license:cc-by-nc-sa-4.0 region:us
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license: cc-by-nc-sa-4.0
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Safesearch V5, which uses our innovative EfficientNetV2.5 architecture, will be released soon, along with the benchmark CSV file containing all image URLs, Google Safesearch predictions, AIstrova Safesearch predictions, and true labels.

Please note that this benchmark (validation set) has been reviewed multiple times, using only commonly accepted definitions of safe and unsafe content to minimize bias. However, it may still contain a few image labels that are controversial.

Also keep in mind that the accuracy for Google Safesearch may not be exact, although we have done our best to reduce the chance of incorrect Google Safesearch labels using [this Google Image search method](./google_image.js).

*We are training the model again with different sequences of input dimensions during training to improve the model's ability to generalize. The table below only shows the best result so far as of Sept 30th, 2023.*

| Model Name          | Benchmark Subset                              | Accuracy     | Test Samples Directly from Google Images | Challenge                                        |
|---------------------|----------------------------------------------|--------------|------------------------------------------|--------------------------------------------------|
| AIstrova Safesearch V5 | Clothing (hentai vs safe waifu)         | **88.755%**  | 249                                      | Ability to classify hentai vs. safe waifu content, even if it's on an unusual format like t-shirt prints  |
| Google Safesearch   |----------------------------------------------| 55.422%      |------------------------------------------|--------------------------------------------------|
| AIstrova Safesearch V5 | Movie Scenes & Video Games (graphic vs safe content) | **90.179%** | 224                                  | Ability to understand the nuanced differences between small injuries, horror, gory, and graphic content     |
| Google Safesearch   |----------------------------------------------| 69.196%      |------------------------------------------|--------------------------------------------------|
| AIstrova Safesearch V5 | African Girls (suggestive vs sexy)      | **93.141%**  | 277                                      | Ability to understand nuanced differences between sexy and sexually suggestive photos and make unbiased predictions, by training on a dataset with almost no African people  |
| Google Safesearch   |----------------------------------------------| 77.617%      |------------------------------------------|--------------------------------------------------|
| AIstrova Safesearch V5 | Drawings (nudity vs safe)                | **90.217%**  | 184                                      | Ability to generalize on artworks with less than 100 artworks in the training data  |
| Google Safesearch   |----------------------------------------------| 79.891%      |------------------------------------------|--------------------------------------------------|

We built our state-of-the-art dataset and model architecture with the hope of beating the average image recognition accuracy of adult human experts using a less than 20M param model.
However, after 32 days of research and over 26 days of GPU hours, this is the closest we could get: about 90% accuracy on sensitive topics as shown in the table, about 90% accuracy on extreme challenges as shown in the benchmark, and 99% accuracy on regular & 300+ edge cases combined as shown in our training accuracy & F1-score.