README.md

---
license: creativeml-openrail-m
---
# Google Safesearch Mini Model Card
This model is trained on InceptionV3 and 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, the model achieves 94% train and test accuracy.

<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.