384 is a lightweight image classification model aimed at identifying NSFW images. It's about 18–20x smaller than other open - source models and attains an accuracy of 98.56% on our dataset. The model takes 384x384 pixel images as input, divided into 16x16 pixel patches.

⚠️ Important Note

Like all models, this one can make mistakes. NSFW content can be subjective and contextual. This model is intended to help identify this content, use at your own risk.

✨ Features

• Lightweight: Approximately 18–20x smaller than other open - source models.
• High Accuracy: Achieves 98.56% accuracy on the dataset.
• Diverse Training Data: Trained on a dataset of 220,000 images with various content types.

📦 Installation

Image Classification with timm

pip install timm

💻 Usage Examples

Basic Usage

from urllib.request import urlopen
from PIL import Image
import timm
import torch

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model("hf_hub:Marqo/nsfw-image-detection-384", pretrained=True)
model = model.eval()

data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

with torch.no_grad():
    output = model(transforms(img).unsqueeze(0)).softmax(dim=-1).cpu()

class_names = model.pretrained_cfg["label_names"]
print("Probabilities:", output[0])
print("Class:", class_names[output[0].argmax()])

📚 Documentation

Evaluation

This model outperforms existing NSFW detectors on our dataset. Here we provide an evaluation against AdamCodd/vit-base-nsfw-detector and Falconsai/nsfw_image_detection:

Thresholds and Precision vs Recall

Adjusting the threshold for the NSFW probability can let you trade off precision, recall, and accuracy. This may be useful in different applications where different degrees of confidence are required.

Training Details

This model is a finetune of the timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k model.

Args

batch_size: 256
color_jitter: 0.2
color_jitter_prob: 0.05
cutmix: 0.1
drop: 0.1
drop_path: 0.05
epoch_repeats: 0.0
epochs: 20
gaussian_blur_prob: 0.005
hflip: 0.5
lr: 5.0e-05
mixup: 0.1
mixup_mode: batch
mixup_prob: 1.0
mixup_switch_prob: 0.5
momentum: 0.9
num_classes: 2
opt: adamw
remode: pixel
reprob: 0.5
sched: cosine
smoothing: 0.1
warmup_epochs: 2
warmup_lr: 1.0e-05
warmup_prefix: false

Information Table

📄 License

This project is licensed under the apache - 2.0 license.

📖 Citation

@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}