Google Safesearch Mini Model Card
<a href="https://huggingface.co/FredZhang7/google-safesearch-mini-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a>
This model is trained on 2,220,000+ images scraped from Google Images, Reddit, Imgur, and Github. The InceptionV3 and Xception models have been fine-tuned to predict the likelihood of an image falling into one of three categories: nsfw_gore, nsfw_suggestive, and safe.
After 20 epochs on PyTorch, the finetuned InceptionV3 model achieves 94% acc on both training and test data. After 3.3 epochs on Keras, the finetuned Xception model scores 94% acc on training set and 92% on test set.
Not only is this model accurate, but it also offers a significant advantage over stable diffusion safety checkers. By using our model, users can save 1.12GB of RAM and disk space.
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PyTorch
The PyTorch model runs much slower with transformers, so downloading it externally is a better option.
pip install --upgrade torchvision
import torch, os, warnings, requests
from io import BytesIO
from PIL import Image
from urllib.request import urlretrieve
from torchvision import transforms
PATH_TO_IMAGE = 'https://images.unsplash.com/photo-1594568284297-7c64464062b1'
USE_CUDA = False
warnings.filterwarnings("ignore")
def download_model():
print("Downloading google_safesearch_mini.bin...")
urlretrieve("https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/pytorch_model.bin", "google_safesearch_mini.bin")
def eval():
if not os.path.exists("google_safesearch_mini.bin"):
download_model()
model = torch.jit.load('./google_safesearch_mini.bin')
img = Image.open(PATH_TO_IMAGE).convert('RGB') if not (PATH_TO_IMAGE.startswith('http://') or PATH_TO_IMAGE.startswith('https://')) else Image.open(BytesIO(requests.get(PATH_TO_IMAGE).content)).convert('RGB')
transform = transforms.Compose([transforms.Resize(299), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
img = transform(img).unsqueeze(0)
if USE_CUDA:
img, model = img.cuda(), model.cuda()
else:
img, model = img.cpu(), model.cpu()
model.eval()
with torch.no_grad():
out, _ = model(img)
_, predicted = torch.max(out.data, 1)
classes = {0: 'nsfw_gore', 1: 'nsfw_suggestive', 2: 'safe'}
# account for edge cases
if predicted[0] != 2 and abs(out[0][2] - out[0][predicted[0]]) > 0.20:
img = Image.new('RGB', image.size, color = (0, 255, 255))
print("\033[93m" + "safe" + "\033[0m")
else:
print('\n\033[1;31m' + classes[predicted.item()] + '\033[0m' if predicted.item() != 2 else '\033[1;32m' + classes[predicted.item()] + '\033[0m\n')
if __name__ == '__main__':
eval()
Output Example:
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Keras
import tensorflow as tf
from PIL import Image
import requests, os
# download the model
url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/saved_model.pb"
r = requests.get(url, allow_redirects=True)
if not os.path.exists('tensorflow'):
os.makedirs('tensorflow')
open('tensorflow/saved_model.pb', 'wb').write(r.content)
# download the variables
url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.data-00000-of-00001"
r = requests.get(url, allow_redirects=True)
if not os.path.exists('tensorflow/variables'):
os.makedirs('tensorflow/variables')
open('tensorflow/variables/variables.data-00000-of-00001', 'wb').write(r.content)
url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.index"
r = requests.get(url, allow_redirects=True)
open('tensorflow/variables/variables.index', 'wb').write(r.content)
# load the model
model = tf.saved_model.load('./tensorflow')
image = Image.open('cat.jpg')
image = image.resize((299, 299))
image = tf.convert_to_tensor(image)
image = tf.expand_dims(image, 0)
# run the model
tensor = model(image)
classes = ['nsfw_gore', 'nsfw_suggestive', 'safe']
prediction = classes[tf.argmax(tensor, 1)[0]]
print('\033[1;32m' + prediction + '\033[0m' if prediction == 'safe' else '\033[1;33m' + prediction + '\033[0m')
Output Example:
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Tensorflow.js
npm i @tensorflow/tfjs-node
const tf = require('@tensorflow/tfjs-node');
const fs = require('fs');
const { pipeline } = require('stream');
const { promisify } = require('util');
const download = async (url, path) => {
// Taken from https://levelup.gitconnected.com/how-to-download-a-file-with-node-js-e2b88fe55409
const streamPipeline = promisify(pipeline);
const response = await fetch(url);
if (!response.ok) {
throw new Error(`unexpected response ${response.statusText}`);
}
await streamPipeline(response.body, fs.createWriteStream(path));
};
async function run() {
// download saved model and variables from https://huggingface.co/FredZhang7/google-safesearch-mini/tree/main/tensorflow
if (!fs.existsSync('tensorflow')) {
fs.mkdirSync('tensorflow');
await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/saved_model.pb', 'tensorflow/saved_model.pb');
fs.mkdirSync('tensorflow/variables');
await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.data-00000-of-00001', 'tensorflow/variables/variables.data-00000-of-00001');
await download('https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/tensorflow/variables/variables.index', 'tensorflow/variables/variables.index');
}
// load model and image
const model = await tf.node.loadSavedModel('./tensorflow/');
const image = tf.node.decodeImage(fs.readFileSync('cat.jpg'), 3);
// predict
const input = tf.expandDims(image, 0);
const tensor = model.predict(input);
const max = tensor.argMax(1);
const classes = ['nsfw_gore', 'nsfw_suggestive', 'safe'];
console.log('\x1b[32m%s\x1b[0m', classes[max.dataSync()[0]], '\n');
}
run();
Output Example:
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Bias and Limitations
Each person's definition of "safe" is different. The images in the dataset are classified as safe/unsafe by Google SafeSearch, Reddit, and Imgur. It is possible that some images may be safe to others but not to you. Also, when a model encounters an image with things it hasn't seen, it likely makes wrong predictions. This is why in the PyTorch example, I accounted for the "edge cases" before printing the predictions.
