---
license: creativeml-openrail-m
---
# Google Safesearch Mini Model Card
This model is trained on 2,224,000 images scraped from Google Safesearch, Reddit, Imgur, and Github.
It predicts the likelihood of an image being nsfw_gore, nsfw_suggestive, and safe.

After 20 epochs on PyTorch's InceptionV3, the model achieves 94% accuracy on both train and test data.

<br>

# PyTorch
```bash
pip install --upgrade transformers
```
```python
from transformers import AutoModelForImageClassification
from torch import cuda

model = AutoModelForImageClassification.from_pretrained("FredZhang7/google-safesearch-mini", trust_remote_code=True, revision="d0b4c6be6d908c39c0dd83d25dce50c0e861e46a")

PATH_TO_IMAGE = 'https://images.unsplash.com/photo-1594568284297-7c64464062b1'
PRINT_TENSOR = False

prediction = model.predict(PATH_TO_IMAGE, device="cuda" if cuda.is_available() else "cpu", print_tensor=PRINT_TENSOR)
print(f"\033[1;{'32' if prediction == 'safe' else '33'}m{prediction}\033[0m")
```
Output Example:
![prediction](./output_example.png)

<br>

# 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.