TensorFlow 2 implementation of the Yahoo Open-NSFW model

Overview

ci License MIT 1.0

Introduction

Detecting Not-Suitable-For-Work (NSFW) images is a high demand task in computer vision. While there are many types of NSFW images, here we focus on the pornographic images.

The Yahoo Open-NSFW model originally developed with the Caffe framework has been a favourite choice, but the work is now discontinued and Caffe is also becoming less popular. Please see the description on the Yahoo project page for the context, definitions, and model training details.

This Open-NSFW 2 project provides a TensorFlow 2 implementation of the Yahoo model, with references to its previous third-party TensorFlow 1 implementation.

Installation

Python 3.7 or above is required. Tested for 3.7, 3.8, and 3.9.

The best way to install Open-NSFW 2 with its dependencies is from PyPI:

python3 -m pip install --upgrade opennsfw2

Alternatively, to obtain the latest version from this repository:

git clone [email protected]:bhky/opennsfw2.git
cd opennsfw2
python3 -m pip install .

Usage

import numpy as np
import opennsfw2 as n2
from PIL import Image

# Load and preprocess image.
image_path = "path/to/your/image.jpg"
pil_image = Image.open(image_path)
image = n2.preprocess_image(pil_image, n2.Preprocessing.YAHOO)
# The preprocessed image is a NumPy array of shape (224, 224, 3).

# Create the model.
# By default, this call will search for the pre-trained weights file from path:
# $HOME/.opennsfw2/weights/open_nsfw_weights.h5
# If not exists, the file will be downloaded from this repository.
# The model is a `tf.keras.Model` object.
model = n2.make_open_nsfw_model()

# Make predictions.
inputs = np.expand_dims(image, axis=0)  # Add batch axis (for single image).
predictions = model.predict(inputs)

# The shape of predictions is (batch_size, 2).
# Each row gives [sfw_probability, nsfw_probability] of an input image, e.g.:
sfw_probability, nsfw_probability = predictions[0]

Alternatively, the end-to-end pipeline function can be used:

import opennsfw2 as n2

image_paths = [
    "path/to/your/image1.jpg",
    "path/to/your/image2.jpg",
    # ...
]

predictions = n2.predict(
    image_paths, batch_size=4, preprocessing=n2.Preprocessing.YAHOO
)

API

preprocess_image

Apply necessary preprocessing to the input image.

  • Parameters:
    • pil_image (PIL.Image): Input as a Pillow image.
    • preprocessing (Preprocessing enum, default Preprocessing.YAHOO): See preprocessing details.
  • Return:
    • NumPy array of shape (224, 224, 3).

Preprocessing

Enum class for preprocessing options.

  • Preprocessing.YAHOO
  • Preprocessing.SIMPLE

make_open_nsfw_model

Create an instance of the NSFW model, optionally with pre-trained weights from Yahoo.

  • Parameters:
    • input_shape (Tuple[int, int, int], default (224, 224, 3)): Input shape of the model, this should not be changed.
    • weights_path (Optional[str], default $HOME/.opennsfw/weights/open_nsfw_weights.h5): Path to the weights in HDF5 format to be loaded by the model. The weights file will be downloaded if not exists. Users can provide path if the default is not preferred. If None, no weights will be downloaded nor loaded to the model.
  • Return:
    • tf.keras.Model object.

predict

End-to-end pipeline function from input image paths to predictions.

  • Parameters:
    • image_paths (Sequence[str]): List of paths to input image files.
    • batch_size (int, default 32): Batch size to be used for model inference.
    • preprocessing: Same as that in preprocess_image.
    • weights_path: Same as that in make_open_nsfw_model.
  • Return:
    • NumPy array of shape (batch_size, 2), each row gives [sfw_probability, nsfw_probability] of an input image.

Preprocessing details

Options

This implementation provides the following preprocessing options.

  • YAHOO: The default option which was used in the original Yahoo's Caffe and the later TensorFlow 1 implementations. The key steps are:
    • Resize the input Pillow image to (256, 256).
    • Save the image as JPEG bytes and reload again to a NumPy image (this step is mysterious, but somehow it really makes a difference).
    • Crop the centre part of the NumPy image with size (224, 224).
    • Swap the colour channels to BGR.
    • Subtract the training dataset mean value of each channel: [104, 117, 123].
  • SIMPLE: A simpler and probably more intuitive preprocessing option is also provided, but note that the model output probabilities will be different. The key steps are:
    • Resize the input Pillow image to (224, 224).
    • Convert to a NumPy image.
    • Swap the colour channels to BGR.
    • Subtract the training dataset mean value of each channel: [104, 117, 123].

Comparison

Using 521 private images, the NSFW probabilities given by three different settings are compared:

  • TensorFlow 1 implementation with YAHOO preprocessing.
  • TensorFlow 2 implementation with YAHOO preprocessing.
  • TensorFlow 2 implementation with SIMPLE preprocessing.

The following figure shows the result:

NSFW probabilities comparison

The current TensorFlow 2 implementation with YAHOO preprocessing can totally reproduce the well-tested TensorFlow 1 result, with small floating point errors only.

With SIMPLE preprocessing the results are different, where the model tends to give lower probabilities.

You might also like...
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting (RVM) English | 中文 Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific

Open-AI's DALL-E for large scale training in mesh-tensorflow.

DALL-E in Mesh-Tensorflow [WIP] Open-AI's DALL-E in Mesh-Tensorflow. If this is similarly efficient to GPT-Neo, this repo should be able to train mode

Using Tensorflow Object Detection API to detect Waymo open dataset
Using Tensorflow Object Detection API to detect Waymo open dataset

Waymo-2D-Object-Detection Using Tensorflow Object Detection API to detect Waymo open dataset Result CenterNet Training Loss SSD ResNet Training Loss C

Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification
Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

STAM - Pytorch Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in

😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

------ Update September 2018 ------ It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such t

Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

Comments
  • ERROR WITH NO ERROR

    ERROR WITH NO ERROR

    Hi, I don't understand what happened with opennsfw2 code. My installation is OK. I install Keras and Tensorflow 2.0 with CUDA but nothing, Any idea ? I attached a screenshot. Thank you to help me 0008_2022-09-10_17_heures_18

    opened by fog88 7
  • Which NSFW Area is this AI covering?

    Which NSFW Area is this AI covering?

    Hi,

    very cool project, I am looking for an AI, which can cover on the one side nudity, but doesn't judge sexy images and also bans traumatic images, like horror and the crazy things, like NSFW 4 things, is it possible with this AI?

    nsfw-chart

    I found this image online, which is your AI covering?

    Thanks!

    opened by Flori00123 5
  • small demo website

    small demo website

    would be nice to have a small website that allows users to demo the model instead of having to run it all, such as https://maybeshewill-cv.github.io/nsfw_classification/

    opened by DankMemeGuy 1
Releases(v0.10.2)
Owner
Bosco Yung
Machine Learning Engineer, Lecturer, Astrophysicist
Bosco Yung
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Evaluation, Training, Demo, and Inference of DeFMO DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021) Denys Rozumnyi, Martin R. O

Denys Rozumnyi 139 Dec 26, 2022
PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

2021-CVPR-MvCLN This repo contains the code and data of the following paper accepted by CVPR 2021 Partially View-aligned Representation Learning with

XLearning Group 33 Nov 01, 2022
Memory efficient transducer loss computation

Introduction This project implements the optimization techniques proposed in Improving RNN Transducer Modeling for End-to-End Speech Recognition to re

Fangjun Kuang 51 Nov 25, 2022
Machine Learning Privacy Meter: A tool to quantify the privacy risks of machine learning models with respect to inference attacks, notably membership inference attacks

ML Privacy Meter Machine learning is playing a central role in automated decision making in a wide range of organization and service providers. The da

Data Privacy and Trustworthy Machine Learning Research Lab 357 Jan 06, 2023
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
🔥 Cannlytics-powered artificial intelligence 🤖

Cannlytics AI 🔥 Cannlytics-powered artificial intelligence 🤖 🏗️ Installation 🏃‍♀️ Quickstart 🧱 Development 🦾 Automation 💸 Support 🏛️ License ?

Cannlytics 3 Nov 11, 2022
My 1st place solution at Kaggle Hotel-ID 2021

1st place solution at Kaggle Hotel-ID My 1st place solution at Kaggle Hotel-ID to Combat Human Trafficking 2021. https://www.kaggle.com/c/hotel-id-202

Kohei Ozaki 18 Aug 19, 2022
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Fast mesh denoising with data driven normal filtering using deep variational autoencoders This is an implementation for the paper entitled "Fast mesh

9 Dec 02, 2022
Transport Mode detection - can detect the mode of transport with the help of features such as acceeration,jerk etc

title emoji colorFrom colorTo sdk app_file pinned Transport_Mode_Detector 🚀 purple yellow gradio app.py false Configuration title: string Display tit

Nishant Rajadhyaksha 3 Jan 16, 2022
A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation This repository contains the source code of the paper A Differentiable

Bernardo Aceituno 2 May 05, 2022
PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

FullSubNet This Git repository for the official PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech E

郝翔 357 Jan 04, 2023
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

40 Dec 13, 2022
Deep Learning Slide Captcha

滑动验证码深度学习识别 本项目使用深度学习 YOLOV3 模型来识别滑动验证码缺口,基于 https://github.com/eriklindernoren/PyTorch-YOLOv3 修改。 只需要几百张缺口标注图片即可训练出精度高的识别模型,识别效果样例: 克隆项目 运行命令: git cl

Python3WebSpider 55 Jan 02, 2023
A study project using the AA-RMVSNet to reconstruct buildings from multiple images

3d-building-reconstruction This is part of a study project using the AA-RMVSNet to reconstruct buildings from multiple images. Introduction It is exci

17 Oct 17, 2022
An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

DeepNER An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models. This repository contains complex Deep

Derrick 9 May 30, 2022
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
An Object Oriented Programming (OOP) interface for Ontology Web language (OWL) ontologies.

Enabling a developer to use Ontology Web Language (OWL) along with its reasoning capabilities in an Object Oriented Programming (OOP) paradigm, by pro

TheEngineRoom-UniGe 7 Sep 23, 2022
PyTorch code for training MM-DistillNet for multimodal knowledge distillation

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge MM-DistillNet is a

51 Dec 20, 2022
PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.

mrzhu 383 Dec 17, 2022