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younissk
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DISCO-v0.1

Image Classification
Transformers
nsfw-detection
content-moderation
clip
safety
child-safety
Eval Results
Model card Files
xet
Community
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  1. .cursor/rules/project-intro.mdc +5 -0
  2. .gitattributes +9 -35
  3. .gitignore +104 -0
  4. .python-version +1 -0
  5. Makefile +17 -0
  6. README.md +87 -0
  7. assets/DISCO-Banner.png +3 -0
  8. assets/disco_predicts.png +3 -0
  9. assets/falcon_baseline.png +0 -0
  10. assets/garbage.png +3 -0
  11. assets/imaterialist_fashion_rules.png +3 -0
  12. data/annotations.csv +456 -0
  13. data/annotations.json +0 -0
  14. data/falcons_predictions.csv +456 -0
  15. data/labels.csv +456 -0
  16. data/sexualised_fashion.csv +3 -0
  17. data/test/Screenshot 2025-12-21 at 14.46.33.png +3 -0
  18. data/test/Screenshot 2025-12-21 at 15.06.41.png +3 -0
  19. notebooks/eda.ipynb +0 -0
  20. notebooks/nswf_test.ipynb +78 -0
  21. notebooks/processing.ipynb +1691 -0
  22. pyproject.toml +25 -0
  23. src/baseline.py +145 -0
  24. src/dataset.py +137 -0
  25. src/inference.py +114 -0
  26. src/model.py +72 -0
  27. src/train.py +389 -0
  28. uv.lock +0 -0
.cursor/rules/project-intro.mdc ADDED
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+ ---
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+ alwaysApply: true
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+ ---
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+
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+ This project aims to fin tune NSFW detection model on data which is not considered modest. The use case ultimately is to be able to detect SUGGESTIVE content, even just bikinis (e.g. for kids to not watch filthy content on their devices).
.gitattributes CHANGED
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- *.7z filter=lfs diff=lfs merge=lfs -text
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- *.arrow filter=lfs diff=lfs merge=lfs -text
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- *.bin filter=lfs diff=lfs merge=lfs -text
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- *.bz2 filter=lfs diff=lfs merge=lfs -text
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- *.ckpt filter=lfs diff=lfs merge=lfs -text
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- *.ftz filter=lfs diff=lfs merge=lfs -text
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- *.gz filter=lfs diff=lfs merge=lfs -text
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- *.h5 filter=lfs diff=lfs merge=lfs -text
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- *.joblib filter=lfs diff=lfs merge=lfs -text
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- *.lfs.* filter=lfs diff=lfs merge=lfs -text
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- *.mlmodel filter=lfs diff=lfs merge=lfs -text
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- *.model filter=lfs diff=lfs merge=lfs -text
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- *.msgpack filter=lfs diff=lfs merge=lfs -text
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- *.npy filter=lfs diff=lfs merge=lfs -text
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- *.onnx filter=lfs diff=lfs merge=lfs -text
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- *.ot filter=lfs diff=lfs merge=lfs -text
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- *.parquet filter=lfs diff=lfs merge=lfs -text
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- *.pb filter=lfs diff=lfs merge=lfs -text
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- *.pickle filter=lfs diff=lfs merge=lfs -text
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- *.pkl filter=lfs diff=lfs merge=lfs -text
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- *.pt filter=lfs diff=lfs merge=lfs -text
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- *.pth filter=lfs diff=lfs merge=lfs -text
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- *.rar filter=lfs diff=lfs merge=lfs -text
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- *.safetensors filter=lfs diff=lfs merge=lfs -text
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- saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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- *.tar.* filter=lfs diff=lfs merge=lfs -text
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- *.tar filter=lfs diff=lfs merge=lfs -text
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- *.tflite filter=lfs diff=lfs merge=lfs -text
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- *.tgz filter=lfs diff=lfs merge=lfs -text
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- *.wasm filter=lfs diff=lfs merge=lfs -text
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- *.xz filter=lfs diff=lfs merge=lfs -text
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- *.zip filter=lfs diff=lfs merge=lfs -text
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- *.zst filter=lfs diff=lfs merge=lfs -text
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- *tfevents* filter=lfs diff=lfs merge=lfs -text
 
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+ # Auto detect text files and perform LF normalization
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+ * text=auto
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+ assets/DISCO-Banner.png filter=lfs diff=lfs merge=lfs -text
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+ assets/disco_predicts.png filter=lfs diff=lfs merge=lfs -text
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+ assets/garbage.png filter=lfs diff=lfs merge=lfs -text
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+ assets/imaterialist_fashion_rules.png filter=lfs diff=lfs merge=lfs -text
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+ data/sexualised_fashion.csv filter=lfs diff=lfs merge=lfs -text
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+ data/test/Screenshot[[:space:]]2025-12-21[[:space:]]at[[:space:]]14.46.33.png filter=lfs diff=lfs merge=lfs -text
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+ data/test/Screenshot[[:space:]]2025-12-21[[:space:]]at[[:space:]]15.06.41.png filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.gitignore ADDED
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+ # Python
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+ *.so
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ pip-wheel-metadata/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # Virtual Environments
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+ .env
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+ .venv
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+ env/
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+ venv/
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+ ENV/
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+ env.bak/
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+ venv.bak/
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+
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+ # IDEs and Editors
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+ .vscode/
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+ .idea/
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+ *.swp
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+ *.swo
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+ *~
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+ .project
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+ .pydevproject
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+ .settings/
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+ *.sublime-project
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+ *.sublime-workspace
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+
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+ *.ipynb_checkpoints/
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+
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+ # OS
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+ .DS_Store
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+ .DS_Store?
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+ ._*
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+ .Spotlight-V100
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+ .Trashes
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+ ehthumbs.db
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+ Thumbs.db
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+ desktop.ini
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+
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+ # Project-specific: Generated models and results
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+ models/
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+ results/
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+ *.joblib
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+ *.pkl
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+ *.h5
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+ *.ckpt
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+ *.pt
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+ *.pth
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+
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+ # Data files (uncomment if you don't want to track images)
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+ # data/imgs/
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+ # data/test/
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+
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+ # Logs
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+ *.log
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+ logs/
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+
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+ # Temporary files
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+ *.tmp
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+ *.temp
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+ .cache/
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+ .pytest_cache/
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+ .mypy_cache/
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+ .dmypy.json
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+ dmypy.json
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+
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+ # Coverage reports
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+ htmlcov/
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+ .tox/
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+ .coverage
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+ .coverage.*
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+ coverage.xml
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+ *.cover
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+ .hypothesis/
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+
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+ # Environment variables
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+ .env.local
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+ .env.*.local
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+
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+ # UV
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+ .uv/
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+
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+ data/imgs
.python-version ADDED
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+ 3.13
Makefile ADDED
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+ .PHONY: baseline help copy-label-studio-images train
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+
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+ help:
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+ @echo "Available targets:"
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+ @echo " baseline - Run the baseline NSFW detection script"
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+ @echo " copy-label-studio-images - Copy annotated images from Label Studio"
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+ @echo " train - Train the model"
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+ @echo " help - Show this help message"
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+
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+ baseline:
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+ uv run python src/baseline.py
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+
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+ copy-label-studio-images:
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+ uv run python -c "import json, shutil; from pathlib import Path; from rich import print; ls_media = Path.home() / 'Library/Application Support/label-studio/media/upload/5'; project_imgs = Path('data/imgs'); anns = json.load(open('data/annotations.json')); files = [a['file_upload'] for a in anns if a.get('file_upload')]; [shutil.copy2(ls_media / f, project_imgs / f) for f in files if (ls_media / f).exists()]; print(f'✓ Copied {sum(1 for f in files if (project_imgs / f).exists())} images')"
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+
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+ train:
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+ uv run python -m src.train
README.md ADDED
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+
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+ # DISCO: Detection of Implicit Suggestive Content Overlays
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+
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+ ![DISCO Banner](assets/DISCO-Banner.png)
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+
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+ Kids are increasingly exposed to suggestive content on social media and the big companies don't do enough against it. E.g. Roblox and Youtube kids.
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+
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+ Here an example of what your kid might see on Youtube Kids:
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+
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+ ![Youtube Kids](assets/garbage.png)
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+
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+ To combat sexualisation of content on social media which is targetted towards kids, I trained a model to detect suggestive content. This can hopefully be extended to either detect content that is not suitable for kids during preprocessing or even during real time on device inference. (maybe in the future)
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+
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+ This was just a small weekend project, however if it seems to be somewhat useful, I might extend it in the future.
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+
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+ ![Disco Predicts](assets/disco_predicts.png)
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+
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+ ## Baseline comparison
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+
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+ I wanted to see how much more accurate my model is than a popular NSFW detection model. I used the Falconsai/nsfw_image_detection model to compare.
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+
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+ To my surprise, the NSFW model did really bad at detecting suggestive content. I understand it wasn't built for this purpose, however I expected it to at least do better than random.
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+
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+ see `baseline.py` and `eda.ipynb` for the implementation.
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+
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+ ![Baseline comparison](assets/falcon_baseline.png)
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+
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+ ## Dataset
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+
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+ For training the model, I used iMaterialist dataset from Kaggle. I took a small subset of images and labled them myself using Label Studio.
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+
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+ Sadly, the terms of the dataset mention that I may not distribute the dataset, nor use it for commercial purposes (which I didnt plan to do anyways).
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+
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+ This however does mean that the model is not fully reproducible in this state, unless you accept the terms of the dataset and download it yourself. If this model seems to be useful, I will try to find a different approach to gathering data and training the model.
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+
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+ ![iMaterialist dataset](assets/imaterialist_fashion_rules.png)
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+
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+ ### Methodology
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+
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+ I labled in total 455 images, with 3 categories to begin with: FAMILY_SAFE, UNCERTAIN and SUGGESTIVE.
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+
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+ For the training however I combined FAMILY_SAFE and UNCERTAIN into a single category called FAMILY_SAFE/UNCERTAIN. This is a point of improvement for the future.
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+
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+ The default split is as follows: 70% for training, 15% for validation and 15% for testing.
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+
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+ ## Model
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+
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+ The model itself combines a CLIP image encoder with a linear classifier. (super simple)
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+
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+ In the forward pass, I generate the image features using the CLIP model and then pass them through the linear classifier.
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+
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+ Then to predict the probability of the SUGGESTIVE class, I use the softmax function.
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+
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+ ```python
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+ proba = torch.softmax(logits, dim=-1)[0, 1].item()
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+ ```
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+
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+ And then I can predict the class by thresholding the probability.
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+
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+ ```python
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+ pred = (proba >= threshold).long()
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+ ```
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+
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+ ## Training
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+
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+ I trained the model using mps instead of CPU or cuda.
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+
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+ It is trained on 10 epochs with a batch size of 32, a learning rate of 1e-3, a weight decay of 1e-4 and a class weight of "balanced". It's trained on 2 classed instead of 3 and it is initialised with a threshold of 0.5 (meaning if an image has a probability of 0.5 or higher, it is predicted as SUGGESTIVE).
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+
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+ During the training, I valvulate the model on the validation set and save the best model based on the F1 score. If it beats the previous best F1 score, I save the model.
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+
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+ After the training, I tune the threshold on the validation set and save the best threshold based on the F1 score.
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+
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+ ```python
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+ best_threshold, threshold_metrics = tune_threshold(
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+ val_labels_np, val_scores, metric="f1")
77
+ ```
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+
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+ ## Results
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+
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+ ## Known problems and future improvements
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+
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+ I am aware that my own labelling is not perfect and subjective. Therefore the biggest improvement would be to have a more objective labelling process. (Add more annotators and images to the dataset)
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+
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+ Another big problem is lisencing and copyright issues. My goal is to make this model better detect what is actually shown to kids, such as things like elsa and spiderman in suggestive content.
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+
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+ Other than that, my goal is to make this model work fast on browsers for the future possibility to add it to a chrome extension to block content in real time.
assets/DISCO-Banner.png ADDED

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assets/disco_predicts.png ADDED

Git LFS Details

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  • Pointer size: 132 Bytes
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assets/falcon_baseline.png ADDED
assets/garbage.png ADDED

Git LFS Details

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  • Pointer size: 132 Bytes
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assets/imaterialist_fashion_rules.png ADDED

Git LFS Details

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  • Size of remote file: 419 kB
data/annotations.csv ADDED
@@ -0,0 +1,456 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ "annotation_id","annotator","choice","created_at","id","image","lead_time","updated_at"
2
+ 175,"1","UNCERTAIN","2025-12-20T10:56:29.661960Z",175,"/data/upload/5/0f7d40c8-ffd86ff5fbd82d0e6eb176cbc0b83634.jpg",24.357,"2025-12-20T10:56:29.661978Z"
3
+ 176,"1","FAMILY_SAFE","2025-12-20T10:56:35.977035Z",176,"/data/upload/5/abe701ef-fff11e31ec0146bd469e5e3afa14a37a.jpg",6.115,"2025-12-20T10:56:35.977061Z"
4
+ 177,"1","UNCERTAIN","2025-12-20T10:56:38.116840Z",177,"/data/upload/5/f3f6d9e7-ff8ba374753afc33d6efcddfa8190820.jpg",1.913,"2025-12-20T10:56:38.116864Z"
5
+ 178,"1","UNCERTAIN","2025-12-20T10:56:41.031047Z",178,"/data/upload/5/2a71e0e5-ff8f980e673cd0ca47acd5e46a60860e.jpg",2.665,"2025-12-20T10:56:41.031067Z"
6
+ 179,"1","UNCERTAIN","2025-12-20T10:56:43.279382Z",179,"/data/upload/5/883fb0f5-ff89d127a396bc4e269161fcb0debafc.jpg",2.029,"2025-12-20T10:56:43.279402Z"
7
+ 180,"1","FAMILY_SAFE","2025-12-20T10:56:44.929628Z",180,"/data/upload/5/2af0302e-ff943feb34677ab7380b172f5a879061.jpg",1.407,"2025-12-20T10:56:44.929649Z"
8
+ 181,"1","SUGGESTIVE","2025-12-20T10:56:47.266206Z",181,"/data/upload/5/00b62e8d-ff8923c82c694582614e3c6426f27d50.jpg",2.1,"2025-12-20T10:56:47.266226Z"
9
+ 182,"1","UNCERTAIN","2025-12-20T10:56:52.306273Z",182,"/data/upload/5/bc29dd51-ffa3ea1e617fc51d9f897c3651d8c58c.jpg",4.823,"2025-12-20T10:56:52.306292Z"
10
+ 183,"1","FAMILY_SAFE","2025-12-20T10:56:55.191244Z",183,"/data/upload/5/7efcc1a9-ffb14bc253b175a8064d17ffe2c7249b.jpg",2.678,"2025-12-20T10:56:55.191265Z"
11
+ 184,"1","UNCERTAIN","2025-12-20T10:56:57.847092Z",184,"/data/upload/5/1ade517e-ff5c303cb978725b41ecdc33f99a9811.jpg",2.437,"2025-12-20T10:56:57.847112Z"
12
+ 185,"1","FAMILY_SAFE","2025-12-20T10:57:00.785194Z",185,"/data/upload/5/e90235cf-ff4ee441959276cee003489e10030787.jpg",2.724,"2025-12-20T10:57:00.785209Z"
13
+ 186,"1","SUGGESTIVE","2025-12-20T10:57:02.767594Z",186,"/data/upload/5/cb00e4b8-ff57d7cdec78610cfc32857254662054.jpg",1.808,"2025-12-20T10:57:02.767604Z"
14
+ 187,"1","SUGGESTIVE","2025-12-20T10:57:05.232064Z",187,"/data/upload/5/d140fb6b-ff42165c1c025a99db8fd3fbf47b9e8d.jpg",2.289,"2025-12-20T10:57:05.232085Z"
15
+ 188,"1","SUGGESTIVE","2025-12-20T10:57:06.997123Z",188,"/data/upload/5/a1cd3b22-ff0dbc383dfd54bed44ec0583cbba777.jpg",1.541,"2025-12-20T10:57:06.997149Z"
16
+ 189,"1","SUGGESTIVE","2025-12-20T10:57:08.812119Z",189,"/data/upload/5/ed323d8b-ff3cae8c55408c7ed67ef1daf9751319.jpg",1.614,"2025-12-20T10:57:08.812139Z"
17
+ 190,"1","UNCERTAIN","2025-12-20T10:57:11.626324Z",190,"/data/upload/5/c8ec92d5-fee9ea29f695f6f590dbaf3ecc99351b.jpg",2.592,"2025-12-20T10:57:11.626346Z"
18
+ 191,"1","SUGGESTIVE","2025-12-20T10:57:13.328356Z",191,"/data/upload/5/f2c80c32-ff0a5673ea6b3139e5440728d7be66b4.jpg",1.495,"2025-12-20T10:57:13.328378Z"
19
+ 192,"1","SUGGESTIVE","2025-12-20T10:57:15.124462Z",192,"/data/upload/5/e9b17aee-ff007a23e0caf370e590d636413628fb.jpg",1.586,"2025-12-20T10:57:15.124505Z"
20
+ 193,"1","SUGGESTIVE","2025-12-20T10:57:16.758021Z",193,"/data/upload/5/c53b2fea-feb2e70ea41a86df9c9984df1aff49d1.jpg",1.427,"2025-12-20T10:57:16.758042Z"
21
+ 194,"1","UNCERTAIN","2025-12-20T10:57:20.199635Z",194,"/data/upload/5/1540c751-feb3ae09e5e5e2d27b1c6b0d75e66c73.jpg",3.232,"2025-12-20T10:57:20.199658Z"
22
+ 195,"1","UNCERTAIN","2025-12-20T10:57:22.724559Z",195,"/data/upload/5/18eb5521-febafb43e0a8a5a62bd64d0e1e2c51bb.jpg",2.323,"2025-12-20T10:57:22.724579Z"
23
+ 196,"1","FAMILY_SAFE","2025-12-20T10:57:24.280506Z",196,"/data/upload/5/09c99130-fec2e3149882333a9a5a381f562f9536.jpg",1.349,"2025-12-20T10:57:24.280525Z"
24
+ 197,"1","SUGGESTIVE","2025-12-20T10:57:26.340851Z",197,"/data/upload/5/6d3cfee1-fec30d1ce7fd3c5f8056da81cc0f56cd.jpg",1.853,"2025-12-20T10:57:26.340871Z"
25
+ 198,"1","UNCERTAIN","2025-12-20T10:57:28.435184Z",198,"/data/upload/5/02e91ed5-fecaf071c6d469a5e9db4516cf04c34f.jpg",1.891,"2025-12-20T10:57:28.435196Z"
26
+ 199,"1","UNCERTAIN","2025-12-20T10:57:31.742539Z",199,"/data/upload/5/6084597d-fed1a27739f7fc733adfe4843185b995.jpg",3.097,"2025-12-20T10:57:31.742561Z"
27
+ 200,"1","FAMILY_SAFE","2025-12-20T10:57:33.618254Z",200,"/data/upload/5/18699932-fe8d4f54e561bf7a76f70e6b28299d59.jpg",1.671,"2025-12-20T10:57:33.618276Z"
28
+ 201,"1","FAMILY_SAFE","2025-12-20T10:57:34.996660Z",201,"/data/upload/5/759b4eb3-fe4ec0e6a0803e088352ad9cba4e969a.jpg",1.163,"2025-12-20T10:57:34.996679Z"
29
+ 202,"1","UNCERTAIN","2025-12-20T10:57:39.780303Z",202,"/data/upload/5/fd0a4064-fe4f57b1dc8f46cbd79d69ad05652ac8.jpg",4.575,"2025-12-20T10:57:39.780323Z"
30
+ 203,"1","SUGGESTIVE","2025-12-20T10:57:42.918342Z",203,"/data/upload/5/722170f8-fe61bb1e7709ddb889307cd6d644bbb9.jpg",2.93,"2025-12-20T10:57:42.918364Z"
31
+ 204,"1","UNCERTAIN","2025-12-20T10:57:46.679930Z",204,"/data/upload/5/597903c6-fe446bc03fc71ccd0559f01bebbd073d.jpg",3.55,"2025-12-20T10:57:46.679953Z"
32
+ 205,"1","UNCERTAIN","2025-12-20T10:57:52.644595Z",205,"/data/upload/5/ded203db-fe11b6d8acc63b2819390a12a32bbd30.jpg",5.756,"2025-12-20T10:57:52.644618Z"
33
+ 206,"1","FAMILY_SAFE","2025-12-20T10:57:54.674103Z",206,"/data/upload/5/72e813f5-fde81d8132b2c70f39798f11186997bd.jpg",1.807,"2025-12-20T10:57:54.674125Z"
34
+ 207,"1","FAMILY_SAFE","2025-12-20T10:57:57.547565Z",207,"/data/upload/5/6474cda9-fde879b3ab6d5f8fb99aacad817dc18f.jpg",2.668,"2025-12-20T10:57:57.547584Z"
35
+ 208,"1","FAMILY_SAFE","2025-12-20T10:57:59.787547Z",208,"/data/upload/5/9d734426-fdfc5d73210e52e70f9c1b337c7a7423.jpg",2.025,"2025-12-20T10:57:59.787569Z"
36
+ 209,"1","UNCERTAIN","2025-12-20T10:58:03.315283Z",209,"/data/upload/5/940a8d2a-fe06e1df8abfcc599b4edf3ceb84b767.jpg",3.285,"2025-12-20T10:58:03.315306Z"
37
+ 210,"1","FAMILY_SAFE","2025-12-20T10:58:05.355008Z",210,"/data/upload/5/6cf544b3-fdcc56c858be84644a24f71ca1ecf009.jpg",1.825,"2025-12-20T10:58:05.355032Z"
38
+ 211,"1","FAMILY_SAFE","2025-12-20T10:58:06.908523Z",211,"/data/upload/5/c90492e9-fdce40a990a776a3ef934aa8eacfa0da.jpg",1.322,"2025-12-20T10:58:06.908545Z"
39
+ 212,"1","FAMILY_SAFE","2025-12-20T10:58:08.724638Z",212,"/data/upload/5/d5d56f5c-fdd0daec3624d1d7acf8048484a7efe2.jpg",1.629,"2025-12-20T10:58:08.724649Z"
40
+ 213,"1","UNCERTAIN","2025-12-20T10:58:11.083198Z",213,"/data/upload/5/1b6ade1e-fdbea88aeaa93ac7dbcb38d07b917e7d.jpg",2.145,"2025-12-20T10:58:11.083219Z"
41
+ 214,"1","UNCERTAIN","2025-12-20T10:58:15.163580Z",214,"/data/upload/5/e7c85aca-fdc00eac708506181cc0b3018d217b97.jpg",3.845,"2025-12-20T10:58:15.163600Z"
42
+ 215,"1","SUGGESTIVE","2025-12-20T10:58:16.794312Z",215,"/data/upload/5/6519a095-fd8eea5a65e5ef6cec86fe214c5a3cd3.jpg",1.405,"2025-12-20T10:58:16.794331Z"
43
+ 216,"1","FAMILY_SAFE","2025-12-20T10:58:18.941871Z",216,"/data/upload/5/bdce6a65-fd918dbde3d770d03716835677e7b611.jpg",1.912,"2025-12-20T10:58:18.941894Z"
44
+ 217,"1","SUGGESTIVE","2025-12-20T10:58:23.970295Z",217,"/data/upload/5/8ed58548-fd9371dd911d3a2099a6a652db4248b4.jpg",4.812,"2025-12-20T10:58:23.970317Z"
45
+ 218,"1","SUGGESTIVE","2025-12-20T10:58:28.725792Z",218,"/data/upload/5/72f07c2b-fd94006f594a7575961947fcca9faa08.jpg",4.527,"2025-12-20T10:58:28.725805Z"
46
+ 219,"1","FAMILY_SAFE","2025-12-20T10:58:30.567211Z",219,"/data/upload/5/9eb0117b-fd7f50b9d57c9384041914d1b4cdd77c.jpg",1.652,"2025-12-20T10:58:30.567236Z"
47
+ 220,"1","FAMILY_SAFE","2025-12-20T10:58:32.107655Z",220,"/data/upload/5/e243712d-fd71a7a81dea0c9fc195cf6d6f32b0a9.jpg",1.329,"2025-12-20T10:58:32.107680Z"
48
+ 221,"1","FAMILY_SAFE","2025-12-20T10:58:34.565800Z",221,"/data/upload/5/3198b794-fd826bc213d0273afbb83b3c4aec1adc.jpg",2.249,"2025-12-20T10:58:34.565818Z"
49
+ 222,"1","FAMILY_SAFE","2025-12-20T10:58:36.088297Z",222,"/data/upload/5/75ee25dc-fd71396788725bb988c9cd3689fcde68.jpg",1.313,"2025-12-20T10:58:36.088320Z"
50
+ 223,"1","UNCERTAIN","2025-12-20T10:58:39.126725Z",223,"/data/upload/5/3dade0ea-fd3e2ce0703f9c027862f5a79c788b56.jpg",2.834,"2025-12-20T10:58:39.126748Z"
51
+ 224,"1","FAMILY_SAFE","2025-12-20T10:58:41.598447Z",224,"/data/upload/5/13a4e537-fd4bbf6927a13c5289dfa509b7a4078b.jpg",2.263,"2025-12-20T10:58:41.598470Z"
52
+ 225,"1","SUGGESTIVE","2025-12-20T10:58:45.314112Z",225,"/data/upload/5/afddcafe-fd4da06b37fbae5380cf0d7fa0678103.jpg",3.518,"2025-12-20T10:58:45.314132Z"
53
+ 226,"1","FAMILY_SAFE","2025-12-20T10:58:47.509028Z",226,"/data/upload/5/84acb822-fd4eb64e7f0ca587d908435fb95b2909.jpg",1.983,"2025-12-20T10:58:47.509054Z"
54
+ 227,"1","FAMILY_SAFE","2025-12-20T10:58:48.909406Z",227,"/data/upload/5/022b4f3e-fd39709199d9f45147c183dee3c5d697.jpg",1.197,"2025-12-20T10:58:48.909427Z"
55
+ 228,"1","SUGGESTIVE","2025-12-20T10:58:51.965227Z",228,"/data/upload/5/d4ccee96-fd24b551b4531ae14433a7950eaa85b3.jpg",2.829,"2025-12-20T10:58:51.965248Z"
56
+ 229,"1","FAMILY_SAFE","2025-12-20T10:58:53.445069Z",229,"/data/upload/5/3a78a22b-fd38a720ae06640dd25aaf9c11dd9672.jpg",1.28,"2025-12-20T10:58:53.445089Z"
57
+ 230,"1","UNCERTAIN","2025-12-20T10:58:55.604188Z",230,"/data/upload/5/6d307a2e-fd2268af5eafa750a7e5a3a521ff7fdd.jpg",6.054,"2025-12-20T10:59:05.944396Z"
58
+ 231,"1","SUGGESTIVE","2025-12-20T10:59:14.148370Z",231,"/data/upload/5/b224fc67-fd150330c8a62cd9ab45e51f4de6969f.jpg",4.829,"2025-12-20T10:59:14.148389Z"
59
+ 232,"1","UNCERTAIN","2025-12-20T10:59:16.230973Z",232,"/data/upload/5/6da2441b-fcf901727f0b68c35c55f5734189c1f0.jpg",1.889,"2025-12-20T10:59:16.230983Z"
60
+ 233,"1","SUGGESTIVE","2025-12-20T10:59:18.476259Z",233,"/data/upload/5/ef1cbf46-fd08dc2a899b79496087b34b88b82f2d.jpg",2.061,"2025-12-20T10:59:18.476284Z"
61
+ 234,"1","SUGGESTIVE","2025-12-20T10:59:20.279562Z",234,"/data/upload/5/d1a55b96-fca9c570e98556bb8c40835c55cba67d.jpg",1.596,"2025-12-20T10:59:20.279581Z"
62
+ 235,"1","SUGGESTIVE","2025-12-20T10:59:23.589158Z",235,"/data/upload/5/fc1397fa-fcbbcf0191ab925aa67604ff0c7f141e.jpg",3.101,"2025-12-20T10:59:23.589180Z"
63
+ 236,"1","SUGGESTIVE","2025-12-20T10:59:26.455141Z",236,"/data/upload/5/74454c95-fcbf1a2490d9531efc33322b2702912f.jpg",2.662,"2025-12-20T10:59:26.455162Z"
64
+ 237,"1","FAMILY_SAFE","2025-12-20T10:59:28.178566Z",237,"/data/upload/5/b7206afa-fcc50f8b1362042b4acad74a4b4ae4ed.jpg",1.529,"2025-12-20T10:59:28.178586Z"
65
+ 238,"1","SUGGESTIVE","2025-12-20T10:59:31.104786Z",238,"/data/upload/5/64be13ce-fc813fdb8cb41651b9b4d5141fcd386a.jpg",2.72,"2025-12-20T10:59:31.104804Z"
66
+ 239,"1","SUGGESTIVE","2025-12-20T10:59:34.924429Z",239,"/data/upload/5/d5c64c7c-fca4fe8aa6a350ab9680692ebbe1e13c.jpg",3.618,"2025-12-20T10:59:34.924454Z"
67
+ 240,"1","SUGGESTIVE","2025-12-20T10:59:38.941569Z",240,"/data/upload/5/8af150d5-fca025dcf76a43feae0bf2e8e9a6c1e2.jpg",3.809,"2025-12-20T10:59:38.941590Z"
68
+ 241,"1","SUGGESTIVE","2025-12-20T10:59:42.058775Z",241,"/data/upload/5/0df4b27b-fc6fefe3f417fdbfd91da8cffe59f7db.jpg",2.934,"2025-12-20T10:59:42.058800Z"
69
+ 242,"1","SUGGESTIVE","2025-12-20T10:59:44.662908Z",242,"/data/upload/5/a90c3b4e-fc39f11aab8aae4bb03057701a7b8312.jpg",2.399,"2025-12-20T10:59:44.662928Z"
70
+ 243,"1","FAMILY_SAFE","2025-12-20T10:59:49.192648Z",243,"/data/upload/5/3c42d798-fc74b1fc4095a324b1f1ca1ac322278d.jpg",4.315,"2025-12-20T10:59:49.192673Z"
71
+ 244,"1","FAMILY_SAFE","2025-12-20T10:59:50.613951Z",244,"/data/upload/5/0bfb63b2-fc644a2683a976d8391e70f88408468b.jpg",1.211,"2025-12-20T10:59:50.613973Z"
72
+ 245,"1","FAMILY_SAFE","2025-12-20T10:59:51.891384Z",245,"/data/upload/5/b0091e81-fbf4b07fffa61fa2650649ec19debf39.jpg",1.069,"2025-12-20T10:59:51.891404Z"
73
+ 246,"1","SUGGESTIVE","2025-12-20T10:59:55.596094Z",246,"/data/upload/5/6faa5147-fbf13c899fbdd8e750dc46cc284249c5.jpg",3.49,"2025-12-20T10:59:55.596114Z"
74
+ 247,"1","FAMILY_SAFE","2025-12-20T10:59:57.228637Z",247,"/data/upload/5/bcfeea6a-fc1baf625d3e36ca4c5a29521f72525e.jpg",1.428,"2025-12-20T10:59:57.228659Z"
75
+ 248,"1","FAMILY_SAFE","2025-12-20T10:59:59.217183Z",248,"/data/upload/5/0d5b1538-fc21d81fb821d214b4f4f1d7f553475d.jpg",1.786,"2025-12-20T10:59:59.217205Z"
76
+ 249,"1","UNCERTAIN","2025-12-20T11:00:08.247796Z",249,"/data/upload/5/cfb6f5d7-fc07723d545d098aafab4aeb7d2e65a3.jpg",8.794,"2025-12-20T11:00:08.247817Z"
77
+ 250,"1","SUGGESTIVE","2025-12-20T11:00:10.107692Z",250,"/data/upload/5/43a803bd-fbcddca8eb614e2e2076c1128d30fa58.jpg",1.625,"2025-12-20T11:00:10.107715Z"
78
+ 251,"1","FAMILY_SAFE","2025-12-20T11:00:11.518608Z",251,"/data/upload/5/930fa681-fbd64159d7d027262f736408659d40cb.jpg",1.201,"2025-12-20T11:00:11.518627Z"
79
+ 252,"1","SUGGESTIVE","2025-12-20T11:00:14.639043Z",252,"/data/upload/5/c884f2e6-fbb763c6f6f858fdfc270f3f3bda1eac.jpg",2.913,"2025-12-20T11:00:14.639065Z"
80
+ 253,"1","FAMILY_SAFE","2025-12-20T11:00:17.082875Z",253,"/data/upload/5/b3f73152-fbc604a00ac5d9d6a73cec8789a75d0c.jpg",2.239,"2025-12-20T11:00:17.082895Z"
81
+ 254,"1","SUGGESTIVE","2025-12-20T11:00:21.019281Z",254,"/data/upload/5/f3e719e9-fbc9929373c24b1afd9523aa5253a594.jpg",3.728,"2025-12-20T11:00:21.019303Z"
82
+ 255,"1","SUGGESTIVE","2025-12-20T11:00:22.337701Z",255,"/data/upload/5/ac2b938f-fbca15037c332d6e9e658e2129217794.jpg",1.094,"2025-12-20T11:00:22.337722Z"
83
+ 256,"1","SUGGESTIVE","2025-12-20T11:00:23.656989Z",256,"/data/upload/5/e0092f87-fb6ad7084b95e0bb2eb9575082907662.jpg",1.108,"2025-12-20T11:00:23.657010Z"
84
+ 257,"1","SUGGESTIVE","2025-12-20T11:00:25.231571Z",257,"/data/upload/5/efd3b12b-fb7ba81d49347796a797d1322a64cb71.jpg",1.364,"2025-12-20T11:00:25.231595Z"
85
+ 258,"1","FAMILY_SAFE","2025-12-20T11:00:26.635777Z",258,"/data/upload/5/74426174-fb701d0e6a714179959ac8e1e4e2cb88.jpg",1.181,"2025-12-20T11:00:26.635798Z"
86
+ 259,"1","SUGGESTIVE","2025-12-20T11:00:28.021230Z",259,"/data/upload/5/68adaf6f-fb61755f0c2afaafdd9aa36f7632cbe5.jpg",1.179,"2025-12-20T11:00:28.021251Z"
87
+ 260,"1","FAMILY_SAFE","2025-12-20T11:00:30.717567Z",260,"/data/upload/5/c61c6042-fb607386dd384e6b0d53ef878bbffc55.jpg",2.494,"2025-12-20T11:00:30.717588Z"
88
+ 261,"1","FAMILY_SAFE","2025-12-20T11:00:32.367921Z",261,"/data/upload/5/7ec3bcbe-fb2e1bdb34ace56b99befc2547a8b83d.jpg",1.441,"2025-12-20T11:00:32.367944Z"
89
+ 262,"1","FAMILY_SAFE","2025-12-20T11:00:34.782659Z",262,"/data/upload/5/bec475e6-fb3c593276f0f040a7450ca213d97882.jpg",2.203,"2025-12-20T11:00:34.782680Z"
90
+ 263,"1","FAMILY_SAFE","2025-12-20T11:00:41.245651Z",263,"/data/upload/5/966460f7-fb50d0f85eb7e491722e734b961926f0.jpg",6.266,"2025-12-20T11:00:41.245662Z"
91
+ 264,"1","FAMILY_SAFE","2025-12-20T11:00:42.448801Z",264,"/data/upload/5/243861f5-fb468ddeda48f68ec3539bebe07b92b4.jpg",1.016,"2025-12-20T11:00:42.448823Z"
92
+ 265,"1","SUGGESTIVE","2025-12-20T11:00:45.419350Z",265,"/data/upload/5/54045990-fb1c02d4c50a59be3fe3cb249de403e0.jpg",2.76,"2025-12-20T11:00:45.419370Z"
93
+ 266,"1","UNCERTAIN","2025-12-20T11:00:48.312669Z",266,"/data/upload/5/7439530f-faf1f5d9d6e51a84ce632df317dd5c5c.jpg",2.68,"2025-12-20T11:00:48.312691Z"
94
+ 267,"1","UNCERTAIN","2025-12-20T11:00:51.135138Z",267,"/data/upload/5/e0eec550-faf27f9511974b5ef529ba718677c5f4.jpg",2.607,"2025-12-20T11:00:51.135161Z"
95
+ 268,"1","FAMILY_SAFE","2025-12-20T11:00:53.511267Z",268,"/data/upload/5/1101ce4b-fab7821aae25aff865b20b0d33d86900.jpg",2.16,"2025-12-20T11:00:53.511289Z"
96
+ 269,"1","FAMILY_SAFE","2025-12-20T11:00:55.652412Z",269,"/data/upload/5/26464718-fac69b44cccb94236c9f905bed46158f.jpg",1.926,"2025-12-20T11:00:55.652431Z"
97
+ 270,"1","FAMILY_SAFE","2025-12-20T11:00:58.249487Z",270,"/data/upload/5/d588345d-fad53b443bc3e52bada85783afdac562.jpg",2.37,"2025-12-20T11:00:58.249507Z"
98
+ 271,"1","UNCERTAIN","2025-12-20T11:01:01.962081Z",271,"/data/upload/5/7f1fa2e7-fad69c88bcef4434a55b3bf3854b3e56.jpg",3.505,"2025-12-20T11:01:01.962105Z"
99
+ 272,"1","FAMILY_SAFE","2025-12-20T11:01:03.385673Z",272,"/data/upload/5/717afd8f-fa6ef41aabda28f51249407ce46a827e.jpg",1.212,"2025-12-20T11:01:03.385696Z"
100
+ 273,"1","UNCERTAIN","2025-12-20T11:01:07.395182Z",273,"/data/upload/5/191a25a2-fa7b42eb84363adcd60082a3a7f02f84.jpg",3.793,"2025-12-20T11:01:07.395203Z"
101
+ 274,"1","FAMILY_SAFE","2025-12-20T11:01:09.348245Z",274,"/data/upload/5/41febcdf-fa9e77f0788145d7ac42edcffb6e9dee.jpg",1.738,"2025-12-20T11:01:09.348266Z"
102
+ 275,"1","UNCERTAIN","2025-12-20T11:01:11.745116Z",275,"/data/upload/5/4bb4ff8b-fa9f2688e1b4e92538569ecb9efecf2e.jpg",2.158,"2025-12-20T11:01:11.745139Z"
103
+ 276,"1","UNCERTAIN","2025-12-20T11:01:13.426503Z",276,"/data/upload/5/12aefe0a-fa87c0f54b8cb07a14fc311c369e9a4c.jpg",1.462,"2025-12-20T11:01:13.426526Z"
104
+ 277,"1","SUGGESTIVE","2025-12-20T11:01:15.136863Z",277,"/data/upload/5/0f927896-fa94a466d4178dcd4c762f7567c11147.jpg",1.495,"2025-12-20T11:01:15.136886Z"
105
+ 278,"1","FAMILY_SAFE","2025-12-20T11:01:17.081107Z",278,"/data/upload/5/3b82e875-faa13416b0d8d2adc2b6815dd1e6c82d.jpg",1.723,"2025-12-20T11:01:17.081130Z"
106
+ 279,"1","SUGGESTIVE","2025-12-20T11:01:20.677830Z",279,"/data/upload/5/7a5551c7-fa5b56c4e382fd4dca384daf1768acb4.jpg",3.381,"2025-12-20T11:01:20.677853Z"
107
+ 280,"1","FAMILY_SAFE","2025-12-20T11:01:23.266159Z",280,"/data/upload/5/abf1e90d-fa5c6d304163052761d4453c693c202d.jpg",2.375,"2025-12-20T11:01:23.266179Z"
108
+ 281,"1","UNCERTAIN","2025-12-20T11:01:25.725201Z",281,"/data/upload/5/150e925a-fa6a15e903e7949093f4ec11fcffdb01.jpg",2.237,"2025-12-20T11:01:25.725220Z"
109
+ 282,"1","SUGGESTIVE","2025-12-20T11:01:31.896568Z",282,"/data/upload/5/76aa5a7e-fa41e4a96249c0b8ed90389733c081d8.jpg",5.951,"2025-12-20T11:01:31.896591Z"
110
+ 283,"1","SUGGESTIVE","2025-12-20T11:01:34.383132Z",283,"/data/upload/5/891276e7-fa63fcd0e11eb612b2c8f1ade43de7c8.jpg",2.271,"2025-12-20T11:01:34.383150Z"
111
+ 284,"1","FAMILY_SAFE","2025-12-20T11:01:36.092103Z",284,"/data/upload/5/0fea6f76-fa412fb78ec94ab051ac0880e255a897.jpg",1.495,"2025-12-20T11:01:36.092126Z"
112
+ 285,"1","UNCERTAIN","2025-12-20T11:01:41.235766Z",285,"/data/upload/5/7cb410fd-fa1a50f4102b66555a1a05afa9ddbf3c.jpg",4.925,"2025-12-20T11:01:41.235780Z"
113
+ 286,"1","UNCERTAIN","2025-12-20T11:01:42.954786Z",286,"/data/upload/5/907cb3ad-fa1dab55d34f1c7140ea41a1b3ab5eae.jpg",1.512,"2025-12-20T11:01:42.954808Z"
114
+ 287,"1","FAMILY_SAFE","2025-12-20T11:01:45.714292Z",287,"/data/upload/5/bf824357-fa2c514037f31c0ab98d66373d371e3a.jpg",2.546,"2025-12-20T11:01:45.714312Z"
115
+ 288,"1","FAMILY_SAFE","2025-12-20T11:01:47.923730Z",288,"/data/upload/5/f953d2e3-f9f74d590b0f4d8da8ba20a07bc2a596.jpg",1.984,"2025-12-20T11:01:47.923749Z"
116
+ 289,"1","FAMILY_SAFE","2025-12-20T11:01:49.235145Z",289,"/data/upload/5/db2c891b-f9fc35243c2c7592c2d5858be342a34d.jpg",1.084,"2025-12-20T11:01:49.235165Z"
117
+ 290,"1","UNCERTAIN","2025-12-20T11:02:02.235317Z",290,"/data/upload/5/53bd404d-fa068c5e8fe813fbb1d90b5a0744e172.jpg",12.779,"2025-12-20T11:02:02.235338Z"
118
+ 291,"1","SUGGESTIVE","2025-12-20T11:02:04.115171Z",291,"/data/upload/5/a23fc1cb-fa075d603e631d04c9871dbb00ed0a14.jpg",1.641,"2025-12-20T11:02:04.115197Z"
119
+ 292,"1","SUGGESTIVE","2025-12-20T11:02:11.567375Z",292,"/data/upload/5/ee0e3f1d-fa1607fba97dd47d889e0c8eb0a82624.jpg",7.221,"2025-12-20T11:02:11.567396Z"
120
+ 293,"1","SUGGESTIVE","2025-12-20T11:02:13.565368Z",293,"/data/upload/5/3aef96bb-f9d6c4a53c12013b5bbb6e830275486e.jpg",1.767,"2025-12-20T11:02:13.565392Z"
121
+ 294,"1","SUGGESTIVE","2025-12-20T11:02:15.066066Z",294,"/data/upload/5/9f665793-f9ea859c158d6cece283b619afdf2f49.jpg",1.266,"2025-12-20T11:02:15.066077Z"
122
+ 295,"1","UNCERTAIN","2025-12-20T11:02:23.249309Z",295,"/data/upload/5/db0aaa30-f9a1f16e76e3adad3ce2ed9fe0c7a03f.jpg",7.982,"2025-12-20T11:02:23.249329Z"
123
+ 296,"1","UNCERTAIN","2025-12-20T11:02:25.540717Z",296,"/data/upload/5/03abd3cc-f9aaa2af6fea2261610e48ed5e11ede4.jpg",2.049,"2025-12-20T11:02:25.540740Z"
124
+ 297,"1","SUGGESTIVE","2025-12-20T11:02:27.850423Z",297,"/data/upload/5/d741a3e7-f9b3403712c87698322446ef48a0027c.jpg",2.078,"2025-12-20T11:02:27.850443Z"
125
+ 298,"1","SUGGESTIVE","2025-12-20T11:02:29.208512Z",298,"/data/upload/5/b502117c-f9bd44609a914f7aabd3a901d457b1e7.jpg",1.124,"2025-12-20T11:02:29.208536Z"
126
+ 299,"1","SUGGESTIVE","2025-12-20T11:02:30.779695Z",299,"/data/upload/5/96ba7ff4-f984b6e28aa8e0e13f71bd41a8c65194.jpg",1.306,"2025-12-20T11:02:30.779716Z"
127
+ 300,"1","FAMILY_SAFE","2025-12-20T11:02:32.419467Z",300,"/data/upload/5/85f90f88-f96b6fc62dadba4eb00b5cfaa1168b87.jpg",1.397,"2025-12-20T11:02:32.419489Z"
128
+ 301,"1","SUGGESTIVE","2025-12-20T11:02:34.614235Z",301,"/data/upload/5/32e49293-f96bffb22c42bd3beb968d5ff5583c6b.jpg",1.947,"2025-12-20T11:02:34.614255Z"
129
+ 302,"1","FAMILY_SAFE","2025-12-20T11:02:36.119276Z",302,"/data/upload/5/263d89d9-f96c4c07dcded0f0fd1aa150a693a181.jpg",1.277,"2025-12-20T11:02:36.119287Z"
130
+ 303,"1","SUGGESTIVE","2025-12-20T11:02:42.328622Z",303,"/data/upload/5/78e8d1c7-f96c7473c85142a222056be7c7759184.jpg",5.968,"2025-12-20T11:02:42.328646Z"
131
+ 304,"1","FAMILY_SAFE","2025-12-20T11:02:44.266093Z",304,"/data/upload/5/0fc4362d-f9610ac04fe21d9bd96689b8c6ae1669.jpg",1.719,"2025-12-20T11:02:44.266106Z"
132
+ 305,"1","UNCERTAIN","2025-12-20T11:02:46.577757Z",305,"/data/upload/5/24f3bf36-f9633749641995381ff42d72352bafe2.jpg",1.997,"2025-12-20T11:02:46.577780Z"
133
+ 306,"1","UNCERTAIN","2025-12-20T11:02:50.471187Z",306,"/data/upload/5/3216e20d-f93c3f8af10c70801ce921842c77afc4.jpg",3.658,"2025-12-20T11:02:50.471209Z"
134
+ 307,"1","SUGGESTIVE","2025-12-20T11:02:52.511045Z",307,"/data/upload/5/93bfe360-f95143e4ae1515b8bf5c2d813aa36bbc.jpg",1.805,"2025-12-20T11:02:52.511066Z"
135
+ 308,"1","FAMILY_SAFE","2025-12-20T11:02:54.818714Z",308,"/data/upload/5/0a08bf3b-f954833a10de7c149ac73c5b4af4c189.jpg",2.065,"2025-12-20T11:02:54.818738Z"
136
+ 309,"1","SUGGESTIVE","2025-12-20T11:02:58.010625Z",309,"/data/upload/5/a54eda5e-f8fdf05663173fa7dc689333e3f21f23.jpg",2.942,"2025-12-20T11:02:58.010648Z"
137
+ 310,"1","FAMILY_SAFE","2025-12-20T11:03:00.421390Z",310,"/data/upload/5/29f522c1-f91a65be37a0e49d78ac4f6bc0cfcb61.jpg",2.172,"2025-12-20T11:03:00.421413Z"
138
+ 311,"1","SUGGESTIVE","2025-12-20T11:03:03.898048Z",311,"/data/upload/5/43ccd6b7-f8b2c595800cb08b05b4f2503fdbbced.jpg",3.24,"2025-12-20T11:03:03.898062Z"
139
+ 312,"1","UNCERTAIN","2025-12-20T11:03:09.233388Z",312,"/data/upload/5/c7f29f62-f8b38d085dc15913e26ed1580d0cb644.jpg",5.086,"2025-12-20T11:03:09.233409Z"
140
+ 313,"1","UNCERTAIN","2025-12-20T11:03:11.716697Z",313,"/data/upload/5/ca35d72e-f8c8ee06b8b818d1f0f5229014ee8760.jpg",2.241,"2025-12-20T11:03:11.716721Z"
141
+ 314,"1","SUGGESTIVE","2025-12-20T11:03:13.523649Z",314,"/data/upload/5/65120b69-f8ee23b284b41157bb5b9eb488cf5828.jpg",1.567,"2025-12-20T11:03:13.523672Z"
142
+ 315,"1","SUGGESTIVE","2025-12-20T11:03:16.281232Z",315,"/data/upload/5/4abc60a4-f8a72d3f92f7e3793a093556c5b6b446.jpg",2.511,"2025-12-20T11:03:16.281246Z"
143
+ 316,"1","SUGGESTIVE","2025-12-20T11:03:17.643247Z",316,"/data/upload/5/ee55d94c-f8a860d078da463b28618029d547eca0.jpg",1.145,"2025-12-20T11:03:17.643268Z"
144
+ 317,"1","SUGGESTIVE","2025-12-20T11:03:18.870690Z",317,"/data/upload/5/42ea7be6-f8a950c61b1b181a99bb6837f76ddcf5.jpg",0.984,"2025-12-20T11:03:18.870710Z"
145
+ 318,"1","FAMILY_SAFE","2025-12-20T11:03:21.494347Z",318,"/data/upload/5/2b96cec7-f87b6da37656aef1c82f6841cdfd216a.jpg",2.384,"2025-12-20T11:03:21.494369Z"
146
+ 319,"1","SUGGESTIVE","2025-12-20T11:03:23.292588Z",319,"/data/upload/5/a4651f0f-f8714d6a5bafd4d86377bf04957f37e5.jpg",1.557,"2025-12-20T11:03:23.292610Z"
147
+ 320,"1","FAMILY_SAFE","2025-12-20T11:03:26.120251Z",320,"/data/upload/5/fee5f161-f8856cf1b3bd39861cb9f95337cd0992.jpg",2.578,"2025-12-20T11:03:26.120276Z"
148
+ 321,"1","FAMILY_SAFE","2025-12-20T11:03:30.645431Z",321,"/data/upload/5/26999a9e-f8869ae287068ce36b4b05663da6713a.jpg",4.259,"2025-12-20T11:03:30.645454Z"
149
+ 322,"1","UNCERTAIN","2025-12-20T11:03:33.317684Z",322,"/data/upload/5/2d689dc9-f852dde27ecdc40533bb40683087f7e1.jpg",2.432,"2025-12-20T11:03:33.317706Z"
150
+ 323,"1","SUGGESTIVE","2025-12-20T11:03:41.346226Z",323,"/data/upload/5/207cc31f-f86351d67e2629dc35f1e524915fcd11.jpg",7.784,"2025-12-20T11:03:41.346247Z"
151
+ 324,"1","UNCERTAIN","2025-12-20T11:03:43.247407Z",324,"/data/upload/5/f3898f2e-f8447631bc00c8f2467b1a20ee6bfa78.jpg",1.657,"2025-12-20T11:03:43.247431Z"
152
+ 325,"1","UNCERTAIN","2025-12-20T11:03:46.517135Z",325,"/data/upload/5/8cb018af-f8542037683b99dbc67c7f7d6c5a46b3.jpg",3.02,"2025-12-20T11:03:46.517159Z"
153
+ 326,"1","FAMILY_SAFE","2025-12-20T11:03:47.992268Z",326,"/data/upload/5/5d6063fc-f822f1d61d47f66997ce6f86c974a4a4.jpg",1.222,"2025-12-20T11:03:47.992289Z"
154
+ 327,"1","SUGGESTIVE","2025-12-20T11:03:49.750452Z",327,"/data/upload/5/57bec3b8-f8222b4da2ca05b7b639120cac5f92f2.jpg",1.508,"2025-12-20T11:03:49.750472Z"
155
+ 328,"1","UNCERTAIN","2025-12-20T11:04:15.579735Z",328,"/data/upload/5/918752f9-f7e4163d39bf966f1d403bfcf469cc5f.jpg",25.57,"2025-12-20T11:04:15.579758Z"
156
+ 329,"1","SUGGESTIVE","2025-12-20T11:04:17.612520Z",329,"/data/upload/5/bdce8a23-f7f267f10da481cbc97981f022b36a07.jpg",1.786,"2025-12-20T11:04:17.612541Z"
157
+ 330,"1","SUGGESTIVE","2025-12-20T11:04:19.295850Z",330,"/data/upload/5/d9e26b6c-f7de775f4820d021c70c42e8785c047b.jpg",1.437,"2025-12-20T11:04:19.295877Z"
158
+ 331,"1","UNCERTAIN","2025-12-20T11:04:22.473350Z",331,"/data/upload/5/654d822f-f7e1c87ca29b75a4e59263db1e0f59a1.jpg",2.915,"2025-12-20T11:04:22.473372Z"
159
+ 332,"1","FAMILY_SAFE","2025-12-20T11:04:24.169415Z",332,"/data/upload/5/13f55350-f7c55782f457e6c3c4a17c4f4254a946.jpg",1.45,"2025-12-20T11:04:24.169437Z"
160
+ 333,"1","UNCERTAIN","2025-12-20T11:04:27.020537Z",333,"/data/upload/5/bf4fd201-f7aa8582cc615c5efc747a723ed54f9e.jpg",2.604,"2025-12-20T11:04:27.020559Z"
161
+ 334,"1","UNCERTAIN","2025-12-20T11:04:29.611666Z",334,"/data/upload/5/6520255c-f79bc54aa05611d22b65098a11e71a34.jpg",2.361,"2025-12-20T11:04:29.611687Z"
162
+ 335,"1","UNCERTAIN","2025-12-20T11:04:31.732147Z",335,"/data/upload/5/e7695c72-f7761769f7463d55a2df9300c743a324.jpg",1.898,"2025-12-20T11:04:31.732160Z"
163
+ 336,"1","UNCERTAIN","2025-12-20T11:04:33.665025Z",336,"/data/upload/5/681dcc99-f77695115dd9a221c2239aece214b49e.jpg",1.721,"2025-12-20T11:04:33.665047Z"
164
+ 337,"1","SUGGESTIVE","2025-12-20T11:04:41.516887Z",337,"/data/upload/5/bc15263c-f745fcf48c946c9d4393fd48f4cffc0e.jpg",7.623,"2025-12-20T11:04:41.516904Z"
165
+ 338,"1","UNCERTAIN","2025-12-20T11:04:45.123787Z",338,"/data/upload/5/88c7b383-f7303e5b824a66239e6afcd87a8ef62f.jpg",3.373,"2025-12-20T11:04:45.123811Z"
166
+ 339,"1","SUGGESTIVE","2025-12-20T11:04:46.709218Z",339,"/data/upload/5/9342d436-f73693da56901519a595fead5f85d069.jpg",1.351,"2025-12-20T11:04:46.709240Z"
167
+ 340,"1","SUGGESTIVE","2025-12-20T11:04:52.783428Z",340,"/data/upload/5/1bfa81bf-f71e69c1823525251dc2201172364595.jpg",5.825,"2025-12-20T11:04:52.783450Z"
168
+ 341,"1","FAMILY_SAFE","2025-12-20T11:04:53.971709Z",341,"/data/upload/5/37fbd106-f72a254e0142a25852937f68f91eeff0.jpg",0.955,"2025-12-20T11:04:53.971730Z"
169
+ 342,"1","UNCERTAIN","2025-12-20T11:04:55.662011Z",342,"/data/upload/5/75ec4326-f6f57e02e3025fda7e6587ada129b5da.jpg",1.454,"2025-12-20T11:04:55.662034Z"
170
+ 343,"1","UNCERTAIN","2025-12-20T11:04:57.428398Z",343,"/data/upload/5/112d370c-f6e10f65554a282ad88561eb6606b673.jpg",1.526,"2025-12-20T11:04:57.428418Z"
171
+ 344,"1","FAMILY_SAFE","2025-12-20T11:05:01.347989Z",344,"/data/upload/5/258f99f7-f6cc5af7cdd7d677b39960916ddce332.jpg",3.699,"2025-12-20T11:05:01.348002Z"
172
+ 345,"1","SUGGESTIVE","2025-12-20T11:05:03.011431Z",345,"/data/upload/5/e654ed6f-f6a9eed8dbdaaebe8483cb023b1cc155.jpg",1.449,"2025-12-20T11:05:03.011455Z"
173
+ 346,"1","SUGGESTIVE","2025-12-20T11:05:04.326722Z",346,"/data/upload/5/32c60d49-f6afce2e8ed7f791361d5f859faf6f93.jpg",1.08,"2025-12-20T11:05:04.326746Z"
174
+ 347,"1","FAMILY_SAFE","2025-12-20T11:05:09.278186Z",347,"/data/upload/5/8e037415-f6b1bdcd5cc1123f61514494633c0261.jpg",4.716,"2025-12-20T11:05:09.278212Z"
175
+ 348,"1","SUGGESTIVE","2025-12-20T11:05:12.797276Z",348,"/data/upload/5/1a95de13-f6a20f3450549f295c9d440e3b50fe4b.jpg",3.287,"2025-12-20T11:05:12.797298Z"
176
+ 349,"1","SUGGESTIVE","2025-12-20T11:05:14.528715Z",349,"/data/upload/5/d8e036ec-f69ba433d87943ba7fe96bdee75c0633.jpg",1.497,"2025-12-20T11:05:14.528736Z"
177
+ 350,"1","FAMILY_SAFE","2025-12-20T11:05:15.976098Z",350,"/data/upload/5/150cadee-f6913caa3c66a906611cd76004dc0e89.jpg",1.216,"2025-12-20T11:05:15.976121Z"
178
+ 351,"1","SUGGESTIVE","2025-12-20T11:05:17.479813Z",351,"/data/upload/5/d45cdae6-f69459f653bf0a475f7003c64ad87484.jpg",1.26,"2025-12-20T11:05:17.479839Z"
179
+ 352,"1","UNCERTAIN","2025-12-20T11:05:25.316547Z",352,"/data/upload/5/3ba7cebd-f67d4c2318dfb10031f4d3a40e767467.jpg",7.598,"2025-12-20T11:05:25.316570Z"
180
+ 353,"1","SUGGESTIVE","2025-12-20T11:05:27.240613Z",353,"/data/upload/5/b947afc5-f68da9a3284e87b939e43ed78c2ba697.jpg",1.686,"2025-12-20T11:05:27.240632Z"
181
+ 354,"1","SUGGESTIVE","2025-12-20T11:05:28.652996Z",354,"/data/upload/5/548c342e-f6555d7bd2ce56360984e39d06eb6d3e.jpg",1.166,"2025-12-20T11:05:28.653020Z"
182
+ 355,"1","SUGGESTIVE","2025-12-20T11:05:35.325213Z",355,"/data/upload/5/2d8b3b40-f6502184b0f514712ec44e93e4cf38ed.jpg",6.406,"2025-12-20T11:05:35.325235Z"
183
+ 356,"1","SUGGESTIVE","2025-12-20T11:05:40.126220Z",356,"/data/upload/5/8d9e6ed8-f60a078c2adb5e2ed7984643b1a3d6ad.jpg",4.563,"2025-12-20T11:05:40.126246Z"
184
+ 357,"1","UNCERTAIN","2025-12-20T11:05:42.162966Z",357,"/data/upload/5/915ec646-f60d0a9ef35ff32f41167f002f604218.jpg",1.798,"2025-12-20T11:05:42.162991Z"
185
+ 358,"1","SUGGESTIVE","2025-12-20T11:05:43.617887Z",358,"/data/upload/5/ac9956e6-f61b32b46a5df8b7604a567f4101de1a.jpg",1.19,"2025-12-20T11:05:43.617910Z"
186
+ 359,"1","UNCERTAIN","2025-12-20T11:05:45.343816Z",359,"/data/upload/5/38e3e29f-f6096fb5a8815393de8d3ce6046dcfa0.jpg",1.49,"2025-12-20T11:05:45.343840Z"
187
+ 360,"1","SUGGESTIVE","2025-12-20T11:05:46.798111Z",360,"/data/upload/5/4cd02552-f627490d7770e822b0646ecc26314049.jpg",1.218,"2025-12-20T11:05:46.798135Z"
188
+ 361,"1","FAMILY_SAFE","2025-12-20T11:05:48.044400Z",361,"/data/upload/5/96423c5a-f5d25e48cb07c00f6b4749341afd4fe9.jpg",1.025,"2025-12-20T11:05:48.044411Z"
189
+ 362,"1","UNCERTAIN","2025-12-20T11:05:52.547956Z",362,"/data/upload/5/b2bdb576-f5f0b5059aa1f31f18454612c7b44cb4.jpg",4.281,"2025-12-20T11:05:52.547975Z"
190
+ 363,"1","FAMILY_SAFE","2025-12-20T11:05:53.790882Z",363,"/data/upload/5/d9b4f6e3-f5f15fde94650b49c19b1108e7baacaa.jpg",0.999,"2025-12-20T11:05:53.790906Z"
191
+ 364,"1","SUGGESTIVE","2025-12-20T11:05:55.131841Z",364,"/data/upload/5/78e1ef6c-f5f68931f14533c57ecfef9acb38dc7d.jpg",1.07,"2025-12-20T11:05:55.131863Z"
192
+ 365,"1","SUGGESTIVE","2025-12-20T11:05:56.800233Z",365,"/data/upload/5/95214b1d-f5fbc4a6c74108a929561383bb572cd4.jpg",1.427,"2025-12-20T11:05:56.800255Z"
193
+ 366,"1","FAMILY_SAFE","2025-12-20T11:05:58.647293Z",366,"/data/upload/5/52f9323f-f5a0911271d9a6ed11455f9f6ff7eb66.jpg",1.608,"2025-12-20T11:05:58.647316Z"
194
+ 367,"1","FAMILY_SAFE","2025-12-20T11:06:06.211872Z",367,"/data/upload/5/437ce914-f5c13ab4ed01cc7efd808da48b8a5457.jpg",7.34,"2025-12-20T11:06:06.211884Z"
195
+ 368,"1","UNCERTAIN","2025-12-20T11:06:08.652944Z",368,"/data/upload/5/3472223a-f5996eb32ca496db64f8988cee9a8878.jpg",2.2,"2025-12-20T11:06:08.652968Z"
196
+ 369,"1","SUGGESTIVE","2025-12-20T11:06:11.313206Z",369,"/data/upload/5/79fb6415-f57b16711597d1da4c6c22c57af8a35a.jpg",2.419,"2025-12-20T11:06:11.313226Z"
197
+ 370,"1","SUGGESTIVE","2025-12-20T11:06:12.945194Z",370,"/data/upload/5/8b7383e6-f57fb11b1c8484d4642295155ba8db7b.jpg",1.388,"2025-12-20T11:06:12.945218Z"
198
+ 371,"1","SUGGESTIVE","2025-12-20T11:06:14.500956Z",371,"/data/upload/5/ae1beb31-f58d8b3fa1b33fbccd8c8d71e0fe1a2c.jpg",1.34,"2025-12-20T11:06:14.500976Z"
199
+ 372,"1","FAMILY_SAFE","2025-12-20T11:06:16.952949Z",372,"/data/upload/5/6be40274-f55c44bbbe7e56c04a616cd84479e46f.jpg",2.211,"2025-12-20T11:06:16.952971Z"
200
+ 373,"1","SUGGESTIVE","2025-12-20T11:06:19.670664Z",373,"/data/upload/5/79a2f826-f55deb0d18263824d1cef3e9ab9f1785.jpg",2.473,"2025-12-20T11:06:19.670687Z"
201
+ 374,"1","FAMILY_SAFE","2025-12-20T11:06:21.212367Z",374,"/data/upload/5/eb3f6ce4-f572b605f472227d2aa10d6905b535e3.jpg",1.299,"2025-12-20T11:06:21.212385Z"
202
+ 375,"1","SUGGESTIVE","2025-12-20T11:06:26.044664Z",375,"/data/upload/5/86690dd2-f573acc9c7d68bfa33ffaf904bdcbee1.jpg",4.589,"2025-12-20T11:06:26.044684Z"
203
+ 376,"1","FAMILY_SAFE","2025-12-20T11:06:27.193909Z",376,"/data/upload/5/cb81d853-f5641be3510ca443208381edefb8dab9.jpg",0.899,"2025-12-20T11:06:27.193934Z"
204
+ 377,"1","UNCERTAIN","2025-12-20T11:06:29.526765Z",377,"/data/upload/5/f3e026cb-f55636ad8ac978de1925576a495326e8.jpg",2.09,"2025-12-20T11:06:29.526787Z"
205
+ 378,"1","FAMILY_SAFE","2025-12-20T11:06:32.430924Z",378,"/data/upload/5/dd964efd-f543f9d63f3caf812049461fe8e82f66.jpg",2.693,"2025-12-20T11:06:32.430947Z"
206
+ 379,"1","SUGGESTIVE","2025-12-20T11:06:34.175038Z",379,"/data/upload/5/cddc368c-f555bcb967a62b3420b1563eabf14e03.jpg",1.491,"2025-12-20T11:06:34.175062Z"
207
+ 380,"1","FAMILY_SAFE","2025-12-20T11:06:42.126381Z",380,"/data/upload/5/8a1bce2e-f531a8694f3e826cc46790d3d2d210f3.jpg",7.745,"2025-12-20T11:06:42.126403Z"
208
+ 381,"1","FAMILY_SAFE","2025-12-20T11:06:44.453585Z",381,"/data/upload/5/4580faae-f532a291cff7ba273fcfbd81e466147a.jpg",2.048,"2025-12-20T11:06:44.453609Z"
209
+ 382,"1","SUGGESTIVE","2025-12-20T11:06:47.490081Z",382,"/data/upload/5/cd5e94e9-f4eed19eaa6f1a5b7f3bf3fdab620193.jpg",2.791,"2025-12-20T11:06:47.490101Z"
210
+ 383,"1","SUGGESTIVE","2025-12-20T11:06:48.990534Z",383,"/data/upload/5/e0abb514-f4f6cec6badab17d10b40557e6e9ceae.jpg",1.264,"2025-12-20T11:06:48.990561Z"
211
+ 384,"1","SUGGESTIVE","2025-12-20T11:06:50.556145Z",384,"/data/upload/5/3265ae1c-f4f91efff09fee24636b9b50d58fcf25.jpg",1.321,"2025-12-20T11:06:50.556167Z"
212
+ 385,"1","SUGGESTIVE","2025-12-20T11:06:51.603016Z",385,"/data/upload/5/e2e7bbee-f4f964fc3d06d2990c04caa8fc0e5295.jpg",0.805,"2025-12-20T11:06:51.603035Z"
213
+ 386,"1","SUGGESTIVE","2025-12-20T11:06:52.717199Z",386,"/data/upload/5/8f9b1b13-f4fe1ba40065807edb72c03485dff3d2.jpg",0.867,"2025-12-20T11:06:52.717223Z"
214
+ 387,"1","SUGGESTIVE","2025-12-20T11:06:53.681878Z",387,"/data/upload/5/9ee74b02-f50238c6721caa983df9656eef22236b.jpg",0.719,"2025-12-20T11:06:53.681898Z"
215
+ 388,"1","SUGGESTIVE","2025-12-20T11:06:54.729565Z",388,"/data/upload/5/97429c97-f50926a996167d5a55ea0e3260627572.jpg",0.798,"2025-12-20T11:06:54.729585Z"
216
+ 389,"1","SUGGESTIVE","2025-12-20T11:06:55.951339Z",389,"/data/upload/5/6775a784-f5072276faee8d0385236b2937d48a33.jpg",0.951,"2025-12-20T11:06:55.951358Z"
217
+ 390,"1","FAMILY_SAFE","2025-12-20T11:07:00.396266Z",390,"/data/upload/5/735adacd-f4d811181ca6367340272ea2817eebb6.jpg",4.194,"2025-12-20T11:07:00.396290Z"
218
+ 391,"1","SUGGESTIVE","2025-12-20T11:07:01.741988Z",391,"/data/upload/5/f2450298-f4e7b715922c1d07a2f7501b722bb12e.jpg",1.1,"2025-12-20T11:07:01.742011Z"
219
+ 392,"1","SUGGESTIVE","2025-12-20T11:07:02.921853Z",392,"/data/upload/5/a04ba9a7-f4aee07e50f09863edf149352e42f73e.jpg",0.935,"2025-12-20T11:07:02.921877Z"
220
+ 393,"1","SUGGESTIVE","2025-12-20T11:07:04.282533Z",393,"/data/upload/5/cdff04e2-f4c226e68f48b630dfd5b8f032c281f3.jpg",1.107,"2025-12-20T11:07:04.282556Z"
221
+ 394,"1","UNCERTAIN","2025-12-20T11:07:07.375699Z",394,"/data/upload/5/1c2faeb4-f4c868518f6194c3d708d889b79c4357.jpg",2.836,"2025-12-20T11:07:07.375722Z"
222
+ 395,"1","UNCERTAIN","2025-12-20T11:07:09.907880Z",395,"/data/upload/5/da36d596-f48abfffe24a134a2605d63c095d5601.jpg",2.28,"2025-12-20T11:07:09.907901Z"
223
+ 396,"1","SUGGESTIVE","2025-12-20T11:07:11.242457Z",396,"/data/upload/5/9e7afccb-f49cbda7d6c418aa68c47ac16678be9d.jpg",1.085,"2025-12-20T11:07:11.242482Z"
224
+ 397,"1","FAMILY_SAFE","2025-12-20T11:07:13.746642Z",397,"/data/upload/5/62a97430-f49d857cb54b7ad2a3a8bcd6c85a76d2.jpg",2.249,"2025-12-20T11:07:13.746663Z"
225
+ 398,"1","FAMILY_SAFE","2025-12-20T11:07:17.639369Z",398,"/data/upload/5/f79720c4-f49f729c28e9aafef241b3e5d1365ebb.jpg",3.641,"2025-12-20T11:07:17.639394Z"
226
+ 399,"1","SUGGESTIVE","2025-12-20T11:07:20.280399Z",399,"/data/upload/5/1fa27620-f46fcea94e5309ef137996d590f947a8.jpg",2.395,"2025-12-20T11:07:20.280420Z"
227
+ 400,"1","FAMILY_SAFE","2025-12-20T11:07:21.492310Z",400,"/data/upload/5/a8f6045f-f44bd04a46d585f08df78d744a9920e0.jpg",0.964,"2025-12-20T11:07:21.492334Z"
228
+ 401,"1","SUGGESTIVE","2025-12-20T11:07:22.755497Z",401,"/data/upload/5/9d5f7026-f442e9b50f65ce8e992c2d6d99045422.jpg",1.013,"2025-12-20T11:07:22.755525Z"
229
+ 402,"1","FAMILY_SAFE","2025-12-20T11:07:23.978630Z",402,"/data/upload/5/d87227e3-f4591e17584e87f5ae145946468488a1.jpg",0.971,"2025-12-20T11:07:23.978651Z"
230
+ 403,"1","UNCERTAIN","2025-12-20T11:07:25.879929Z",403,"/data/upload/5/5c828279-f45776fd9cff138a422bc299fda11907.jpg",1.693,"2025-12-20T11:07:25.879950Z"
231
+ 404,"1","SUGGESTIVE","2025-12-20T11:07:27.548623Z",404,"/data/upload/5/5c339677-f450689a8cf22e1c4471ef894ea28a81.jpg",1.422,"2025-12-20T11:07:27.548646Z"
232
+ 405,"1","SUGGESTIVE","2025-12-20T11:07:28.896320Z",405,"/data/upload/5/b47e520f-f453124a40f3bdbbe0c9dfe4996b6844.jpg",1.11,"2025-12-20T11:07:28.896332Z"
233
+ 406,"1","SUGGESTIVE","2025-12-20T11:07:30.624164Z",406,"/data/upload/5/3a55ff4b-f40d7fc2218b3f42761e7abe785c7785.jpg",1.497,"2025-12-20T11:07:30.624188Z"
234
+ 407,"1","UNCERTAIN","2025-12-20T11:07:32.486345Z",407,"/data/upload/5/ab4e8b9b-f433c523573d6c17da4f5f3da31fec75.jpg",1.618,"2025-12-20T11:07:32.486367Z"
235
+ 408,"1","FAMILY_SAFE","2025-12-20T11:07:34.295061Z",408,"/data/upload/5/c5a2e52f-f412680ce7ff76d5c276884092c008f3.jpg",1.556,"2025-12-20T11:07:34.295084Z"
236
+ 409,"1","FAMILY_SAFE","2025-12-20T11:07:35.358829Z",409,"/data/upload/5/2707ecab-f432252b9d85a485c9e4b3097633f71e.jpg",0.819,"2025-12-20T11:07:35.358854Z"
237
+ 410,"1","SUGGESTIVE","2025-12-20T11:07:37.054116Z",410,"/data/upload/5/79a56ecc-f3e1b0aced852718d2b2cc7208728b62.jpg",1.446,"2025-12-20T11:07:37.054140Z"
238
+ 411,"1","FAMILY_SAFE","2025-12-20T11:07:38.329402Z",411,"/data/upload/5/84efc00c-f3ebae6cdb55ebee567dc5937d26f433.jpg",1.025,"2025-12-20T11:07:38.329419Z"
239
+ 412,"1","SUGGESTIVE","2025-12-20T11:07:39.328789Z",412,"/data/upload/5/916b7794-f3f76e5bd2aae5290c40831d2dba06fb.jpg",0.762,"2025-12-20T11:07:39.328810Z"
240
+ 413,"1","SUGGESTIVE","2025-12-20T11:07:40.448655Z",413,"/data/upload/5/0c0ff4f4-f3b7d61057e3aabc5c1f2fa2e1d91f80.jpg",0.872,"2025-12-20T11:07:40.448677Z"
241
+ 414,"1","FAMILY_SAFE","2025-12-20T11:07:41.981553Z",414,"/data/upload/5/de3532f8-f3c6565d9b0b5b4a9ea5791663a1cb98.jpg",1.284,"2025-12-20T11:07:41.981577Z"
242
+ 415,"1","FAMILY_SAFE","2025-12-20T11:08:22.962376Z",415,"/data/upload/5/53a286c2-f3a74abc0a833c5b5b69b06d3530b13c.jpg",40.71,"2025-12-20T11:08:22.962396Z"
243
+ 416,"1","SUGGESTIVE","2025-12-20T11:08:27.908642Z",416,"/data/upload/5/b80429f0-f3a782299db638313bc78a42fe799566.jpg",4.603,"2025-12-20T11:08:27.908664Z"
244
+ 417,"1","SUGGESTIVE","2025-12-20T11:08:29.180520Z",417,"/data/upload/5/c5926927-f392b17c19a743f54238920721a05483.jpg",1.015,"2025-12-20T11:08:29.180541Z"
245
+ 418,"1","SUGGESTIVE","2025-12-20T11:08:30.516467Z",418,"/data/upload/5/aaba3830-f392495a6934f1189fab05eeb50e6fd9.jpg",1.067,"2025-12-20T11:08:30.516488Z"
246
+ 419,"1","SUGGESTIVE","2025-12-20T11:08:31.933046Z",419,"/data/upload/5/52172c96-f379ca12e1aa320afc400fb76fb2c470.jpg",1.157,"2025-12-20T11:08:31.933059Z"
247
+ 420,"1","SUGGESTIVE","2025-12-20T11:08:33.358481Z",420,"/data/upload/5/6e49273c-f36c6d209021488c8bf51e89c19f58fb.jpg",1.179,"2025-12-20T11:08:33.358499Z"
248
+ 421,"1","UNCERTAIN","2025-12-20T11:08:35.193055Z",421,"/data/upload/5/920afa72-f3537cd026f876e2535d657df257076c.jpg",1.554,"2025-12-20T11:08:35.193082Z"
249
+ 422,"1","UNCERTAIN","2025-12-20T11:08:37.119985Z",422,"/data/upload/5/7e9fe457-f366645e8bc5d7b1122603ec99290bc2.jpg",1.656,"2025-12-20T11:08:37.120002Z"
250
+ 423,"1","SUGGESTIVE","2025-12-20T11:08:38.625745Z",423,"/data/upload/5/10f36423-f30e3a374309519194229b8153c51588.jpg",1.235,"2025-12-20T11:08:38.625771Z"
251
+ 424,"1","FAMILY_SAFE","2025-12-20T11:08:39.911415Z",424,"/data/upload/5/cd3dd942-f32a3c9a3b2b2f5fa7be6f8c73ae293b.jpg",1.021,"2025-12-20T11:08:39.911434Z"
252
+ 425,"1","UNCERTAIN","2025-12-20T11:08:42.146916Z",425,"/data/upload/5/aac071e6-f32bb35b2b035066b5f7d88a1a27c0d7.jpg",1.966,"2025-12-20T11:08:42.146938Z"
253
+ 426,"1","FAMILY_SAFE","2025-12-20T11:08:43.653249Z",426,"/data/upload/5/678d2056-f33cf9e01fb72fc6dce190f37c65c2dd.jpg",1.228,"2025-12-20T11:08:43.653271Z"
254
+ 427,"1","FAMILY_SAFE","2025-12-20T11:08:44.961635Z",427,"/data/upload/5/965c31ef-f2f78f897ad0a224175515bbde549e2e.jpg",1.038,"2025-12-20T11:08:44.961656Z"
255
+ 428,"1","UNCERTAIN","2025-12-20T11:08:47.791064Z",428,"/data/upload/5/579612c2-f2fa9ecf3da8d00302338efa4f87123c.jpg",2.554,"2025-12-20T11:08:47.791088Z"
256
+ 429,"1","SUGGESTIVE","2025-12-20T11:08:50.707093Z",429,"/data/upload/5/9f66f820-f2fae6e2500b65f269b5e6de80996f17.jpg",2.651,"2025-12-20T11:08:50.707113Z"
257
+ 430,"1","SUGGESTIVE","2025-12-20T11:08:53.843375Z",430,"/data/upload/5/75006524-f30c43c00748e7912ce105d616264ca8.jpg",2.868,"2025-12-20T11:08:53.843395Z"
258
+ 431,"1","FAMILY_SAFE","2025-12-20T11:08:55.124117Z",431,"/data/upload/5/7c688d00-f3066b02932fbc939c22e9c3bb8e942a.jpg",1.024,"2025-12-20T11:08:55.124137Z"
259
+ 432,"1","SUGGESTIVE","2025-12-20T11:08:56.726229Z",432,"/data/upload/5/3174b410-f2f05fc8d36aa9b2d14d9a0eed30416a.jpg",1.327,"2025-12-20T11:08:56.726251Z"
260
+ 433,"1","UNCERTAIN","2025-12-20T11:08:58.921837Z",433,"/data/upload/5/c43cb7fb-f2f33b9d050b1eec02c29464f036d292.jpg",1.977,"2025-12-20T11:08:58.921878Z"
261
+ 434,"1","SUGGESTIVE","2025-12-20T11:09:00.226999Z",434,"/data/upload/5/55e1d1ac-f2b9d5dc127dddd36b113f2719d89a97.jpg",1.048,"2025-12-20T11:09:00.227020Z"
262
+ 435,"1","FAMILY_SAFE","2025-12-20T11:09:01.706294Z",435,"/data/upload/5/26f72a43-f2b9d6126ecc23b075d1c1acc8fe340d.jpg",1.226,"2025-12-20T11:09:01.706316Z"
263
+ 436,"1","FAMILY_SAFE","2025-12-20T11:09:03.408842Z",436,"/data/upload/5/a6eee7e4-f2d5d25ff62ef48331c529bf26e170d4.jpg",1.444,"2025-12-20T11:09:03.408865Z"
264
+ 437,"1","SUGGESTIVE","2025-12-20T11:09:05.442262Z",437,"/data/upload/5/6583b10b-f2aae7afc3a013755b2365cb2942dc64.jpg",1.779,"2025-12-20T11:09:05.442285Z"
265
+ 438,"1","SUGGESTIVE","2025-12-20T11:09:07.089870Z",438,"/data/upload/5/3dc4ad74-f27b0f685d9b622d7ff36bdd97f72831.jpg",1.382,"2025-12-20T11:09:07.089889Z"
266
+ 439,"1","UNCERTAIN","2025-12-20T11:09:09.933482Z",439,"/data/upload/5/6a8274e7-f2801326462d460409fa733227426003.jpg",2.586,"2025-12-20T11:09:09.933505Z"
267
+ 440,"1","SUGGESTIVE","2025-12-20T11:09:11.186992Z",440,"/data/upload/5/7e1849f3-f26a9865a58639d9b17de158040c8d99.jpg",0.996,"2025-12-20T11:09:11.187012Z"
268
+ 441,"1","SUGGESTIVE","2025-12-20T11:09:12.728129Z",441,"/data/upload/5/e4ae160c-f26b087c218148a8ca3f2e96e12d7a1b.jpg",1.284,"2025-12-20T11:09:12.728152Z"
269
+ 442,"1","SUGGESTIVE","2025-12-20T11:09:15.215308Z",442,"/data/upload/5/4f4b4d5f-f26d88ffcd70edc8984c024f2de1ca39.jpg",2.229,"2025-12-20T11:09:15.215327Z"
270
+ 443,"1","SUGGESTIVE","2025-12-20T11:09:16.452020Z",443,"/data/upload/5/55668d0d-f2691c288c683bcf94651a0bbffe21ad.jpg",0.979,"2025-12-20T11:09:16.452039Z"
271
+ 444,"1","FAMILY_SAFE","2025-12-20T11:09:18.025849Z",444,"/data/upload/5/302fc960-f24d9b56d05bdf13a83cfabd87f8e5af.jpg",1.366,"2025-12-20T11:09:18.025867Z"
272
+ 445,"1","SUGGESTIVE","2025-12-20T11:09:20.323696Z",445,"/data/upload/5/da992908-f24efb4f9760b0e7fbb3fcf77dba0891.jpg",2.055,"2025-12-20T11:09:20.323720Z"
273
+ 446,"1","SUGGESTIVE","2025-12-20T11:09:21.705670Z",446,"/data/upload/5/09535428-f244b1f6799b0610306318fa09e744c4.jpg",1.122,"2025-12-20T11:09:21.705698Z"
274
+ 447,"1","FAMILY_SAFE","2025-12-20T11:09:23.206455Z",447,"/data/upload/5/6b3255e8-f20ecb2425f29d21d4047bec13a2babb.jpg",1.26,"2025-12-20T11:09:23.206467Z"
275
+ 448,"1","SUGGESTIVE","2025-12-20T11:09:24.479583Z",448,"/data/upload/5/3d3f89e4-f20eef0275884df087aefc827000488a.jpg",1.008,"2025-12-20T11:09:24.479605Z"
276
+ 449,"1","FAMILY_SAFE","2025-12-20T11:09:26.174253Z",449,"/data/upload/5/476d2b62-f21375899b6c734ba68a8f2a74990efb.jpg",1.438,"2025-12-20T11:09:26.174278Z"
277
+ 450,"1","FAMILY_SAFE","2025-12-20T11:09:27.939835Z",450,"/data/upload/5/2821adf9-f20de5b6356da5f1e38626064c8ac85e.jpg",1.504,"2025-12-20T11:09:27.939857Z"
278
+ 451,"1","SUGGESTIVE","2025-12-20T11:09:29.381016Z",451,"/data/upload/5/78dd5846-f1b48acd52be4f8693ed99170c2a044c.jpg",1.189,"2025-12-20T11:09:29.381040Z"
279
+ 452,"1","SUGGESTIVE","2025-12-20T11:09:31.326367Z",452,"/data/upload/5/7d5de576-f1c966431895a337d35d9ec3a7470b17.jpg",1.575,"2025-12-20T11:09:31.326386Z"
280
+ 453,"1","SUGGESTIVE","2025-12-20T11:09:32.434032Z",453,"/data/upload/5/8cf1683d-f1d11292af5391d00957494b8b58f315.jpg",0.84,"2025-12-20T11:09:32.434054Z"
281
+ 454,"1","SUGGESTIVE","2025-12-20T11:09:33.754811Z",454,"/data/upload/5/59b576bf-f1a168d27daac1da3571fcbcce7c1ebd.jpg",1.063,"2025-12-20T11:09:33.754833Z"
282
+ 455,"1","UNCERTAIN","2025-12-20T11:09:36.458670Z",455,"/data/upload/5/8f0ff5de-f18c7dac0d1162379e292b0e4b9e22ce.jpg",2.447,"2025-12-20T11:09:36.458691Z"
283
+ 456,"1","SUGGESTIVE","2025-12-20T11:09:37.745287Z",456,"/data/upload/5/3d83aaea-f186d77202e18462443a8c7ffc449919.jpg",1.026,"2025-12-20T11:09:37.745307Z"
284
+ 457,"1","SUGGESTIVE","2025-12-20T11:09:38.769270Z",457,"/data/upload/5/d4d29b7b-f162dbe9bf0b44a6f2ddb910d725d56b.jpg",0.76,"2025-12-20T11:09:38.769291Z"
285
+ 458,"1","SUGGESTIVE","2025-12-20T11:09:39.839893Z",458,"/data/upload/5/c6f24a4d-f1646b36bb33f474b8f66c112bf97cc3.jpg",0.815,"2025-12-20T11:09:39.839903Z"
286
+ 459,"1","FAMILY_SAFE","2025-12-20T11:09:41.484421Z",459,"/data/upload/5/9d710615-f1712c2ed94b10ffedda4e1ea6e56fa9.jpg",1.379,"2025-12-20T11:09:41.484444Z"
287
+ 460,"1","SUGGESTIVE","2025-12-20T11:09:44.628375Z",460,"/data/upload/5/c325de0e-f16866c512ca721aa3ba3b2129e4b66f.jpg",2.881,"2025-12-20T11:09:44.628395Z"
288
+ 461,"1","SUGGESTIVE","2025-12-20T11:09:45.688039Z",461,"/data/upload/5/c9983270-f1643668073aa69e7f2b505d1b237098.jpg",0.795,"2025-12-20T11:09:45.688063Z"
289
+ 462,"1","SUGGESTIVE","2025-12-20T11:09:46.769325Z",462,"/data/upload/5/ff926dbf-f12e5c2510e88db3c8fdf32aa7968f9d.jpg",0.817,"2025-12-20T11:09:46.769348Z"
290
+ 463,"1","SUGGESTIVE","2025-12-20T11:09:47.754151Z",463,"/data/upload/5/edbf7bc4-f13bac93da13444f529aa1c821731161.jpg",0.72,"2025-12-20T11:09:47.754171Z"
291
+ 464,"1","SUGGESTIVE","2025-12-20T11:09:48.805953Z",464,"/data/upload/5/d3721c85-f126f5fa7fd6939084fa439a5c194f18.jpg",0.787,"2025-12-20T11:09:48.805975Z"
292
+ 465,"1","SUGGESTIVE","2025-12-20T11:09:49.715473Z",465,"/data/upload/5/b5e5e620-f126f93e4853f89a6e25b661040dc314.jpg",0.649,"2025-12-20T11:09:49.715495Z"
293
+ 466,"1","SUGGESTIVE","2025-12-20T11:09:50.754293Z",466,"/data/upload/5/10135976-f1243b33b2f6d754c9bc9da9e8de97e4.jpg",0.773,"2025-12-20T11:09:50.754316Z"
294
+ 467,"1","SUGGESTIVE","2025-12-20T11:09:51.874405Z",467,"/data/upload/5/b81fe122-f1404d9866f6128bd44a1eaee0a39cf7.jpg",0.856,"2025-12-20T11:09:51.874429Z"
295
+ 468,"1","SUGGESTIVE","2025-12-20T11:09:53.537766Z",468,"/data/upload/5/f3d934fc-f14861197568fd42d5a3a8f39a8970b9.jpg",1.44,"2025-12-20T11:09:53.537786Z"
296
+ 469,"1","SUGGESTIVE","2025-12-20T11:09:54.782268Z",469,"/data/upload/5/34496229-f104ed4e8f3c30e2fa8ddfbd3332b413.jpg",0.999,"2025-12-20T11:09:54.782295Z"
297
+ 470,"1","SUGGESTIVE","2025-12-20T11:09:55.875573Z",470,"/data/upload/5/849c7056-f0b773be242aee171c66e49b6c3dfb63.jpg",0.823,"2025-12-20T11:09:55.875599Z"
298
+ 471,"1","SUGGESTIVE","2025-12-20T11:09:56.955151Z",471,"/data/upload/5/c6d20edb-f0c80a1febd6aeb4ed6ccaf3fd82760a.jpg",0.811,"2025-12-20T11:09:56.955173Z"
299
+ 472,"1","FAMILY_SAFE","2025-12-20T11:12:46.136601Z",472,"/data/upload/5/906d1fce-f0ca8c9fdee2f995b567b0626cdadcac.jpg",168.866,"2025-12-20T11:12:46.136613Z"
300
+ 473,"1","SUGGESTIVE","2025-12-20T11:12:49.184658Z",473,"/data/upload/5/f638acd0-f0d3205ee8ebe32b2d53f1ad223e9818.jpg",2.728,"2025-12-20T11:12:49.184678Z"
301
+ 474,"1","SUGGESTIVE","2025-12-20T11:12:50.440778Z",474,"/data/upload/5/da3f5f03-f0dd8d4ac7e466a7a233905f8db38a1c.jpg",0.972,"2025-12-20T11:12:50.440799Z"
302
+ 475,"1","SUGGESTIVE","2025-12-20T11:12:52.212396Z",475,"/data/upload/5/c2917e19-f06b1f090a5e2f8b788720ce4489183a.jpg",1.49,"2025-12-20T11:12:52.212416Z"
303
+ 476,"1","FAMILY_SAFE","2025-12-20T11:12:53.702227Z",476,"/data/upload/5/698e9ebb-f06d35d37cacac0d69db96016e0c182f.jpg",1.211,"2025-12-20T11:12:53.702249Z"
304
+ 477,"1","SUGGESTIVE","2025-12-20T11:12:54.968969Z",477,"/data/upload/5/4696edaa-f06eb7893da319aec3f0de768a1a845d.jpg",0.98,"2025-12-20T11:12:54.968990Z"
305
+ 478,"1","UNCERTAIN","2025-12-20T11:12:56.583670Z",478,"/data/upload/5/ad86327f-f08abc73a82adca2bbb2304c62991458.jpg",1.349,"2025-12-20T11:12:56.583692Z"
306
+ 479,"1","SUGGESTIVE","2025-12-20T11:13:00.655782Z",479,"/data/upload/5/7713ccd7-f08c1d5efaa9ae3e99274ef65dbc7cc4.jpg",3.804,"2025-12-20T11:13:00.655803Z"
307
+ 480,"1","SUGGESTIVE","2025-12-20T11:13:02.802689Z",480,"/data/upload/5/88e6096c-f0809c968c34e21b25ef28da489b68fd.jpg",1.878,"2025-12-20T11:13:02.802709Z"
308
+ 481,"1","FAMILY_SAFE","2025-12-20T11:13:04.135859Z",481,"/data/upload/5/524bc0ae-f06772600ff35982e30ec813edb89dc2.jpg",1.067,"2025-12-20T11:13:04.135880Z"
309
+ 482,"1","FAMILY_SAFE","2025-12-20T11:13:06.542634Z",482,"/data/upload/5/3d31944e-f049c93ef72e3bd5edbf919222d2d1ae.jpg",2.137,"2025-12-20T11:13:06.542654Z"
310
+ 483,"1","SUGGESTIVE","2025-12-20T11:13:07.889861Z",483,"/data/upload/5/47efba26-f0445c4671cbf87f6be472f5102475c2.jpg",1.076,"2025-12-20T11:13:07.889884Z"
311
+ 484,"1","SUGGESTIVE","2025-12-20T11:13:09.407175Z",484,"/data/upload/5/fa8fe379-f0357ced6ed7226ecfd6bc274c3e8a5b.jpg",1.25,"2025-12-20T11:13:09.407198Z"
312
+ 485,"1","SUGGESTIVE","2025-12-20T11:13:10.806777Z",485,"/data/upload/5/475a96b3-eff990daac3ddb54436bad2cc950abb3.jpg",1.13,"2025-12-20T11:13:10.806798Z"
313
+ 486,"1","SUGGESTIVE","2025-12-20T11:13:12.241372Z",486,"/data/upload/5/fa249f4f-efb7440d597d698c5442b03cc11096ab.jpg",1.212,"2025-12-20T11:13:12.241388Z"
314
+ 487,"1","SUGGESTIVE","2025-12-20T11:13:13.939921Z",487,"/data/upload/5/b10f30c8-efc08bbcc30ce2804f50c73ff07cffec.jpg",1.483,"2025-12-20T11:13:13.939940Z"
315
+ 488,"1","SUGGESTIVE","2025-12-20T11:13:15.124442Z",488,"/data/upload/5/faba1751-efc073e437c18987a2f1476da3de0b3a.jpg",0.916,"2025-12-20T11:13:15.124463Z"
316
+ 489,"1","SUGGESTIVE","2025-12-20T11:13:16.664950Z",489,"/data/upload/5/0098546d-efd85646ed2bdbc974b9c3c579691730.jpg",1.27,"2025-12-20T11:13:16.664969Z"
317
+ 490,"1","SUGGESTIVE","2025-12-20T11:13:17.941764Z",490,"/data/upload/5/b4635fff-efef5f2439ee81918faf609d5a0aa163.jpg",1.003,"2025-12-20T11:13:17.941787Z"
318
+ 491,"1","SUGGESTIVE","2025-12-20T11:13:19.103118Z",491,"/data/upload/5/4c7faf4b-efef22ec0ad48c3b2f51c82dcb9565e6.jpg",0.891,"2025-12-20T11:13:19.103139Z"
319
+ 492,"1","SUGGESTIVE","2025-12-20T11:13:20.409808Z",492,"/data/upload/5/e9be5783-efa81f12be12471dd91827cfb74404a9.jpg",1.036,"2025-12-20T11:13:20.409828Z"
320
+ 493,"1","SUGGESTIVE","2025-12-20T11:13:21.543572Z",493,"/data/upload/5/0df8255e-efaa8dd97b36b54c67356ca6c1500791.jpg",0.866,"2025-12-20T11:13:21.543588Z"
321
+ 494,"1","SUGGESTIVE","2025-12-20T11:13:22.602764Z",494,"/data/upload/5/e96341ac-ef6a78b93788b6cab069d01b92516ec6.jpg",0.793,"2025-12-20T11:13:22.602784Z"
322
+ 495,"1","SUGGESTIVE","2025-12-20T11:13:23.921753Z",495,"/data/upload/5/2a7d0d01-ef6b436f284fd4c6a95adef2c0dc0555.jpg",1.062,"2025-12-20T11:13:23.921763Z"
323
+ 496,"1","SUGGESTIVE","2025-12-20T11:13:25.119683Z",496,"/data/upload/5/e764a81b-ef7d423ff22abad26fcdc945e31dca5b.jpg",0.94,"2025-12-20T11:13:25.119704Z"
324
+ 497,"1","SUGGESTIVE","2025-12-20T11:13:26.356293Z",497,"/data/upload/5/6ffc5735-ef70be698e9f8dbd29dacb15ac4d0ec9.jpg",0.97,"2025-12-20T11:13:26.356315Z"
325
+ 498,"1","SUGGESTIVE","2025-12-20T11:13:27.651103Z",498,"/data/upload/5/98ce088a-ef76eea72ea523ba10369ff89f844b8d.jpg",1.024,"2025-12-20T11:13:27.651127Z"
326
+ 499,"1","SUGGESTIVE","2025-12-20T11:13:28.931608Z",499,"/data/upload/5/772bc39c-ef4a21bca7ceab6a75e2a5fd738408d9.jpg",1.044,"2025-12-20T11:13:28.931630Z"
327
+ 500,"1","SUGGESTIVE","2025-12-20T11:13:30.180348Z",500,"/data/upload/5/7207626d-ef378dd1f4004be18abc11bef3c80de4.jpg",0.96,"2025-12-20T11:13:30.180370Z"
328
+ 501,"1","SUGGESTIVE","2025-12-20T11:13:31.336122Z",501,"/data/upload/5/281a7d11-ef4790408c0eb4ad41d481796a8d76fd.jpg",0.882,"2025-12-20T11:13:31.336143Z"
329
+ 502,"1","FAMILY_SAFE","2025-12-20T11:13:32.792343Z",502,"/data/upload/5/15072791-ef0a3b5c272d10607604167b4bc37034.jpg",1.183,"2025-12-20T11:13:32.792367Z"
330
+ 503,"1","UNCERTAIN","2025-12-20T11:13:36.021549Z",503,"/data/upload/5/6dc70b1e-ef1026a33c894d18a1a1f32922017fbf.jpg",2.957,"2025-12-20T11:13:36.021570Z"
331
+ 504,"1","SUGGESTIVE","2025-12-20T11:13:37.328820Z",504,"/data/upload/5/c8740c8b-eedc4a75b4521825dd368a81324741e4.jpg",1.046,"2025-12-20T11:13:37.328833Z"
332
+ 505,"1","UNCERTAIN","2025-12-20T11:13:39.974605Z",505,"/data/upload/5/dcfb8a01-eebd67e28f811ad93ce45d7a22c9a280.jpg",2.385,"2025-12-20T11:13:39.974623Z"
333
+ 506,"1","SUGGESTIVE","2025-12-20T11:13:41.259678Z",506,"/data/upload/5/9283a4a4-eed2a73a4dc7506b1ce5042c0d86a41f.jpg",1.023,"2025-12-20T11:13:41.259699Z"
334
+ 507,"1","SUGGESTIVE","2025-12-20T11:13:43.239857Z",507,"/data/upload/5/d9b69ac8-eed7b464c9a9d59069f62bd631953120.jpg",1.703,"2025-12-20T11:13:43.239877Z"
335
+ 508,"1","UNCERTAIN","2025-12-20T11:13:47.266206Z",508,"/data/upload/5/81f42f14-ee9ba96fe055cbfa17dbe24799365177.jpg",3.765,"2025-12-20T11:13:47.266218Z"
336
+ 509,"1","SUGGESTIVE","2025-12-20T11:13:55.896786Z",509,"/data/upload/5/5878b83e-ee9dc9d669cf306bf6bf04dfaf3b1c9d.jpg",8.423,"2025-12-20T11:13:55.896802Z"
337
+ 510,"1","SUGGESTIVE","2025-12-20T11:13:57.135918Z",510,"/data/upload/5/e31a796f-eea76211c790d725a7dd027a99213afb.jpg",0.949,"2025-12-20T11:13:57.135942Z"
338
+ 511,"1","SUGGESTIVE","2025-12-20T11:13:58.373320Z",511,"/data/upload/5/a4c89124-eeaa866b8633bedc8034bce163670339.jpg",0.955,"2025-12-20T11:13:58.373342Z"
339
+ 512,"1","SUGGESTIVE","2025-12-20T11:13:59.537399Z",512,"/data/upload/5/35909ea6-eeab35797f69d18b0187e08c5549efb6.jpg",0.893,"2025-12-20T11:13:59.537422Z"
340
+ 513,"1","SUGGESTIVE","2025-12-20T11:14:00.628161Z",513,"/data/upload/5/da887e4a-eeb5d0dbe82e36107e2af75b20ab17c7.jpg",0.823,"2025-12-20T11:14:00.628182Z"
341
+ 514,"1","SUGGESTIVE","2025-12-20T11:14:01.905252Z",514,"/data/upload/5/75ab9401-eebbd19e449133670a58baff0fa109ec.jpg",0.991,"2025-12-20T11:14:01.905271Z"
342
+ 515,"1","SUGGESTIVE","2025-12-20T11:14:02.983344Z",515,"/data/upload/5/e75f4995-eebcec31909a43812f2334fb37807008.jpg",0.807,"2025-12-20T11:14:02.983367Z"
343
+ 516,"1","SUGGESTIVE","2025-12-20T11:14:04.036058Z",516,"/data/upload/5/de0c936f-ee8a39d9411e2cd46e726afe1c5a4895.jpg",0.777,"2025-12-20T11:14:04.036081Z"
344
+ 517,"1","FAMILY_SAFE","2025-12-20T11:14:05.905275Z",517,"/data/upload/5/e2e721c6-ee8c7ace06eac5eecd8b709b93976d0b.jpg",1.588,"2025-12-20T11:14:05.905294Z"
345
+ 518,"1","SUGGESTIVE","2025-12-20T11:14:07.773033Z",518,"/data/upload/5/6828a485-ee90a0e72628f9dc27de80e5ce701bc2.jpg",1.592,"2025-12-20T11:14:07.773054Z"
346
+ 519,"1","FAMILY_SAFE","2025-12-20T11:14:09.171253Z",519,"/data/upload/5/ef0ac7d6-ee890fafca12595501b30093d54320af.jpg",1.119,"2025-12-20T11:14:09.171273Z"
347
+ 520,"1","UNCERTAIN","2025-12-20T11:14:13.686655Z",520,"/data/upload/5/eb7e0cef-ee93704434f4aa96859be27f8b04f4ca.jpg",4.233,"2025-12-20T11:14:13.686678Z"
348
+ 521,"1","FAMILY_SAFE","2025-12-20T11:14:15.141517Z",521,"/data/upload/5/6127f82d-ee4fad4fd52e6f1dc5121d1965a31e61.jpg",1.18,"2025-12-20T11:14:15.141540Z"
349
+ 522,"1","FAMILY_SAFE","2025-12-20T11:14:16.832347Z",522,"/data/upload/5/cc55d5b2-ee745023dec8ba83a3b401aaa5a34f2d.jpg",1.415,"2025-12-20T11:14:16.832370Z"
350
+ 523,"1","SUGGESTIVE","2025-12-20T11:14:18.268038Z",523,"/data/upload/5/ae6d504b-ee44eff2d80d58e6f2c6122fcc468c63.jpg",1.162,"2025-12-20T11:14:18.268059Z"
351
+ 524,"1","UNCERTAIN","2025-12-20T11:14:19.950922Z",524,"/data/upload/5/5fee8ad9-ee435d2ab53c469e63808c8d60c6351e.jpg",1.403,"2025-12-20T11:14:19.950946Z"
352
+ 525,"1","SUGGESTIVE","2025-12-20T11:14:21.710157Z",525,"/data/upload/5/a0a2d935-ee166c5de961d809600d8a47a8284e7e.jpg",1.485,"2025-12-20T11:14:21.710180Z"
353
+ 526,"1","FAMILY_SAFE","2025-12-20T11:14:24.977643Z",526,"/data/upload/5/8a5bf427-ee3086dce1a61b091b09bb8bf460d671.jpg",2.987,"2025-12-20T11:14:24.977665Z"
354
+ 527,"1","FAMILY_SAFE","2025-12-20T11:14:26.473080Z",527,"/data/upload/5/305abb91-ede68be22f91fbddc1f4e072b05c0ae5.jpg",1.216,"2025-12-20T11:14:26.473099Z"
355
+ 528,"1","SUGGESTIVE","2025-12-20T11:14:34.476082Z",528,"/data/upload/5/429234c0-edf1ded0501e179112b516a99a58f6b3.jpg",7.624,"2025-12-20T11:14:34.476105Z"
356
+ 529,"1","FAMILY_SAFE","2025-12-20T11:14:35.731319Z",529,"/data/upload/5/90e7a11a-edf5de76bd4db7ec201caf452ffd1519.jpg",0.964,"2025-12-20T11:14:35.731341Z"
357
+ 530,"1","SUGGESTIVE","2025-12-20T11:14:40.517155Z",530,"/data/upload/5/357576cd-edbca137d1801a86d305e4ad3f3a6305.jpg",4.498,"2025-12-20T11:14:40.517177Z"
358
+ 531,"1","SUGGESTIVE","2025-12-20T11:14:41.874482Z",531,"/data/upload/5/274e3d8d-edcdf84578e3103a185073e581089641.jpg",1.121,"2025-12-20T11:14:41.874501Z"
359
+ 532,"1","SUGGESTIVE","2025-12-20T11:14:43.078036Z",532,"/data/upload/5/09696d76-edd88c179c4b8d37223a74e58757921b.jpg",0.936,"2025-12-20T11:14:43.078049Z"
360
+ 533,"1","SUGGESTIVE","2025-12-20T11:14:44.531706Z",533,"/data/upload/5/2befe926-eddca7fbfe13ebce00a268c70550b580.jpg",1.176,"2025-12-20T11:14:44.531729Z"
361
+ 534,"1","UNCERTAIN","2025-12-20T11:14:46.797039Z",534,"/data/upload/5/dd0a59aa-edddf271573716e8e4e571451c634b85.jpg",1.951,"2025-12-20T11:14:46.797055Z"
362
+ 535,"1","FAMILY_SAFE","2025-12-20T11:14:48.109629Z",535,"/data/upload/5/52784190-ed8db5c9ad94f3f769b504dba5d12f3c.jpg",1.026,"2025-12-20T11:14:48.109649Z"
363
+ 536,"1","UNCERTAIN","2025-12-20T11:14:52.728165Z",536,"/data/upload/5/7c8e5c47-ed9f73ea5150c7871fa9ab71e2808929.jpg",4.327,"2025-12-20T11:14:52.728187Z"
364
+ 537,"1","SUGGESTIVE","2025-12-20T11:14:57.525160Z",537,"/data/upload/5/23660c64-ed78b3d3e8cf8fa817e55818ef219f2f.jpg",4.515,"2025-12-20T11:14:57.525177Z"
365
+ 538,"1","SUGGESTIVE","2025-12-20T11:14:58.675477Z",538,"/data/upload/5/36ccdc32-ed9326789faddcf165c85c0c46e61892.jpg",0.859,"2025-12-20T11:14:58.675498Z"
366
+ 539,"1","SUGGESTIVE","2025-12-20T11:14:59.853456Z",539,"/data/upload/5/eae0b0ad-eda271d3e424bcbddf17ba2795ca31ab.jpg",0.942,"2025-12-20T11:14:59.853477Z"
367
+ 540,"1","FAMILY_SAFE","2025-12-20T11:15:02.140692Z",540,"/data/upload/5/7817ce56-edb7db1fcb69296415e2f32cd5cac1ba.jpg",1.997,"2025-12-20T11:15:02.140713Z"
368
+ 541,"1","FAMILY_SAFE","2025-12-20T11:15:03.743247Z",541,"/data/upload/5/7151cdf8-edb3802789ea655bda76512662ef16fd.jpg",1.321,"2025-12-20T11:15:03.743270Z"
369
+ 542,"1","SUGGESTIVE","2025-12-20T11:15:05.815531Z",542,"/data/upload/5/420c8652-ed4fa0d4320a9474fcdbf9f260d5ccd9.jpg",1.829,"2025-12-20T11:15:05.815552Z"
370
+ 543,"1","SUGGESTIVE","2025-12-20T11:15:07.248456Z",543,"/data/upload/5/f12d507b-ed6ad32bca5a4012c154cea7d74675c1.jpg",1.15,"2025-12-20T11:15:07.248477Z"
371
+ 544,"1","SUGGESTIVE","2025-12-20T11:15:09.882659Z",544,"/data/upload/5/ea78c268-ed59c63ff419564a0091bf4dba0db57b.jpg",2.361,"2025-12-20T11:15:09.882680Z"
372
+ 545,"1","SUGGESTIVE","2025-12-20T11:15:11.329418Z",545,"/data/upload/5/8bc1df46-ed684d1f722a3e6fd6644a086a941908.jpg",1.166,"2025-12-20T11:15:11.329443Z"
373
+ 546,"1","FAMILY_SAFE","2025-12-20T11:15:13.800073Z",546,"/data/upload/5/6ac74b20-ed0dd06c432216aa905a2732eb14d356.jpg",2.181,"2025-12-20T11:15:13.800097Z"
374
+ 547,"1","UNCERTAIN","2025-12-20T11:15:16.999522Z",547,"/data/upload/5/f88fee24-ed21cb4f07ee87064a3203c18fd4cce1.jpg",2.921,"2025-12-20T11:15:16.999544Z"
375
+ 548,"1","FAMILY_SAFE","2025-12-20T11:15:25.935429Z",548,"/data/upload/5/2f604374-ed22e47a89b289fcddbd58d84fe4af7a.jpg",8.658,"2025-12-20T11:15:25.935441Z"
376
+ 549,"1","FAMILY_SAFE","2025-12-20T11:15:35.261454Z",549,"/data/upload/5/3afea32d-ece69a65ed45a3edf1fc00efbbfaedb7.jpg",9.003,"2025-12-20T11:15:35.261477Z"
377
+ 550,"1","SUGGESTIVE","2025-12-20T11:15:36.996397Z",550,"/data/upload/5/e4e420e1-ece99c6f98d2a711d94c11bb54cce29f.jpg",1.411,"2025-12-20T11:15:36.996419Z"
378
+ 551,"1","FAMILY_SAFE","2025-12-20T11:15:38.332445Z",551,"/data/upload/5/c356522a-ed0ab3e1587ed20eeace37e94f8d54ba.jpg",1.041,"2025-12-20T11:15:38.332458Z"
379
+ 552,"1","FAMILY_SAFE","2025-12-20T11:15:39.769695Z",552,"/data/upload/5/a9f4ce1f-ed0b80d5b8a1a6cd023555b1695c5037.jpg",1.173,"2025-12-20T11:15:39.769708Z"
380
+ 553,"1","FAMILY_SAFE","2025-12-20T11:15:40.870114Z",553,"/data/upload/5/5822bed3-ed0c697143d8cfcf7772d205537c1a53.jpg",0.827,"2025-12-20T11:15:40.870126Z"
381
+ 554,"1","UNCERTAIN","2025-12-20T11:15:42.636356Z",554,"/data/upload/5/8d880f44-ed01e05d1b0f341627595626f0d1fc19.jpg",1.48,"2025-12-20T11:15:42.636377Z"
382
+ 555,"1","UNCERTAIN","2025-12-20T11:15:45.710540Z",555,"/data/upload/5/d513daab-ed0590024f5fe63e2dca696949cd4a3b.jpg",2.776,"2025-12-20T11:15:45.710565Z"
383
+ 556,"1","SUGGESTIVE","2025-12-20T11:15:47.346622Z",556,"/data/upload/5/c80032d6-ecd074a8a73aafc255fb87c50ab46d32.jpg",1.348,"2025-12-20T11:15:47.346644Z"
384
+ 557,"1","SUGGESTIVE","2025-12-20T11:15:48.529900Z",557,"/data/upload/5/5cb76bcd-ecd461884c7d815ca300840ac7dff722.jpg",0.895,"2025-12-20T11:15:48.529922Z"
385
+ 558,"1","SUGGESTIVE","2025-12-20T11:15:52.668563Z",558,"/data/upload/5/475e3763-ecab477c7f479d98386c20ed15ddc8ed.jpg",3.851,"2025-12-20T11:15:52.668584Z"
386
+ 559,"1","SUGGESTIVE","2025-12-20T11:15:54.567362Z",559,"/data/upload/5/38faa256-ecb96a10b5007cac877671cd626e11bc.jpg",1.602,"2025-12-20T11:15:54.567383Z"
387
+ 560,"1","FAMILY_SAFE","2025-12-20T11:15:56.337890Z",560,"/data/upload/5/0a0d8662-ecb215bf839692ed00a48cf006405234.jpg",1.481,"2025-12-20T11:15:56.337909Z"
388
+ 561,"1","SUGGESTIVE","2025-12-20T11:15:57.966410Z",561,"/data/upload/5/fe38b006-ec912c8d68846d3d4cae46a6dc2e2e67.jpg",1.341,"2025-12-20T11:15:57.966432Z"
389
+ 562,"1","SUGGESTIVE","2025-12-20T11:15:59.137586Z",562,"/data/upload/5/61198ea6-eca396464fb1343904128cac952dccba.jpg",0.887,"2025-12-20T11:15:59.137606Z"
390
+ 563,"1","SUGGESTIVE","2025-12-20T11:16:00.622365Z",563,"/data/upload/5/f42a237d-ec8ba9e30ab570db99c521c4d75e877a.jpg",1.19,"2025-12-20T11:16:00.622387Z"
391
+ 564,"1","FAMILY_SAFE","2025-12-20T11:16:02.887401Z",564,"/data/upload/5/9ea2a68b-ec75da7bce977ab4b2d77224ad0ed08d.jpg",2.012,"2025-12-20T11:16:02.887421Z"
392
+ 565,"1","SUGGESTIVE","2025-12-20T11:16:04.458689Z",565,"/data/upload/5/b19e9aff-ec7752c4445664d6a594779d0cba33a6.jpg",1.266,"2025-12-20T11:16:04.458709Z"
393
+ 566,"1","FAMILY_SAFE","2025-12-20T11:16:05.825255Z",566,"/data/upload/5/cb8a1b42-ec4ed6c5f59cd751b1d4a8cbd5ef9730.jpg",1.121,"2025-12-20T11:16:05.825278Z"
394
+ 567,"1","SUGGESTIVE","2025-12-20T11:16:07.289021Z",567,"/data/upload/5/11f16074-ec4f3a4fceac2b83221995baa3ce6138.jpg",1.171,"2025-12-20T11:16:07.289043Z"
395
+ 568,"1","FAMILY_SAFE","2025-12-20T11:16:09.451497Z",568,"/data/upload/5/cf990f78-ec4898ce293c0fce9e3df490ef83741d.jpg",1.874,"2025-12-20T11:16:09.451517Z"
396
+ 569,"1","FAMILY_SAFE","2025-12-20T11:16:12.080564Z",569,"/data/upload/5/958ec76b-ec5156a1d4dad967c4ed14a44fc9c10f.jpg",2.338,"2025-12-20T11:16:12.080586Z"
397
+ 570,"1","FAMILY_SAFE","2025-12-20T11:16:15.484336Z",570,"/data/upload/5/b1c40d0c-ec5965d57ca71e648adc70401156b743.jpg",3.151,"2025-12-20T11:16:15.484358Z"
398
+ 571,"1","SUGGESTIVE","2025-12-20T11:16:18.658159Z",571,"/data/upload/5/5c8c7c7e-ec3c4b7b7261c385c7792663a153b2dc.jpg",2.875,"2025-12-20T11:16:18.658181Z"
399
+ 572,"1","SUGGESTIVE","2025-12-20T11:16:19.912910Z",572,"/data/upload/5/c8925d60-ec2488d58f5c6f80593119f44bdb6f36.jpg",0.962,"2025-12-20T11:16:19.912934Z"
400
+ 573,"1","SUGGESTIVE","2025-12-20T11:16:21.529093Z",573,"/data/upload/5/783f692a-ec3873aa10b64f2c5c0ae4044d8dd916.jpg",1.316,"2025-12-20T11:16:21.529115Z"
401
+ 574,"1","SUGGESTIVE","2025-12-20T11:16:23.018673Z",574,"/data/upload/5/c59d6aef-ec366037d7416be3f574f23f7e0fb3ef.jpg",1.234,"2025-12-20T11:16:23.018695Z"
402
+ 575,"1","UNCERTAIN","2025-12-20T11:16:26.042777Z",575,"/data/upload/5/ba90116c-ebfda352bd5113859294aff74239faed.jpg",2.726,"2025-12-20T11:16:26.042812Z"
403
+ 576,"1","FAMILY_SAFE","2025-12-20T11:16:28.693166Z",576,"/data/upload/5/74013397-ebfe9c315f7eba7ee13ffecfbe52cab8.jpg",2.355,"2025-12-20T11:16:28.693188Z"
404
+ 577,"1","SUGGESTIVE","2025-12-20T11:16:31.044435Z",577,"/data/upload/5/2827b91c-ec1a1c97dab7c740188280a871b78251.jpg",2.062,"2025-12-20T11:16:31.044457Z"
405
+ 578,"1","SUGGESTIVE","2025-12-20T11:16:32.150600Z",578,"/data/upload/5/07efebe4-ebd3a3a173aa3985f69c95a9bbeec91a.jpg",0.811,"2025-12-20T11:16:32.150620Z"
406
+ 579,"1","SUGGESTIVE","2025-12-20T11:16:34.495508Z",579,"/data/upload/5/34018f60-ebe7ee3f3605c66f1be4eb323e3ae05f.jpg",2.053,"2025-12-20T11:16:34.495530Z"
407
+ 580,"1","SUGGESTIVE","2025-12-20T11:16:35.645933Z",580,"/data/upload/5/a2dd1244-ebe59192b6950204b3fee2c0edf9242d.jpg",0.897,"2025-12-20T11:16:35.645960Z"
408
+ 581,"1","UNCERTAIN","2025-12-20T11:16:38.122775Z",581,"/data/upload/5/670193c9-ebef8223abe988c0821aefae3c6e6e98.jpg",2.184,"2025-12-20T11:16:38.122798Z"
409
+ 582,"1","SUGGESTIVE","2025-12-20T11:16:39.382056Z",582,"/data/upload/5/c91cb641-ebf05bafa1371d6feb6aa81ea7cbbf7a.jpg",0.962,"2025-12-20T11:16:39.382081Z"
410
+ 583,"1","SUGGESTIVE","2025-12-20T11:16:40.717924Z",583,"/data/upload/5/e91af0b1-ebf0503fe32e2e3a76346007e4aaf886.jpg",1.082,"2025-12-20T11:16:40.717945Z"
411
+ 584,"1","FAMILY_SAFE","2025-12-20T11:16:42.560031Z",584,"/data/upload/5/5fddd072-ebb2b0dd48dc25f919f57ff2a2e7312a.jpg",1.529,"2025-12-20T11:16:42.560060Z"
412
+ 585,"1","UNCERTAIN","2025-12-20T11:16:47.348097Z",585,"/data/upload/5/26c7e03e-ebcf6c5f871a5dc3e6e9f035bf8fba78.jpg",4.49,"2025-12-20T11:16:47.348120Z"
413
+ 586,"1","SUGGESTIVE","2025-12-20T11:16:48.792913Z",586,"/data/upload/5/19060fc3-ebd170644a805b3966416bc6f2d40528.jpg",1.152,"2025-12-20T11:16:48.792936Z"
414
+ 587,"1","SUGGESTIVE","2025-12-20T11:16:50.003030Z",587,"/data/upload/5/6a2a26d9-eb8a6ed9fea3fd6949774e598fed9d20.jpg",0.913,"2025-12-20T11:16:50.003050Z"
415
+ 588,"1","SUGGESTIVE","2025-12-20T11:16:51.116372Z",588,"/data/upload/5/61cf0086-eb8d76f9ea022d2b18f119288e0fb083.jpg",0.818,"2025-12-20T11:16:51.116393Z"
416
+ 589,"1","SUGGESTIVE","2025-12-20T11:16:52.553391Z",589,"/data/upload/5/71c87364-eb9f10122a8e23579c8d1a2cdc0e8db4.jpg",1.141,"2025-12-20T11:16:52.553410Z"
417
+ 590,"1","SUGGESTIVE","2025-12-20T11:16:54.439366Z",590,"/data/upload/5/9eebe5b1-eb905d1b7fc7b0eac9adea12bee291bc.jpg",1.581,"2025-12-20T11:16:54.439389Z"
418
+ 591,"1","SUGGESTIVE","2025-12-20T11:16:55.809365Z",591,"/data/upload/5/b7ac5915-eb915f5d723fe6d575e7471e8130924d.jpg",1.065,"2025-12-20T11:16:55.809387Z"
419
+ 592,"1","FAMILY_SAFE","2025-12-20T11:16:57.902445Z",592,"/data/upload/5/700eea71-eb7967ec45df17fadffae1e9b84a1c03.jpg",1.838,"2025-12-20T11:16:57.902469Z"
420
+ 593,"1","SUGGESTIVE","2025-12-20T11:17:03.290332Z",593,"/data/upload/5/dd925e69-eba026d7ab6dcbf77c05e9e1fce9f98b.jpg",5.127,"2025-12-20T11:17:03.290353Z"
421
+ 594,"1","FAMILY_SAFE","2025-12-20T11:17:04.792415Z",594,"/data/upload/5/01dc90a4-eba313f10bf5953b52dc8145a668f4b1.jpg",1.253,"2025-12-20T11:17:04.792426Z"
422
+ 595,"1","FAMILY_SAFE","2025-12-20T11:17:06.088346Z",595,"/data/upload/5/49462a8c-eb3e848d5f3969eb49f10ef14db11593.jpg",1.058,"2025-12-20T11:17:06.088364Z"
423
+ 596,"1","UNCERTAIN","2025-12-20T11:17:08.374506Z",596,"/data/upload/5/84cabf94-eb46a86b864004fb56c426f3e6859869.jpg",2.047,"2025-12-20T11:17:08.374518Z"
424
+ 597,"1","SUGGESTIVE","2025-12-20T11:17:10.703608Z",597,"/data/upload/5/8d99d164-eb630bc86c8b347b6e4f3f947ef03b45.jpg",2.084,"2025-12-20T11:17:10.703628Z"
425
+ 598,"1","FAMILY_SAFE","2025-12-20T11:17:12.055943Z",598,"/data/upload/5/a5dc7a8b-eb0b4aec28838b01b635ae2d906319fd.jpg",1.095,"2025-12-20T11:17:12.055962Z"
426
+ 599,"1","SUGGESTIVE","2025-12-20T11:17:14.330023Z",599,"/data/upload/5/c998ec26-eb0fd6ae406bcbf648f37716832167b7.jpg",2.013,"2025-12-20T11:17:14.330043Z"
427
+ 600,"1","UNCERTAIN","2025-12-20T11:17:16.223352Z",600,"/data/upload/5/9a363203-eb1efa483d18843e2d34330b324e49e7.jpg",1.63,"2025-12-20T11:17:16.223375Z"
428
+ 601,"1","SUGGESTIVE","2025-12-20T11:17:18.298434Z",601,"/data/upload/5/80174771-eb2bbc40d914d047f6a15d0d1db4af9b.jpg",1.816,"2025-12-20T11:17:18.298455Z"
429
+ 602,"1","SUGGESTIVE","2025-12-20T11:17:20.449203Z",602,"/data/upload/5/da470e08-eb214a887113aeae8902116d5ebcb84a.jpg",1.891,"2025-12-20T11:17:20.449223Z"
430
+ 603,"1","UNCERTAIN","2025-12-20T11:17:22.482759Z",603,"/data/upload/5/c1369917-eb396372a0cbb497fcb259ecf9859fab.jpg",1.772,"2025-12-20T11:17:22.482780Z"
431
+ 604,"1","SUGGESTIVE","2025-12-20T11:17:23.676845Z",604,"/data/upload/5/eaf48512-eae00de851425f14cf837f6238fb3111.jpg",0.934,"2025-12-20T11:17:23.676867Z"
432
+ 605,"1","SUGGESTIVE","2025-12-20T11:17:25.074810Z",605,"/data/upload/5/3ae42af7-ea9478ccf8264c91cf5485c93fecba1b.jpg",1.109,"2025-12-20T11:17:25.074831Z"
433
+ 606,"1","FAMILY_SAFE","2025-12-20T11:17:26.367613Z",606,"/data/upload/5/990348b4-ea889002933fd0689c1bd805505d00ca.jpg",1.028,"2025-12-20T11:17:26.367637Z"
434
+ 607,"1","SUGGESTIVE","2025-12-20T11:17:27.785077Z",607,"/data/upload/5/77e2c941-eaa26e78346e1d92ee8b34a80aff9826.jpg",1.154,"2025-12-20T11:17:27.785097Z"
435
+ 608,"1","SUGGESTIVE","2025-12-20T11:17:29.276528Z",608,"/data/upload/5/775910aa-eaac5564f212f117e0ea7174b0876484.jpg",1.231,"2025-12-20T11:17:29.276546Z"
436
+ 609,"1","SUGGESTIVE","2025-12-20T11:17:30.986551Z",609,"/data/upload/5/b8405e3c-eac0ebc831eeb117fb36c86452b6c5f5.jpg",1.448,"2025-12-20T11:17:30.986569Z"
437
+ 610,"1","SUGGESTIVE","2025-12-20T11:17:32.280226Z",610,"/data/upload/5/1e5eb554-ea5d2de8ab1fd0ec4b61ed153805d638.jpg",1.031,"2025-12-20T11:17:32.280245Z"
438
+ 611,"1","SUGGESTIVE","2025-12-20T11:17:33.617114Z",611,"/data/upload/5/ee2ed958-ea6df97b08a8c96a9587abe7764f9cc3.jpg",1.069,"2025-12-20T11:17:33.617138Z"
439
+ 612,"1","SUGGESTIVE","2025-12-20T11:17:34.830280Z",612,"/data/upload/5/27bffd86-ea6fffbf1d3afbbbeea92a3c1c6deffd.jpg",0.96,"2025-12-20T11:17:34.830291Z"
440
+ 613,"1","SUGGESTIVE","2025-12-20T11:17:36.024430Z",613,"/data/upload/5/bc246849-ea71edf190810bac9c56483239eae59a.jpg",0.954,"2025-12-20T11:17:36.024452Z"
441
+ 614,"1","UNCERTAIN","2025-12-20T11:17:38.001067Z",614,"/data/upload/5/39c155ed-ea4b1f27828cbd87b0f95f1f36cd7a54.jpg",1.711,"2025-12-20T11:17:38.001089Z"
442
+ 615,"1","UNCERTAIN","2025-12-20T11:17:39.958934Z",615,"/data/upload/5/2b92f3dc-ea5380a591b6e34d1382a67221b558aa.jpg",1.693,"2025-12-20T11:17:39.958953Z"
443
+ 616,"1","UNCERTAIN","2025-12-20T11:17:42.141113Z",616,"/data/upload/5/03579438-ea1f42c6e20979a40230b242e5d6bbee.jpg",1.922,"2025-12-20T11:17:42.141136Z"
444
+ 617,"1","SUGGESTIVE","2025-12-20T11:17:47.238864Z",617,"/data/upload/5/e5da72ea-ea20b59163f3b360f62d68e4f132393b.jpg",4.806,"2025-12-20T11:17:47.238881Z"
445
+ 618,"1","UNCERTAIN","2025-12-20T11:17:49.325187Z",618,"/data/upload/5/7fce3819-ea37c94a8cb1c50c4084d4c9cc9bd177.jpg",1.798,"2025-12-20T11:17:49.325212Z"
446
+ 619,"1","UNCERTAIN","2025-12-20T11:17:51.372252Z",619,"/data/upload/5/0b24edd3-e9d77067389acfaa2897de087ec279a1.jpg",1.783,"2025-12-20T11:17:51.372275Z"
447
+ 620,"1","UNCERTAIN","2025-12-20T11:17:53.649618Z",620,"/data/upload/5/b61339af-e9e395951ba5901de8ba2b15899689ee.jpg",2.015,"2025-12-20T11:17:53.649643Z"
448
+ 621,"1","SUGGESTIVE","2025-12-20T11:17:55.332499Z",621,"/data/upload/5/bb7c935c-e9eefa0852edb5311b63d6fbf85a9815.jpg",1.416,"2025-12-20T11:17:55.332524Z"
449
+ 622,"1","SUGGESTIVE","2025-12-20T11:17:56.616158Z",622,"/data/upload/5/3bcb2655-e9f9cb6790fcb4c6960e5b834abfe72c.jpg",1.017,"2025-12-20T11:17:56.616182Z"
450
+ 623,"1","SUGGESTIVE","2025-12-20T11:17:57.786359Z",623,"/data/upload/5/630fff65-e9f0689b5df3c0bbb17ec7a670ebacb8.jpg",0.903,"2025-12-20T11:17:57.786380Z"
451
+ 624,"1","SUGGESTIVE","2025-12-20T11:17:58.867328Z",624,"/data/upload/5/3c9f8955-e9b23e6e929900bbbbb29be5affa0ae5.jpg",0.813,"2025-12-20T11:17:58.867353Z"
452
+ 625,"1","UNCERTAIN","2025-12-20T11:18:01.360483Z",625,"/data/upload/5/e2b2a6ac-e9a15368497b12a5f502151ce2babdd1.jpg",2.229,"2025-12-20T11:18:01.360506Z"
453
+ 626,"1","SUGGESTIVE","2025-12-20T11:18:02.960491Z",626,"/data/upload/5/28c4871f-e9ab035913a3f11963041ef9b89caa2e.jpg",1.347,"2025-12-20T11:18:02.960501Z"
454
+ 627,"1","SUGGESTIVE","2025-12-20T11:18:04.139209Z",627,"/data/upload/5/e41d2daa-e96c2e00fce0f439d17ad2731eb76f06.jpg",0.927,"2025-12-20T11:18:04.139229Z"
455
+ 628,"1","FAMILY_SAFE","2025-12-20T11:18:05.410684Z",628,"/data/upload/5/31a90818-e983a4f33a2035ff412231c35d7fcfa5.jpg",1.001,"2025-12-20T11:18:05.410706Z"
456
+ 629,"1","FAMILY_SAFE","2025-12-20T11:18:06.674246Z",629,"/data/upload/5/4da9491c-e95cf7d4d15184aeb5847e9e6a51e9d0.jpg",1.002,"2025-12-20T11:18:06.674269Z"
data/annotations.json ADDED
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data/falcons_predictions.csv ADDED
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1
+ annotation_id,file_upload,actual_filename,label_studio_choice,label_0,label_1,score_0,score_1
2
+ 175,0f7d40c8-ffd86ff5fbd82d0e6eb176cbc0b83634.jpg,0f7d40c8-ffd86ff5fbd82d0e6eb176cbc0b83634.jpg,UNCERTAIN,normal,nsfw,0.9997729659080505,0.00022706740128342062
3
+ 176,abe701ef-fff11e31ec0146bd469e5e3afa14a37a.jpg,abe701ef-fff11e31ec0146bd469e5e3afa14a37a.jpg,FAMILY_SAFE,normal,nsfw,0.9992530941963196,0.0007468326948583126
4
+ 177,f3f6d9e7-ff8ba374753afc33d6efcddfa8190820.jpg,f3f6d9e7-ff8ba374753afc33d6efcddfa8190820.jpg,UNCERTAIN,normal,nsfw,0.9977607727050781,0.0022392449900507927
5
+ 178,2a71e0e5-ff8f980e673cd0ca47acd5e46a60860e.jpg,2a71e0e5-ff8f980e673cd0ca47acd5e46a60860e.jpg,UNCERTAIN,normal,nsfw,0.9959889054298401,0.004011069890111685
6
+ 179,883fb0f5-ff89d127a396bc4e269161fcb0debafc.jpg,883fb0f5-ff89d127a396bc4e269161fcb0debafc.jpg,UNCERTAIN,normal,nsfw,0.9997225403785706,0.00027748438878916204
7
+ 180,2af0302e-ff943feb34677ab7380b172f5a879061.jpg,2af0302e-ff943feb34677ab7380b172f5a879061.jpg,FAMILY_SAFE,normal,nsfw,0.9970329999923706,0.0029670321382582188
8
+ 181,00b62e8d-ff8923c82c694582614e3c6426f27d50.jpg,00b62e8d-ff8923c82c694582614e3c6426f27d50.jpg,SUGGESTIVE,nsfw,normal,0.540604829788208,0.459395170211792
9
+ 182,bc29dd51-ffa3ea1e617fc51d9f897c3651d8c58c.jpg,bc29dd51-ffa3ea1e617fc51d9f897c3651d8c58c.jpg,UNCERTAIN,normal,nsfw,0.9997908473014832,0.00020912200852762908
10
+ 183,7efcc1a9-ffb14bc253b175a8064d17ffe2c7249b.jpg,7efcc1a9-ffb14bc253b175a8064d17ffe2c7249b.jpg,FAMILY_SAFE,normal,nsfw,0.9997037053108215,0.000296269339742139
11
+ 184,1ade517e-ff5c303cb978725b41ecdc33f99a9811.jpg,1ade517e-ff5c303cb978725b41ecdc33f99a9811.jpg,UNCERTAIN,normal,nsfw,0.9994058609008789,0.0005940930568613112
12
+ 185,e90235cf-ff4ee441959276cee003489e10030787.jpg,e90235cf-ff4ee441959276cee003489e10030787.jpg,FAMILY_SAFE,normal,nsfw,0.9998548030853271,0.0001452306896680966
13
+ 186,cb00e4b8-ff57d7cdec78610cfc32857254662054.jpg,cb00e4b8-ff57d7cdec78610cfc32857254662054.jpg,SUGGESTIVE,normal,nsfw,0.9996379613876343,0.000361983897164464
14
+ 187,d140fb6b-ff42165c1c025a99db8fd3fbf47b9e8d.jpg,d140fb6b-ff42165c1c025a99db8fd3fbf47b9e8d.jpg,SUGGESTIVE,normal,nsfw,0.9991843104362488,0.0008156667463481426
15
+ 188,a1cd3b22-ff0dbc383dfd54bed44ec0583cbba777.jpg,a1cd3b22-ff0dbc383dfd54bed44ec0583cbba777.jpg,SUGGESTIVE,normal,nsfw,0.9913567304611206,0.008643229492008686
16
+ 189,ed323d8b-ff3cae8c55408c7ed67ef1daf9751319.jpg,ed323d8b-ff3cae8c55408c7ed67ef1daf9751319.jpg,SUGGESTIVE,normal,nsfw,0.9957081079483032,0.00429188646376133
17
+ 190,c8ec92d5-fee9ea29f695f6f590dbaf3ecc99351b.jpg,c8ec92d5-fee9ea29f695f6f590dbaf3ecc99351b.jpg,UNCERTAIN,normal,nsfw,0.9986482262611389,0.0013518353225663304
18
+ 191,f2c80c32-ff0a5673ea6b3139e5440728d7be66b4.jpg,f2c80c32-ff0a5673ea6b3139e5440728d7be66b4.jpg,SUGGESTIVE,normal,nsfw,0.9998561143875122,0.00014389827265404165
19
+ 192,e9b17aee-ff007a23e0caf370e590d636413628fb.jpg,e9b17aee-ff007a23e0caf370e590d636413628fb.jpg,SUGGESTIVE,normal,nsfw,0.9974243640899658,0.0025756950490176678
20
+ 193,c53b2fea-feb2e70ea41a86df9c9984df1aff49d1.jpg,c53b2fea-feb2e70ea41a86df9c9984df1aff49d1.jpg,SUGGESTIVE,normal,nsfw,0.9991050362586975,0.0008949259063228965
21
+ 194,1540c751-feb3ae09e5e5e2d27b1c6b0d75e66c73.jpg,1540c751-feb3ae09e5e5e2d27b1c6b0d75e66c73.jpg,UNCERTAIN,normal,nsfw,0.9998250603675842,0.00017495673091616482
22
+ 195,18eb5521-febafb43e0a8a5a62bd64d0e1e2c51bb.jpg,18eb5521-febafb43e0a8a5a62bd64d0e1e2c51bb.jpg,UNCERTAIN,nsfw,normal,0.6787581443786621,0.3212418854236603
23
+ 196,09c99130-fec2e3149882333a9a5a381f562f9536.jpg,09c99130-fec2e3149882333a9a5a381f562f9536.jpg,FAMILY_SAFE,normal,nsfw,0.9995145797729492,0.0004854484577663243
24
+ 197,6d3cfee1-fec30d1ce7fd3c5f8056da81cc0f56cd.jpg,6d3cfee1-fec30d1ce7fd3c5f8056da81cc0f56cd.jpg,SUGGESTIVE,normal,nsfw,0.999420166015625,0.0005798744969069958
25
+ 198,02e91ed5-fecaf071c6d469a5e9db4516cf04c34f.jpg,02e91ed5-fecaf071c6d469a5e9db4516cf04c34f.jpg,UNCERTAIN,normal,nsfw,0.997307538986206,0.0026925040874630213
26
+ 199,6084597d-fed1a27739f7fc733adfe4843185b995.jpg,6084597d-fed1a27739f7fc733adfe4843185b995.jpg,UNCERTAIN,normal,nsfw,0.9895889759063721,0.010411050170660019
27
+ 200,18699932-fe8d4f54e561bf7a76f70e6b28299d59.jpg,18699932-fe8d4f54e561bf7a76f70e6b28299d59.jpg,FAMILY_SAFE,normal,nsfw,0.9997500777244568,0.00024995882995426655
28
+ 201,759b4eb3-fe4ec0e6a0803e088352ad9cba4e969a.jpg,759b4eb3-fe4ec0e6a0803e088352ad9cba4e969a.jpg,FAMILY_SAFE,normal,nsfw,0.9990509152412415,0.0009490289958193898
29
+ 202,fd0a4064-fe4f57b1dc8f46cbd79d69ad05652ac8.jpg,fd0a4064-fe4f57b1dc8f46cbd79d69ad05652ac8.jpg,UNCERTAIN,normal,nsfw,0.9997256398200989,0.0002743340446613729
30
+ 203,722170f8-fe61bb1e7709ddb889307cd6d644bbb9.jpg,722170f8-fe61bb1e7709ddb889307cd6d644bbb9.jpg,SUGGESTIVE,normal,nsfw,0.9990665316581726,0.0009335206705145538
31
+ 204,597903c6-fe446bc03fc71ccd0559f01bebbd073d.jpg,597903c6-fe446bc03fc71ccd0559f01bebbd073d.jpg,UNCERTAIN,normal,nsfw,0.985272228717804,0.01472778432071209
32
+ 205,ded203db-fe11b6d8acc63b2819390a12a32bbd30.jpg,ded203db-fe11b6d8acc63b2819390a12a32bbd30.jpg,UNCERTAIN,normal,nsfw,0.998673677444458,0.001326378551311791
33
+ 206,72e813f5-fde81d8132b2c70f39798f11186997bd.jpg,72e813f5-fde81d8132b2c70f39798f11186997bd.jpg,FAMILY_SAFE,normal,nsfw,0.8751723170280457,0.12482769787311554
34
+ 207,6474cda9-fde879b3ab6d5f8fb99aacad817dc18f.jpg,6474cda9-fde879b3ab6d5f8fb99aacad817dc18f.jpg,FAMILY_SAFE,normal,nsfw,0.9998339414596558,0.0001660660345805809
35
+ 208,9d734426-fdfc5d73210e52e70f9c1b337c7a7423.jpg,9d734426-fdfc5d73210e52e70f9c1b337c7a7423.jpg,FAMILY_SAFE,normal,nsfw,0.999377965927124,0.0006220253999345005
36
+ 209,940a8d2a-fe06e1df8abfcc599b4edf3ceb84b767.jpg,940a8d2a-fe06e1df8abfcc599b4edf3ceb84b767.jpg,UNCERTAIN,normal,nsfw,0.9946976900100708,0.005302340257912874
37
+ 210,6cf544b3-fdcc56c858be84644a24f71ca1ecf009.jpg,6cf544b3-fdcc56c858be84644a24f71ca1ecf009.jpg,FAMILY_SAFE,normal,nsfw,0.9998241066932678,0.0001759189326548949
38
+ 211,c90492e9-fdce40a990a776a3ef934aa8eacfa0da.jpg,c90492e9-fdce40a990a776a3ef934aa8eacfa0da.jpg,FAMILY_SAFE,normal,nsfw,0.9997498393058777,0.0002501762646716088
39
+ 212,d5d56f5c-fdd0daec3624d1d7acf8048484a7efe2.jpg,d5d56f5c-fdd0daec3624d1d7acf8048484a7efe2.jpg,FAMILY_SAFE,normal,nsfw,0.9997718930244446,0.00022811919916421175
40
+ 213,1b6ade1e-fdbea88aeaa93ac7dbcb38d07b917e7d.jpg,1b6ade1e-fdbea88aeaa93ac7dbcb38d07b917e7d.jpg,UNCERTAIN,normal,nsfw,0.9995244741439819,0.0004754928813781589
41
+ 214,e7c85aca-fdc00eac708506181cc0b3018d217b97.jpg,e7c85aca-fdc00eac708506181cc0b3018d217b97.jpg,UNCERTAIN,normal,nsfw,0.9997454285621643,0.0002546043542679399
42
+ 215,6519a095-fd8eea5a65e5ef6cec86fe214c5a3cd3.jpg,6519a095-fd8eea5a65e5ef6cec86fe214c5a3cd3.jpg,SUGGESTIVE,normal,nsfw,0.9910959005355835,0.00890403613448143
43
+ 216,bdce6a65-fd918dbde3d770d03716835677e7b611.jpg,bdce6a65-fd918dbde3d770d03716835677e7b611.jpg,FAMILY_SAFE,normal,nsfw,0.999869704246521,0.0001302508608205244
44
+ 217,8ed58548-fd9371dd911d3a2099a6a652db4248b4.jpg,8ed58548-fd9371dd911d3a2099a6a652db4248b4.jpg,SUGGESTIVE,normal,nsfw,0.9998261332511902,0.00017390357970725745
45
+ 218,72f07c2b-fd94006f594a7575961947fcca9faa08.jpg,72f07c2b-fd94006f594a7575961947fcca9faa08.jpg,SUGGESTIVE,normal,nsfw,0.9692232608795166,0.030776789411902428
46
+ 219,9eb0117b-fd7f50b9d57c9384041914d1b4cdd77c.jpg,9eb0117b-fd7f50b9d57c9384041914d1b4cdd77c.jpg,FAMILY_SAFE,normal,nsfw,0.9997332692146301,0.0002667715307325125
47
+ 220,e243712d-fd71a7a81dea0c9fc195cf6d6f32b0a9.jpg,e243712d-fd71a7a81dea0c9fc195cf6d6f32b0a9.jpg,FAMILY_SAFE,normal,nsfw,0.999825656414032,0.000174321947270073
48
+ 221,3198b794-fd826bc213d0273afbb83b3c4aec1adc.jpg,3198b794-fd826bc213d0273afbb83b3c4aec1adc.jpg,FAMILY_SAFE,normal,nsfw,0.9998593330383301,0.00014069890312384814
49
+ 222,75ee25dc-fd71396788725bb988c9cd3689fcde68.jpg,75ee25dc-fd71396788725bb988c9cd3689fcde68.jpg,FAMILY_SAFE,normal,nsfw,0.9998794794082642,0.00012045811308780685
50
+ 223,3dade0ea-fd3e2ce0703f9c027862f5a79c788b56.jpg,3dade0ea-fd3e2ce0703f9c027862f5a79c788b56.jpg,UNCERTAIN,normal,nsfw,0.9997287392616272,0.0002712743589654565
51
+ 224,13a4e537-fd4bbf6927a13c5289dfa509b7a4078b.jpg,13a4e537-fd4bbf6927a13c5289dfa509b7a4078b.jpg,FAMILY_SAFE,normal,nsfw,0.9997079968452454,0.0002919857215601951
52
+ 225,afddcafe-fd4da06b37fbae5380cf0d7fa0678103.jpg,afddcafe-fd4da06b37fbae5380cf0d7fa0678103.jpg,SUGGESTIVE,normal,nsfw,0.9938650727272034,0.006134872790426016
53
+ 226,84acb822-fd4eb64e7f0ca587d908435fb95b2909.jpg,84acb822-fd4eb64e7f0ca587d908435fb95b2909.jpg,FAMILY_SAFE,normal,nsfw,0.9997391104698181,0.00026090958272106946
54
+ 227,022b4f3e-fd39709199d9f45147c183dee3c5d697.jpg,022b4f3e-fd39709199d9f45147c183dee3c5d697.jpg,FAMILY_SAFE,normal,nsfw,0.9998074173927307,0.00019258396059740335
55
+ 228,d4ccee96-fd24b551b4531ae14433a7950eaa85b3.jpg,d4ccee96-fd24b551b4531ae14433a7950eaa85b3.jpg,SUGGESTIVE,normal,nsfw,0.9998704195022583,0.00012956224964000285
56
+ 229,3a78a22b-fd38a720ae06640dd25aaf9c11dd9672.jpg,3a78a22b-fd38a720ae06640dd25aaf9c11dd9672.jpg,FAMILY_SAFE,normal,nsfw,0.9983555674552917,0.0016443702625110745
57
+ 230,6d307a2e-fd2268af5eafa750a7e5a3a521ff7fdd.jpg,6d307a2e-fd2268af5eafa750a7e5a3a521ff7fdd.jpg,UNCERTAIN,normal,nsfw,0.9991208910942078,0.000879096332937479
58
+ 231,b224fc67-fd150330c8a62cd9ab45e51f4de6969f.jpg,b224fc67-fd150330c8a62cd9ab45e51f4de6969f.jpg,SUGGESTIVE,normal,nsfw,0.9981344938278198,0.0018654720624908805
59
+ 232,6da2441b-fcf901727f0b68c35c55f5734189c1f0.jpg,6da2441b-fcf901727f0b68c35c55f5734189c1f0.jpg,UNCERTAIN,normal,nsfw,0.9996097683906555,0.00039020637632347643
60
+ 233,ef1cbf46-fd08dc2a899b79496087b34b88b82f2d.jpg,ef1cbf46-fd08dc2a899b79496087b34b88b82f2d.jpg,SUGGESTIVE,normal,nsfw,0.9989173412322998,0.001082667731679976
61
+ 234,d1a55b96-fca9c570e98556bb8c40835c55cba67d.jpg,d1a55b96-fca9c570e98556bb8c40835c55cba67d.jpg,SUGGESTIVE,normal,nsfw,0.9889764189720154,0.011023531667888165
62
+ 235,fc1397fa-fcbbcf0191ab925aa67604ff0c7f141e.jpg,fc1397fa-fcbbcf0191ab925aa67604ff0c7f141e.jpg,SUGGESTIVE,normal,nsfw,0.9978255033493042,0.0021744840778410435
63
+ 236,74454c95-fcbf1a2490d9531efc33322b2702912f.jpg,74454c95-fcbf1a2490d9531efc33322b2702912f.jpg,SUGGESTIVE,normal,nsfw,0.9995723366737366,0.0004276306426618248
64
+ 237,b7206afa-fcc50f8b1362042b4acad74a4b4ae4ed.jpg,b7206afa-fcc50f8b1362042b4acad74a4b4ae4ed.jpg,FAMILY_SAFE,normal,nsfw,0.9998793601989746,0.00012062559835612774
65
+ 238,64be13ce-fc813fdb8cb41651b9b4d5141fcd386a.jpg,64be13ce-fc813fdb8cb41651b9b4d5141fcd386a.jpg,SUGGESTIVE,normal,nsfw,0.9902662634849548,0.009733765386044979
66
+ 239,d5c64c7c-fca4fe8aa6a350ab9680692ebbe1e13c.jpg,d5c64c7c-fca4fe8aa6a350ab9680692ebbe1e13c.jpg,SUGGESTIVE,normal,nsfw,0.9657689332962036,0.034231096506118774
67
+ 240,8af150d5-fca025dcf76a43feae0bf2e8e9a6c1e2.jpg,8af150d5-fca025dcf76a43feae0bf2e8e9a6c1e2.jpg,SUGGESTIVE,normal,nsfw,0.9997478127479553,0.00025219659437425435
68
+ 241,0df4b27b-fc6fefe3f417fdbfd91da8cffe59f7db.jpg,0df4b27b-fc6fefe3f417fdbfd91da8cffe59f7db.jpg,SUGGESTIVE,normal,nsfw,0.9997876286506653,0.00021238009503576905
69
+ 242,a90c3b4e-fc39f11aab8aae4bb03057701a7b8312.jpg,a90c3b4e-fc39f11aab8aae4bb03057701a7b8312.jpg,SUGGESTIVE,normal,nsfw,0.9993686079978943,0.0006314468337222934
70
+ 243,3c42d798-fc74b1fc4095a324b1f1ca1ac322278d.jpg,3c42d798-fc74b1fc4095a324b1f1ca1ac322278d.jpg,FAMILY_SAFE,normal,nsfw,0.9997140765190125,0.0002858526713680476
71
+ 244,0bfb63b2-fc644a2683a976d8391e70f88408468b.jpg,0bfb63b2-fc644a2683a976d8391e70f88408468b.jpg,FAMILY_SAFE,normal,nsfw,0.9951044321060181,0.0048955390229821205
72
+ 245,b0091e81-fbf4b07fffa61fa2650649ec19debf39.jpg,b0091e81-fbf4b07fffa61fa2650649ec19debf39.jpg,FAMILY_SAFE,normal,nsfw,0.9922400712966919,0.0077599151991307735
73
+ 246,6faa5147-fbf13c899fbdd8e750dc46cc284249c5.jpg,6faa5147-fbf13c899fbdd8e750dc46cc284249c5.jpg,SUGGESTIVE,normal,nsfw,0.9998651742935181,0.00013476356980390847
74
+ 247,bcfeea6a-fc1baf625d3e36ca4c5a29521f72525e.jpg,bcfeea6a-fc1baf625d3e36ca4c5a29521f72525e.jpg,FAMILY_SAFE,normal,nsfw,0.9995433688163757,0.0004566544957924634
75
+ 248,0d5b1538-fc21d81fb821d214b4f4f1d7f553475d.jpg,0d5b1538-fc21d81fb821d214b4f4f1d7f553475d.jpg,FAMILY_SAFE,normal,nsfw,0.9997746348381042,0.0002253740094602108
76
+ 249,cfb6f5d7-fc07723d545d098aafab4aeb7d2e65a3.jpg,cfb6f5d7-fc07723d545d098aafab4aeb7d2e65a3.jpg,UNCERTAIN,normal,nsfw,0.999457061290741,0.000542939524166286
77
+ 250,43a803bd-fbcddca8eb614e2e2076c1128d30fa58.jpg,43a803bd-fbcddca8eb614e2e2076c1128d30fa58.jpg,SUGGESTIVE,normal,nsfw,0.9997774958610535,0.00022255742806009948
78
+ 251,930fa681-fbd64159d7d027262f736408659d40cb.jpg,930fa681-fbd64159d7d027262f736408659d40cb.jpg,FAMILY_SAFE,normal,nsfw,0.9990699887275696,0.0009300094679929316
79
+ 252,c884f2e6-fbb763c6f6f858fdfc270f3f3bda1eac.jpg,c884f2e6-fbb763c6f6f858fdfc270f3f3bda1eac.jpg,SUGGESTIVE,normal,nsfw,0.9953006505966187,0.004699346609413624
80
+ 253,b3f73152-fbc604a00ac5d9d6a73cec8789a75d0c.jpg,b3f73152-fbc604a00ac5d9d6a73cec8789a75d0c.jpg,FAMILY_SAFE,normal,nsfw,0.993653416633606,0.0063466038554906845
81
+ 254,f3e719e9-fbc9929373c24b1afd9523aa5253a594.jpg,f3e719e9-fbc9929373c24b1afd9523aa5253a594.jpg,SUGGESTIVE,normal,nsfw,0.9996848106384277,0.00031520574702881277
82
+ 255,ac2b938f-fbca15037c332d6e9e658e2129217794.jpg,ac2b938f-fbca15037c332d6e9e658e2129217794.jpg,SUGGESTIVE,normal,nsfw,0.9951456189155579,0.00485434802249074
83
+ 256,e0092f87-fb6ad7084b95e0bb2eb9575082907662.jpg,e0092f87-fb6ad7084b95e0bb2eb9575082907662.jpg,SUGGESTIVE,normal,nsfw,0.9990241527557373,0.000975856208242476
84
+ 257,efd3b12b-fb7ba81d49347796a797d1322a64cb71.jpg,efd3b12b-fb7ba81d49347796a797d1322a64cb71.jpg,SUGGESTIVE,normal,nsfw,0.9998902082443237,0.0001097766580642201
85
+ 258,74426174-fb701d0e6a714179959ac8e1e4e2cb88.jpg,74426174-fb701d0e6a714179959ac8e1e4e2cb88.jpg,FAMILY_SAFE,normal,nsfw,0.9997884631156921,0.00021158819436095655
86
+ 259,68adaf6f-fb61755f0c2afaafdd9aa36f7632cbe5.jpg,68adaf6f-fb61755f0c2afaafdd9aa36f7632cbe5.jpg,SUGGESTIVE,normal,nsfw,0.9869140982627869,0.01308595109730959
87
+ 260,c61c6042-fb607386dd384e6b0d53ef878bbffc55.jpg,c61c6042-fb607386dd384e6b0d53ef878bbffc55.jpg,FAMILY_SAFE,normal,nsfw,0.9994868040084839,0.0005132092628628016
88
+ 261,7ec3bcbe-fb2e1bdb34ace56b99befc2547a8b83d.jpg,7ec3bcbe-fb2e1bdb34ace56b99befc2547a8b83d.jpg,FAMILY_SAFE,normal,nsfw,0.9998151659965515,0.00018488273781258613
89
+ 262,bec475e6-fb3c593276f0f040a7450ca213d97882.jpg,bec475e6-fb3c593276f0f040a7450ca213d97882.jpg,FAMILY_SAFE,normal,nsfw,0.9996404647827148,0.00035952351754531264
90
+ 263,966460f7-fb50d0f85eb7e491722e734b961926f0.jpg,966460f7-fb50d0f85eb7e491722e734b961926f0.jpg,FAMILY_SAFE,normal,nsfw,0.9993305206298828,0.0006695562624372542
91
+ 264,243861f5-fb468ddeda48f68ec3539bebe07b92b4.jpg,243861f5-fb468ddeda48f68ec3539bebe07b92b4.jpg,FAMILY_SAFE,normal,nsfw,0.9998698234558105,0.00013020416372455657
92
+ 265,54045990-fb1c02d4c50a59be3fe3cb249de403e0.jpg,54045990-fb1c02d4c50a59be3fe3cb249de403e0.jpg,SUGGESTIVE,normal,nsfw,0.986099362373352,0.013900673016905785
93
+ 266,7439530f-faf1f5d9d6e51a84ce632df317dd5c5c.jpg,7439530f-faf1f5d9d6e51a84ce632df317dd5c5c.jpg,UNCERTAIN,normal,nsfw,0.9969670176506042,0.0030329835135489702
94
+ 267,e0eec550-faf27f9511974b5ef529ba718677c5f4.jpg,e0eec550-faf27f9511974b5ef529ba718677c5f4.jpg,UNCERTAIN,normal,nsfw,0.9993395209312439,0.00066045590210706
95
+ 268,1101ce4b-fab7821aae25aff865b20b0d33d86900.jpg,1101ce4b-fab7821aae25aff865b20b0d33d86900.jpg,FAMILY_SAFE,normal,nsfw,0.9998095631599426,0.00019051322306040674
96
+ 269,26464718-fac69b44cccb94236c9f905bed46158f.jpg,26464718-fac69b44cccb94236c9f905bed46158f.jpg,FAMILY_SAFE,normal,nsfw,0.9996384382247925,0.00036152900429442525
97
+ 270,d588345d-fad53b443bc3e52bada85783afdac562.jpg,d588345d-fad53b443bc3e52bada85783afdac562.jpg,FAMILY_SAFE,normal,nsfw,0.9911034107208252,0.008896546438336372
98
+ 271,7f1fa2e7-fad69c88bcef4434a55b3bf3854b3e56.jpg,7f1fa2e7-fad69c88bcef4434a55b3bf3854b3e56.jpg,UNCERTAIN,normal,nsfw,0.9998892545700073,0.00011075734801124781
99
+ 272,717afd8f-fa6ef41aabda28f51249407ce46a827e.jpg,717afd8f-fa6ef41aabda28f51249407ce46a827e.jpg,FAMILY_SAFE,normal,nsfw,0.9992254972457886,0.0007745388429611921
100
+ 273,191a25a2-fa7b42eb84363adcd60082a3a7f02f84.jpg,191a25a2-fa7b42eb84363adcd60082a3a7f02f84.jpg,UNCERTAIN,normal,nsfw,0.9753845930099487,0.024615352973341942
101
+ 274,41febcdf-fa9e77f0788145d7ac42edcffb6e9dee.jpg,41febcdf-fa9e77f0788145d7ac42edcffb6e9dee.jpg,FAMILY_SAFE,normal,nsfw,0.9998276233673096,0.00017234891129191965
102
+ 275,4bb4ff8b-fa9f2688e1b4e92538569ecb9efecf2e.jpg,4bb4ff8b-fa9f2688e1b4e92538569ecb9efecf2e.jpg,UNCERTAIN,normal,nsfw,0.9995473027229309,0.000452632230008021
103
+ 276,12aefe0a-fa87c0f54b8cb07a14fc311c369e9a4c.jpg,12aefe0a-fa87c0f54b8cb07a14fc311c369e9a4c.jpg,UNCERTAIN,normal,nsfw,0.9997121691703796,0.00028786665643565357
104
+ 277,0f927896-fa94a466d4178dcd4c762f7567c11147.jpg,0f927896-fa94a466d4178dcd4c762f7567c11147.jpg,SUGGESTIVE,normal,nsfw,0.9997789263725281,0.00022108960547484457
105
+ 278,3b82e875-faa13416b0d8d2adc2b6815dd1e6c82d.jpg,3b82e875-faa13416b0d8d2adc2b6815dd1e6c82d.jpg,FAMILY_SAFE,normal,nsfw,0.9997296929359436,0.00027034926461055875
106
+ 279,7a5551c7-fa5b56c4e382fd4dca384daf1768acb4.jpg,7a5551c7-fa5b56c4e382fd4dca384daf1768acb4.jpg,SUGGESTIVE,normal,nsfw,0.9997959733009338,0.00020401085203047842
107
+ 280,abf1e90d-fa5c6d304163052761d4453c693c202d.jpg,abf1e90d-fa5c6d304163052761d4453c693c202d.jpg,FAMILY_SAFE,normal,nsfw,0.9998476505279541,0.00015236144827213138
108
+ 281,150e925a-fa6a15e903e7949093f4ec11fcffdb01.jpg,150e925a-fa6a15e903e7949093f4ec11fcffdb01.jpg,UNCERTAIN,normal,nsfw,0.999750554561615,0.0002493993961252272
109
+ 282,76aa5a7e-fa41e4a96249c0b8ed90389733c081d8.jpg,76aa5a7e-fa41e4a96249c0b8ed90389733c081d8.jpg,SUGGESTIVE,normal,nsfw,0.9998562335968018,0.00014372151053976268
110
+ 283,891276e7-fa63fcd0e11eb612b2c8f1ade43de7c8.jpg,891276e7-fa63fcd0e11eb612b2c8f1ade43de7c8.jpg,SUGGESTIVE,normal,nsfw,0.9962157607078552,0.0037842097226530313
111
+ 284,0fea6f76-fa412fb78ec94ab051ac0880e255a897.jpg,0fea6f76-fa412fb78ec94ab051ac0880e255a897.jpg,FAMILY_SAFE,normal,nsfw,0.9998717308044434,0.0001282975572394207
112
+ 285,7cb410fd-fa1a50f4102b66555a1a05afa9ddbf3c.jpg,7cb410fd-fa1a50f4102b66555a1a05afa9ddbf3c.jpg,UNCERTAIN,normal,nsfw,0.9993300437927246,0.0006699986406601965
113
+ 286,907cb3ad-fa1dab55d34f1c7140ea41a1b3ab5eae.jpg,907cb3ad-fa1dab55d34f1c7140ea41a1b3ab5eae.jpg,UNCERTAIN,normal,nsfw,0.9992107152938843,0.0007892599678598344
114
+ 287,bf824357-fa2c514037f31c0ab98d66373d371e3a.jpg,bf824357-fa2c514037f31c0ab98d66373d371e3a.jpg,FAMILY_SAFE,normal,nsfw,0.9998695850372314,0.000130453088786453
115
+ 288,f953d2e3-f9f74d590b0f4d8da8ba20a07bc2a596.jpg,f953d2e3-f9f74d590b0f4d8da8ba20a07bc2a596.jpg,FAMILY_SAFE,normal,nsfw,0.9996881484985352,0.0003119299071840942
116
+ 289,db2c891b-f9fc35243c2c7592c2d5858be342a34d.jpg,db2c891b-f9fc35243c2c7592c2d5858be342a34d.jpg,FAMILY_SAFE,normal,nsfw,0.999743640422821,0.00025635765632614493
117
+ 290,53bd404d-fa068c5e8fe813fbb1d90b5a0744e172.jpg,53bd404d-fa068c5e8fe813fbb1d90b5a0744e172.jpg,UNCERTAIN,normal,nsfw,0.999733030796051,0.00026693433756008744
118
+ 291,a23fc1cb-fa075d603e631d04c9871dbb00ed0a14.jpg,a23fc1cb-fa075d603e631d04c9871dbb00ed0a14.jpg,SUGGESTIVE,normal,nsfw,0.9997664093971252,0.0002335895405849442
119
+ 292,ee0e3f1d-fa1607fba97dd47d889e0c8eb0a82624.jpg,ee0e3f1d-fa1607fba97dd47d889e0c8eb0a82624.jpg,SUGGESTIVE,normal,nsfw,0.9997625946998596,0.00023744819918647408
120
+ 293,3aef96bb-f9d6c4a53c12013b5bbb6e830275486e.jpg,3aef96bb-f9d6c4a53c12013b5bbb6e830275486e.jpg,SUGGESTIVE,normal,nsfw,0.9998776912689209,0.00012230630090925843
121
+ 294,9f665793-f9ea859c158d6cece283b619afdf2f49.jpg,9f665793-f9ea859c158d6cece283b619afdf2f49.jpg,SUGGESTIVE,normal,nsfw,0.9998897314071655,0.00011029541201423854
122
+ 295,db0aaa30-f9a1f16e76e3adad3ce2ed9fe0c7a03f.jpg,db0aaa30-f9a1f16e76e3adad3ce2ed9fe0c7a03f.jpg,UNCERTAIN,normal,nsfw,0.9998188614845276,0.0001811861147871241
123
+ 296,03abd3cc-f9aaa2af6fea2261610e48ed5e11ede4.jpg,03abd3cc-f9aaa2af6fea2261610e48ed5e11ede4.jpg,UNCERTAIN,normal,nsfw,0.9996980428695679,0.0003019814030267298
124
+ 297,d741a3e7-f9b3403712c87698322446ef48a0027c.jpg,d741a3e7-f9b3403712c87698322446ef48a0027c.jpg,SUGGESTIVE,normal,nsfw,0.9936589598655701,0.006341090425848961
125
+ 298,b502117c-f9bd44609a914f7aabd3a901d457b1e7.jpg,b502117c-f9bd44609a914f7aabd3a901d457b1e7.jpg,SUGGESTIVE,nsfw,normal,0.9648807644844055,0.035119153559207916
126
+ 299,96ba7ff4-f984b6e28aa8e0e13f71bd41a8c65194.jpg,96ba7ff4-f984b6e28aa8e0e13f71bd41a8c65194.jpg,SUGGESTIVE,normal,nsfw,0.9775092601776123,0.022490795701742172
127
+ 300,85f90f88-f96b6fc62dadba4eb00b5cfaa1168b87.jpg,85f90f88-f96b6fc62dadba4eb00b5cfaa1168b87.jpg,FAMILY_SAFE,normal,nsfw,0.9998483657836914,0.0001515850017312914
128
+ 301,32e49293-f96bffb22c42bd3beb968d5ff5583c6b.jpg,32e49293-f96bffb22c42bd3beb968d5ff5583c6b.jpg,SUGGESTIVE,normal,nsfw,0.9995248317718506,0.00047522177919745445
129
+ 302,263d89d9-f96c4c07dcded0f0fd1aa150a693a181.jpg,263d89d9-f96c4c07dcded0f0fd1aa150a693a181.jpg,FAMILY_SAFE,normal,nsfw,0.9998806715011597,0.00011933724454138428
130
+ 303,78e8d1c7-f96c7473c85142a222056be7c7759184.jpg,78e8d1c7-f96c7473c85142a222056be7c7759184.jpg,SUGGESTIVE,normal,nsfw,0.9996101260185242,0.0003899533476214856
131
+ 304,0fc4362d-f9610ac04fe21d9bd96689b8c6ae1669.jpg,0fc4362d-f9610ac04fe21d9bd96689b8c6ae1669.jpg,FAMILY_SAFE,normal,nsfw,0.9996802806854248,0.0003197768528480083
132
+ 305,24f3bf36-f9633749641995381ff42d72352bafe2.jpg,24f3bf36-f9633749641995381ff42d72352bafe2.jpg,UNCERTAIN,normal,nsfw,0.9978522062301636,0.0021477804984897375
133
+ 306,3216e20d-f93c3f8af10c70801ce921842c77afc4.jpg,3216e20d-f93c3f8af10c70801ce921842c77afc4.jpg,UNCERTAIN,normal,nsfw,0.9602324366569519,0.039767488837242126
134
+ 307,93bfe360-f95143e4ae1515b8bf5c2d813aa36bbc.jpg,93bfe360-f95143e4ae1515b8bf5c2d813aa36bbc.jpg,SUGGESTIVE,normal,nsfw,0.999043881893158,0.0009561094339005649
135
+ 308,0a08bf3b-f954833a10de7c149ac73c5b4af4c189.jpg,0a08bf3b-f954833a10de7c149ac73c5b4af4c189.jpg,FAMILY_SAFE,normal,nsfw,0.9998812675476074,0.00011877575161634013
136
+ 309,a54eda5e-f8fdf05663173fa7dc689333e3f21f23.jpg,a54eda5e-f8fdf05663173fa7dc689333e3f21f23.jpg,SUGGESTIVE,normal,nsfw,0.9991862177848816,0.0008137212134897709
137
+ 310,29f522c1-f91a65be37a0e49d78ac4f6bc0cfcb61.jpg,29f522c1-f91a65be37a0e49d78ac4f6bc0cfcb61.jpg,FAMILY_SAFE,normal,nsfw,0.9990737438201904,0.0009262749226763844
138
+ 311,43ccd6b7-f8b2c595800cb08b05b4f2503fdbbced.jpg,43ccd6b7-f8b2c595800cb08b05b4f2503fdbbced.jpg,SUGGESTIVE,normal,nsfw,0.9903642535209656,0.009635809808969498
139
+ 312,c7f29f62-f8b38d085dc15913e26ed1580d0cb644.jpg,c7f29f62-f8b38d085dc15913e26ed1580d0cb644.jpg,UNCERTAIN,normal,nsfw,0.9997991919517517,0.0002008238952839747
140
+ 313,ca35d72e-f8c8ee06b8b818d1f0f5229014ee8760.jpg,ca35d72e-f8c8ee06b8b818d1f0f5229014ee8760.jpg,UNCERTAIN,normal,nsfw,0.9824240803718567,0.01757584512233734
141
+ 314,65120b69-f8ee23b284b41157bb5b9eb488cf5828.jpg,65120b69-f8ee23b284b41157bb5b9eb488cf5828.jpg,SUGGESTIVE,normal,nsfw,0.9984427094459534,0.0015573232667520642
142
+ 315,4abc60a4-f8a72d3f92f7e3793a093556c5b6b446.jpg,4abc60a4-f8a72d3f92f7e3793a093556c5b6b446.jpg,SUGGESTIVE,normal,nsfw,0.9984434247016907,0.0015565553912892938
143
+ 316,ee55d94c-f8a860d078da463b28618029d547eca0.jpg,ee55d94c-f8a860d078da463b28618029d547eca0.jpg,SUGGESTIVE,normal,nsfw,0.9984941482543945,0.001505902037024498
144
+ 317,42ea7be6-f8a950c61b1b181a99bb6837f76ddcf5.jpg,42ea7be6-f8a950c61b1b181a99bb6837f76ddcf5.jpg,SUGGESTIVE,normal,nsfw,0.8773965835571289,0.12260346859693527
145
+ 318,2b96cec7-f87b6da37656aef1c82f6841cdfd216a.jpg,2b96cec7-f87b6da37656aef1c82f6841cdfd216a.jpg,FAMILY_SAFE,normal,nsfw,0.9998650550842285,0.00013486770330928266
146
+ 319,a4651f0f-f8714d6a5bafd4d86377bf04957f37e5.jpg,a4651f0f-f8714d6a5bafd4d86377bf04957f37e5.jpg,SUGGESTIVE,normal,nsfw,0.9695289731025696,0.030470989644527435
147
+ 320,fee5f161-f8856cf1b3bd39861cb9f95337cd0992.jpg,fee5f161-f8856cf1b3bd39861cb9f95337cd0992.jpg,FAMILY_SAFE,normal,nsfw,0.9998781681060791,0.00012184139632154256
148
+ 321,26999a9e-f8869ae287068ce36b4b05663da6713a.jpg,26999a9e-f8869ae287068ce36b4b05663da6713a.jpg,FAMILY_SAFE,normal,nsfw,0.9998818635940552,0.00011806297698058188
149
+ 322,2d689dc9-f852dde27ecdc40533bb40683087f7e1.jpg,2d689dc9-f852dde27ecdc40533bb40683087f7e1.jpg,UNCERTAIN,normal,nsfw,0.9998548030853271,0.0001451258867746219
150
+ 323,207cc31f-f86351d67e2629dc35f1e524915fcd11.jpg,207cc31f-f86351d67e2629dc35f1e524915fcd11.jpg,SUGGESTIVE,normal,nsfw,0.9993031024932861,0.000696860603056848
151
+ 324,f3898f2e-f8447631bc00c8f2467b1a20ee6bfa78.jpg,f3898f2e-f8447631bc00c8f2467b1a20ee6bfa78.jpg,UNCERTAIN,normal,nsfw,0.9997257590293884,0.000274246180197224
152
+ 325,8cb018af-f8542037683b99dbc67c7f7d6c5a46b3.jpg,8cb018af-f8542037683b99dbc67c7f7d6c5a46b3.jpg,UNCERTAIN,normal,nsfw,0.9981718063354492,0.00182823627255857
153
+ 326,5d6063fc-f822f1d61d47f66997ce6f86c974a4a4.jpg,5d6063fc-f822f1d61d47f66997ce6f86c974a4a4.jpg,FAMILY_SAFE,normal,nsfw,0.9998130202293396,0.00018702170928008854
154
+ 327,57bec3b8-f8222b4da2ca05b7b639120cac5f92f2.jpg,57bec3b8-f8222b4da2ca05b7b639120cac5f92f2.jpg,SUGGESTIVE,normal,nsfw,0.9990129470825195,0.000986975384876132
155
+ 328,918752f9-f7e4163d39bf966f1d403bfcf469cc5f.jpg,918752f9-f7e4163d39bf966f1d403bfcf469cc5f.jpg,UNCERTAIN,normal,nsfw,0.9995145797729492,0.0004854544240515679
156
+ 329,bdce8a23-f7f267f10da481cbc97981f022b36a07.jpg,bdce8a23-f7f267f10da481cbc97981f022b36a07.jpg,SUGGESTIVE,normal,nsfw,0.9884392619132996,0.011560764163732529
157
+ 330,d9e26b6c-f7de775f4820d021c70c42e8785c047b.jpg,d9e26b6c-f7de775f4820d021c70c42e8785c047b.jpg,SUGGESTIVE,normal,nsfw,0.9781965613365173,0.02180345170199871
158
+ 331,654d822f-f7e1c87ca29b75a4e59263db1e0f59a1.jpg,654d822f-f7e1c87ca29b75a4e59263db1e0f59a1.jpg,UNCERTAIN,normal,nsfw,0.9995540976524353,0.00044591561891138554
159
+ 332,13f55350-f7c55782f457e6c3c4a17c4f4254a946.jpg,13f55350-f7c55782f457e6c3c4a17c4f4254a946.jpg,FAMILY_SAFE,normal,nsfw,0.9995529055595398,0.0004470178682822734
160
+ 333,bf4fd201-f7aa8582cc615c5efc747a723ed54f9e.jpg,bf4fd201-f7aa8582cc615c5efc747a723ed54f9e.jpg,UNCERTAIN,normal,nsfw,0.9998310804367065,0.00016887497622519732
161
+ 334,6520255c-f79bc54aa05611d22b65098a11e71a34.jpg,6520255c-f79bc54aa05611d22b65098a11e71a34.jpg,UNCERTAIN,normal,nsfw,0.9873402714729309,0.012659757398068905
162
+ 335,e7695c72-f7761769f7463d55a2df9300c743a324.jpg,e7695c72-f7761769f7463d55a2df9300c743a324.jpg,UNCERTAIN,normal,nsfw,0.996834933757782,0.0031650972086936235
163
+ 336,681dcc99-f77695115dd9a221c2239aece214b49e.jpg,681dcc99-f77695115dd9a221c2239aece214b49e.jpg,UNCERTAIN,normal,nsfw,0.9998076558113098,0.00019238794629927725
164
+ 337,bc15263c-f745fcf48c946c9d4393fd48f4cffc0e.jpg,bc15263c-f745fcf48c946c9d4393fd48f4cffc0e.jpg,SUGGESTIVE,normal,nsfw,0.9814505577087402,0.018549490720033646
165
+ 338,88c7b383-f7303e5b824a66239e6afcd87a8ef62f.jpg,88c7b383-f7303e5b824a66239e6afcd87a8ef62f.jpg,UNCERTAIN,normal,nsfw,0.9998843669891357,0.00011559967970242724
166
+ 339,9342d436-f73693da56901519a595fead5f85d069.jpg,9342d436-f73693da56901519a595fead5f85d069.jpg,SUGGESTIVE,normal,nsfw,0.9998339414596558,0.00016600285016465932
167
+ 340,1bfa81bf-f71e69c1823525251dc2201172364595.jpg,1bfa81bf-f71e69c1823525251dc2201172364595.jpg,SUGGESTIVE,normal,nsfw,0.9996392726898193,0.00036067768814973533
168
+ 341,37fbd106-f72a254e0142a25852937f68f91eeff0.jpg,37fbd106-f72a254e0142a25852937f68f91eeff0.jpg,FAMILY_SAFE,normal,nsfw,0.9998490810394287,0.00015092763351276517
169
+ 342,75ec4326-f6f57e02e3025fda7e6587ada129b5da.jpg,75ec4326-f6f57e02e3025fda7e6587ada129b5da.jpg,UNCERTAIN,normal,nsfw,0.9996010661125183,0.00039896275848150253
170
+ 343,112d370c-f6e10f65554a282ad88561eb6606b673.jpg,112d370c-f6e10f65554a282ad88561eb6606b673.jpg,UNCERTAIN,normal,nsfw,0.9995631575584412,0.0004368835943751037
171
+ 344,258f99f7-f6cc5af7cdd7d677b39960916ddce332.jpg,258f99f7-f6cc5af7cdd7d677b39960916ddce332.jpg,FAMILY_SAFE,normal,nsfw,0.9987820982933044,0.0012178717879578471
172
+ 345,e654ed6f-f6a9eed8dbdaaebe8483cb023b1cc155.jpg,e654ed6f-f6a9eed8dbdaaebe8483cb023b1cc155.jpg,SUGGESTIVE,normal,nsfw,0.9922598004341125,0.007740158587694168
173
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+ "/Users/youniss/Documents/GitHub/haram-police/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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+ "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n",
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+ ],
20
+ "source": [
21
+ "from transformers import pipeline\n",
22
+ "\n",
23
+ "pipe = pipeline(\"image-classification\", model=\"Falconsai/nsfw_image_detection\")"
24
+ ]
25
+ },
26
+ {
27
+ "cell_type": "code",
28
+ "execution_count": 2,
29
+ "id": "cb8aac6f",
30
+ "metadata": {},
31
+ "outputs": [
32
+ {
33
+ "data": {
34
+ "text/plain": [
35
+ "[{'label': 'normal', 'score': 0.9997637867927551},\n",
36
+ " {'label': 'nsfw', 'score': 0.00023620268621016294}]"
37
+ ]
38
+ },
39
+ "execution_count": 2,
40
+ "metadata": {},
41
+ "output_type": "execute_result"
42
+ }
43
+ ],
44
+ "source": [
45
+ "pipe(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png\")\n"
46
+ ]
47
+ },
48
+ {
49
+ "cell_type": "code",
50
+ "execution_count": null,
51
+ "id": "9d21f66c",
52
+ "metadata": {},
53
+ "outputs": [],
54
+ "source": []
55
+ }
56
+ ],
57
+ "metadata": {
58
+ "kernelspec": {
59
+ "display_name": ".venv",
60
+ "language": "python",
61
+ "name": "python3"
62
+ },
63
+ "language_info": {
64
+ "codemirror_mode": {
65
+ "name": "ipython",
66
+ "version": 3
67
+ },
68
+ "file_extension": ".py",
69
+ "mimetype": "text/x-python",
70
+ "name": "python",
71
+ "nbconvert_exporter": "python",
72
+ "pygments_lexer": "ipython3",
73
+ "version": "3.13.5"
74
+ }
75
+ },
76
+ "nbformat": 4,
77
+ "nbformat_minor": 5
78
+ }
notebooks/processing.ipynb ADDED
@@ -0,0 +1,1691 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 53,
6
+ "id": "d3a1f52b",
7
+ "metadata": {},
8
+ "outputs": [
9
+ {
10
+ "data": {
11
+ "text/plain": [
12
+ "True"
13
+ ]
14
+ },
15
+ "execution_count": 53,
16
+ "metadata": {},
17
+ "output_type": "execute_result"
18
+ }
19
+ ],
20
+ "source": [
21
+ "import pandas as pd\n",
22
+ "from dotenv import load_dotenv\n",
23
+ "from pathlib import Path\n",
24
+ "import json\n",
25
+ "\n",
26
+ "load_dotenv()"
27
+ ]
28
+ },
29
+ {
30
+ "cell_type": "code",
31
+ "execution_count": 54,
32
+ "id": "bca20bcc",
33
+ "metadata": {},
34
+ "outputs": [
35
+ {
36
+ "data": {
37
+ "text/html": [
38
+ "<div>\n",
39
+ "<style scoped>\n",
40
+ " .dataframe tbody tr th:only-of-type {\n",
41
+ " vertical-align: middle;\n",
42
+ " }\n",
43
+ "\n",
44
+ " .dataframe tbody tr th {\n",
45
+ " vertical-align: top;\n",
46
+ " }\n",
47
+ "\n",
48
+ " .dataframe thead th {\n",
49
+ " text-align: right;\n",
50
+ " }\n",
51
+ "</style>\n",
52
+ "<table border=\"1\" class=\"dataframe\">\n",
53
+ " <thead>\n",
54
+ " <tr style=\"text-align: right;\">\n",
55
+ " <th></th>\n",
56
+ " <th>ImageId</th>\n",
57
+ " <th>EncodedPixels</th>\n",
58
+ " <th>Height</th>\n",
59
+ " <th>Width</th>\n",
60
+ " <th>ClassId</th>\n",
61
+ " <th>AttributesIds</th>\n",
62
+ " </tr>\n",
63
+ " </thead>\n",
64
+ " <tbody>\n",
65
+ " <tr>\n",
66
+ " <th>0</th>\n",
67
+ " <td>00000663ed1ff0c4e0132b9b9ac53f6e</td>\n",
68
+ " <td>6068157 7 6073371 20 6078584 34 6083797 48 608...</td>\n",
69
+ " <td>5214</td>\n",
70
+ " <td>3676</td>\n",
71
+ " <td>6</td>\n",
72
+ " <td>115,136,143,154,230,295,316,317</td>\n",
73
+ " </tr>\n",
74
+ " <tr>\n",
75
+ " <th>1</th>\n",
76
+ " <td>00000663ed1ff0c4e0132b9b9ac53f6e</td>\n",
77
+ " <td>6323163 11 6328356 32 6333549 53 6338742 75 63...</td>\n",
78
+ " <td>5214</td>\n",
79
+ " <td>3676</td>\n",
80
+ " <td>0</td>\n",
81
+ " <td>115,136,142,146,225,295,316,317</td>\n",
82
+ " </tr>\n",
83
+ " <tr>\n",
84
+ " <th>2</th>\n",
85
+ " <td>00000663ed1ff0c4e0132b9b9ac53f6e</td>\n",
86
+ " <td>8521389 10 8526585 30 8531789 42 8537002 46 85...</td>\n",
87
+ " <td>5214</td>\n",
88
+ " <td>3676</td>\n",
89
+ " <td>28</td>\n",
90
+ " <td>163</td>\n",
91
+ " </tr>\n",
92
+ " <tr>\n",
93
+ " <th>3</th>\n",
94
+ " <td>00000663ed1ff0c4e0132b9b9ac53f6e</td>\n",
95
+ " <td>12903854 2 12909064 7 12914275 10 12919485 15 ...</td>\n",
96
+ " <td>5214</td>\n",
97
+ " <td>3676</td>\n",
98
+ " <td>31</td>\n",
99
+ " <td>160,204</td>\n",
100
+ " </tr>\n",
101
+ " <tr>\n",
102
+ " <th>4</th>\n",
103
+ " <td>00000663ed1ff0c4e0132b9b9ac53f6e</td>\n",
104
+ " <td>10837337 5 10842542 14 10847746 24 10852951 33...</td>\n",
105
+ " <td>5214</td>\n",
106
+ " <td>3676</td>\n",
107
+ " <td>32</td>\n",
108
+ " <td>219</td>\n",
109
+ " </tr>\n",
110
+ " </tbody>\n",
111
+ "</table>\n",
112
+ "</div>"
113
+ ],
114
+ "text/plain": [
115
+ " ImageId \\\n",
116
+ "0 00000663ed1ff0c4e0132b9b9ac53f6e \n",
117
+ "1 00000663ed1ff0c4e0132b9b9ac53f6e \n",
118
+ "2 00000663ed1ff0c4e0132b9b9ac53f6e \n",
119
+ "3 00000663ed1ff0c4e0132b9b9ac53f6e \n",
120
+ "4 00000663ed1ff0c4e0132b9b9ac53f6e \n",
121
+ "\n",
122
+ " EncodedPixels Height Width ClassId \\\n",
123
+ "0 6068157 7 6073371 20 6078584 34 6083797 48 608... 5214 3676 6 \n",
124
+ "1 6323163 11 6328356 32 6333549 53 6338742 75 63... 5214 3676 0 \n",
125
+ "2 8521389 10 8526585 30 8531789 42 8537002 46 85... 5214 3676 28 \n",
126
+ "3 12903854 2 12909064 7 12914275 10 12919485 15 ... 5214 3676 31 \n",
127
+ "4 10837337 5 10842542 14 10847746 24 10852951 33... 5214 3676 32 \n",
128
+ "\n",
129
+ " AttributesIds \n",
130
+ "0 115,136,143,154,230,295,316,317 \n",
131
+ "1 115,136,142,146,225,295,316,317 \n",
132
+ "2 163 \n",
133
+ "3 160,204 \n",
134
+ "4 219 "
135
+ ]
136
+ },
137
+ "execution_count": 54,
138
+ "metadata": {},
139
+ "output_type": "execute_result"
140
+ }
141
+ ],
142
+ "source": [
143
+ "# Get project root (one level up from notebooks/ if running from notebooks directory)\n",
144
+ "current_dir = Path.cwd()\n",
145
+ "PROJECT_ROOT = current_dir.parent if current_dir.name == \"notebooks\" else current_dir\n",
146
+ "DATA_PATH = PROJECT_ROOT / \"data\"\n",
147
+ "\n",
148
+ "fashion_df = pd.read_csv(DATA_PATH / \"train.csv\")\n",
149
+ "fashion_df.head()\n"
150
+ ]
151
+ },
152
+ {
153
+ "cell_type": "code",
154
+ "execution_count": 55,
155
+ "id": "58f4f7b4",
156
+ "metadata": {},
157
+ "outputs": [
158
+ {
159
+ "data": {
160
+ "text/html": [
161
+ "<div>\n",
162
+ "<style scoped>\n",
163
+ " .dataframe tbody tr th:only-of-type {\n",
164
+ " vertical-align: middle;\n",
165
+ " }\n",
166
+ "\n",
167
+ " .dataframe tbody tr th {\n",
168
+ " vertical-align: top;\n",
169
+ " }\n",
170
+ "\n",
171
+ " .dataframe thead th {\n",
172
+ " text-align: right;\n",
173
+ " }\n",
174
+ "</style>\n",
175
+ "<table border=\"1\" class=\"dataframe\">\n",
176
+ " <thead>\n",
177
+ " <tr style=\"text-align: right;\">\n",
178
+ " <th></th>\n",
179
+ " <th>id</th>\n",
180
+ " <th>name</th>\n",
181
+ " <th>supercategory</th>\n",
182
+ " <th>level</th>\n",
183
+ " </tr>\n",
184
+ " </thead>\n",
185
+ " <tbody>\n",
186
+ " <tr>\n",
187
+ " <th>0</th>\n",
188
+ " <td>0</td>\n",
189
+ " <td>shirt, blouse</td>\n",
190
+ " <td>upperbody</td>\n",
191
+ " <td>2</td>\n",
192
+ " </tr>\n",
193
+ " <tr>\n",
194
+ " <th>1</th>\n",
195
+ " <td>1</td>\n",
196
+ " <td>top, t-shirt, sweatshirt</td>\n",
197
+ " <td>upperbody</td>\n",
198
+ " <td>2</td>\n",
199
+ " </tr>\n",
200
+ " <tr>\n",
201
+ " <th>2</th>\n",
202
+ " <td>2</td>\n",
203
+ " <td>sweater</td>\n",
204
+ " <td>upperbody</td>\n",
205
+ " <td>2</td>\n",
206
+ " </tr>\n",
207
+ " <tr>\n",
208
+ " <th>3</th>\n",
209
+ " <td>3</td>\n",
210
+ " <td>cardigan</td>\n",
211
+ " <td>upperbody</td>\n",
212
+ " <td>2</td>\n",
213
+ " </tr>\n",
214
+ " <tr>\n",
215
+ " <th>4</th>\n",
216
+ " <td>4</td>\n",
217
+ " <td>jacket</td>\n",
218
+ " <td>upperbody</td>\n",
219
+ " <td>2</td>\n",
220
+ " </tr>\n",
221
+ " <tr>\n",
222
+ " <th>5</th>\n",
223
+ " <td>5</td>\n",
224
+ " <td>vest</td>\n",
225
+ " <td>upperbody</td>\n",
226
+ " <td>2</td>\n",
227
+ " </tr>\n",
228
+ " <tr>\n",
229
+ " <th>6</th>\n",
230
+ " <td>6</td>\n",
231
+ " <td>pants</td>\n",
232
+ " <td>lowerbody</td>\n",
233
+ " <td>2</td>\n",
234
+ " </tr>\n",
235
+ " <tr>\n",
236
+ " <th>7</th>\n",
237
+ " <td>7</td>\n",
238
+ " <td>shorts</td>\n",
239
+ " <td>lowerbody</td>\n",
240
+ " <td>2</td>\n",
241
+ " </tr>\n",
242
+ " <tr>\n",
243
+ " <th>8</th>\n",
244
+ " <td>8</td>\n",
245
+ " <td>skirt</td>\n",
246
+ " <td>lowerbody</td>\n",
247
+ " <td>2</td>\n",
248
+ " </tr>\n",
249
+ " <tr>\n",
250
+ " <th>9</th>\n",
251
+ " <td>9</td>\n",
252
+ " <td>coat</td>\n",
253
+ " <td>wholebody</td>\n",
254
+ " <td>2</td>\n",
255
+ " </tr>\n",
256
+ " <tr>\n",
257
+ " <th>10</th>\n",
258
+ " <td>10</td>\n",
259
+ " <td>dress</td>\n",
260
+ " <td>wholebody</td>\n",
261
+ " <td>2</td>\n",
262
+ " </tr>\n",
263
+ " <tr>\n",
264
+ " <th>11</th>\n",
265
+ " <td>11</td>\n",
266
+ " <td>jumpsuit</td>\n",
267
+ " <td>wholebody</td>\n",
268
+ " <td>2</td>\n",
269
+ " </tr>\n",
270
+ " <tr>\n",
271
+ " <th>12</th>\n",
272
+ " <td>12</td>\n",
273
+ " <td>cape</td>\n",
274
+ " <td>wholebody</td>\n",
275
+ " <td>2</td>\n",
276
+ " </tr>\n",
277
+ " <tr>\n",
278
+ " <th>13</th>\n",
279
+ " <td>13</td>\n",
280
+ " <td>glasses</td>\n",
281
+ " <td>head</td>\n",
282
+ " <td>2</td>\n",
283
+ " </tr>\n",
284
+ " <tr>\n",
285
+ " <th>14</th>\n",
286
+ " <td>14</td>\n",
287
+ " <td>hat</td>\n",
288
+ " <td>head</td>\n",
289
+ " <td>2</td>\n",
290
+ " </tr>\n",
291
+ " <tr>\n",
292
+ " <th>15</th>\n",
293
+ " <td>15</td>\n",
294
+ " <td>headband, head covering, hair accessory</td>\n",
295
+ " <td>head</td>\n",
296
+ " <td>2</td>\n",
297
+ " </tr>\n",
298
+ " <tr>\n",
299
+ " <th>16</th>\n",
300
+ " <td>16</td>\n",
301
+ " <td>tie</td>\n",
302
+ " <td>neck</td>\n",
303
+ " <td>2</td>\n",
304
+ " </tr>\n",
305
+ " <tr>\n",
306
+ " <th>17</th>\n",
307
+ " <td>17</td>\n",
308
+ " <td>glove</td>\n",
309
+ " <td>arms and hands</td>\n",
310
+ " <td>2</td>\n",
311
+ " </tr>\n",
312
+ " <tr>\n",
313
+ " <th>18</th>\n",
314
+ " <td>18</td>\n",
315
+ " <td>watch</td>\n",
316
+ " <td>arms and hands</td>\n",
317
+ " <td>2</td>\n",
318
+ " </tr>\n",
319
+ " <tr>\n",
320
+ " <th>19</th>\n",
321
+ " <td>19</td>\n",
322
+ " <td>belt</td>\n",
323
+ " <td>waist</td>\n",
324
+ " <td>2</td>\n",
325
+ " </tr>\n",
326
+ " <tr>\n",
327
+ " <th>20</th>\n",
328
+ " <td>20</td>\n",
329
+ " <td>leg warmer</td>\n",
330
+ " <td>legs and feet</td>\n",
331
+ " <td>2</td>\n",
332
+ " </tr>\n",
333
+ " <tr>\n",
334
+ " <th>21</th>\n",
335
+ " <td>21</td>\n",
336
+ " <td>tights, stockings</td>\n",
337
+ " <td>legs and feet</td>\n",
338
+ " <td>2</td>\n",
339
+ " </tr>\n",
340
+ " <tr>\n",
341
+ " <th>22</th>\n",
342
+ " <td>22</td>\n",
343
+ " <td>sock</td>\n",
344
+ " <td>legs and feet</td>\n",
345
+ " <td>2</td>\n",
346
+ " </tr>\n",
347
+ " <tr>\n",
348
+ " <th>23</th>\n",
349
+ " <td>23</td>\n",
350
+ " <td>shoe</td>\n",
351
+ " <td>legs and feet</td>\n",
352
+ " <td>2</td>\n",
353
+ " </tr>\n",
354
+ " <tr>\n",
355
+ " <th>24</th>\n",
356
+ " <td>24</td>\n",
357
+ " <td>bag, wallet</td>\n",
358
+ " <td>others</td>\n",
359
+ " <td>2</td>\n",
360
+ " </tr>\n",
361
+ " <tr>\n",
362
+ " <th>25</th>\n",
363
+ " <td>25</td>\n",
364
+ " <td>scarf</td>\n",
365
+ " <td>others</td>\n",
366
+ " <td>2</td>\n",
367
+ " </tr>\n",
368
+ " <tr>\n",
369
+ " <th>26</th>\n",
370
+ " <td>26</td>\n",
371
+ " <td>umbrella</td>\n",
372
+ " <td>others</td>\n",
373
+ " <td>2</td>\n",
374
+ " </tr>\n",
375
+ " <tr>\n",
376
+ " <th>27</th>\n",
377
+ " <td>27</td>\n",
378
+ " <td>hood</td>\n",
379
+ " <td>garment parts</td>\n",
380
+ " <td>2</td>\n",
381
+ " </tr>\n",
382
+ " <tr>\n",
383
+ " <th>28</th>\n",
384
+ " <td>28</td>\n",
385
+ " <td>collar</td>\n",
386
+ " <td>garment parts</td>\n",
387
+ " <td>2</td>\n",
388
+ " </tr>\n",
389
+ " <tr>\n",
390
+ " <th>29</th>\n",
391
+ " <td>29</td>\n",
392
+ " <td>lapel</td>\n",
393
+ " <td>garment parts</td>\n",
394
+ " <td>2</td>\n",
395
+ " </tr>\n",
396
+ " <tr>\n",
397
+ " <th>30</th>\n",
398
+ " <td>30</td>\n",
399
+ " <td>epaulette</td>\n",
400
+ " <td>garment parts</td>\n",
401
+ " <td>2</td>\n",
402
+ " </tr>\n",
403
+ " <tr>\n",
404
+ " <th>31</th>\n",
405
+ " <td>31</td>\n",
406
+ " <td>sleeve</td>\n",
407
+ " <td>garment parts</td>\n",
408
+ " <td>2</td>\n",
409
+ " </tr>\n",
410
+ " <tr>\n",
411
+ " <th>32</th>\n",
412
+ " <td>32</td>\n",
413
+ " <td>pocket</td>\n",
414
+ " <td>garment parts</td>\n",
415
+ " <td>2</td>\n",
416
+ " </tr>\n",
417
+ " <tr>\n",
418
+ " <th>33</th>\n",
419
+ " <td>33</td>\n",
420
+ " <td>neckline</td>\n",
421
+ " <td>garment parts</td>\n",
422
+ " <td>2</td>\n",
423
+ " </tr>\n",
424
+ " <tr>\n",
425
+ " <th>34</th>\n",
426
+ " <td>34</td>\n",
427
+ " <td>buckle</td>\n",
428
+ " <td>closures</td>\n",
429
+ " <td>2</td>\n",
430
+ " </tr>\n",
431
+ " <tr>\n",
432
+ " <th>35</th>\n",
433
+ " <td>35</td>\n",
434
+ " <td>zipper</td>\n",
435
+ " <td>closures</td>\n",
436
+ " <td>2</td>\n",
437
+ " </tr>\n",
438
+ " <tr>\n",
439
+ " <th>36</th>\n",
440
+ " <td>36</td>\n",
441
+ " <td>applique</td>\n",
442
+ " <td>decorations</td>\n",
443
+ " <td>2</td>\n",
444
+ " </tr>\n",
445
+ " <tr>\n",
446
+ " <th>37</th>\n",
447
+ " <td>37</td>\n",
448
+ " <td>bead</td>\n",
449
+ " <td>decorations</td>\n",
450
+ " <td>2</td>\n",
451
+ " </tr>\n",
452
+ " <tr>\n",
453
+ " <th>38</th>\n",
454
+ " <td>38</td>\n",
455
+ " <td>bow</td>\n",
456
+ " <td>decorations</td>\n",
457
+ " <td>2</td>\n",
458
+ " </tr>\n",
459
+ " <tr>\n",
460
+ " <th>39</th>\n",
461
+ " <td>39</td>\n",
462
+ " <td>flower</td>\n",
463
+ " <td>decorations</td>\n",
464
+ " <td>2</td>\n",
465
+ " </tr>\n",
466
+ " <tr>\n",
467
+ " <th>40</th>\n",
468
+ " <td>40</td>\n",
469
+ " <td>fringe</td>\n",
470
+ " <td>decorations</td>\n",
471
+ " <td>2</td>\n",
472
+ " </tr>\n",
473
+ " <tr>\n",
474
+ " <th>41</th>\n",
475
+ " <td>41</td>\n",
476
+ " <td>ribbon</td>\n",
477
+ " <td>decorations</td>\n",
478
+ " <td>2</td>\n",
479
+ " </tr>\n",
480
+ " <tr>\n",
481
+ " <th>42</th>\n",
482
+ " <td>42</td>\n",
483
+ " <td>rivet</td>\n",
484
+ " <td>decorations</td>\n",
485
+ " <td>2</td>\n",
486
+ " </tr>\n",
487
+ " <tr>\n",
488
+ " <th>43</th>\n",
489
+ " <td>43</td>\n",
490
+ " <td>ruffle</td>\n",
491
+ " <td>decorations</td>\n",
492
+ " <td>2</td>\n",
493
+ " </tr>\n",
494
+ " <tr>\n",
495
+ " <th>44</th>\n",
496
+ " <td>44</td>\n",
497
+ " <td>sequin</td>\n",
498
+ " <td>decorations</td>\n",
499
+ " <td>2</td>\n",
500
+ " </tr>\n",
501
+ " <tr>\n",
502
+ " <th>45</th>\n",
503
+ " <td>45</td>\n",
504
+ " <td>tassel</td>\n",
505
+ " <td>decorations</td>\n",
506
+ " <td>2</td>\n",
507
+ " </tr>\n",
508
+ " </tbody>\n",
509
+ "</table>\n",
510
+ "</div>"
511
+ ],
512
+ "text/plain": [
513
+ " id name supercategory level\n",
514
+ "0 0 shirt, blouse upperbody 2\n",
515
+ "1 1 top, t-shirt, sweatshirt upperbody 2\n",
516
+ "2 2 sweater upperbody 2\n",
517
+ "3 3 cardigan upperbody 2\n",
518
+ "4 4 jacket upperbody 2\n",
519
+ "5 5 vest upperbody 2\n",
520
+ "6 6 pants lowerbody 2\n",
521
+ "7 7 shorts lowerbody 2\n",
522
+ "8 8 skirt lowerbody 2\n",
523
+ "9 9 coat wholebody 2\n",
524
+ "10 10 dress wholebody 2\n",
525
+ "11 11 jumpsuit wholebody 2\n",
526
+ "12 12 cape wholebody 2\n",
527
+ "13 13 glasses head 2\n",
528
+ "14 14 hat head 2\n",
529
+ "15 15 headband, head covering, hair accessory head 2\n",
530
+ "16 16 tie neck 2\n",
531
+ "17 17 glove arms and hands 2\n",
532
+ "18 18 watch arms and hands 2\n",
533
+ "19 19 belt waist 2\n",
534
+ "20 20 leg warmer legs and feet 2\n",
535
+ "21 21 tights, stockings legs and feet 2\n",
536
+ "22 22 sock legs and feet 2\n",
537
+ "23 23 shoe legs and feet 2\n",
538
+ "24 24 bag, wallet others 2\n",
539
+ "25 25 scarf others 2\n",
540
+ "26 26 umbrella others 2\n",
541
+ "27 27 hood garment parts 2\n",
542
+ "28 28 collar garment parts 2\n",
543
+ "29 29 lapel garment parts 2\n",
544
+ "30 30 epaulette garment parts 2\n",
545
+ "31 31 sleeve garment parts 2\n",
546
+ "32 32 pocket garment parts 2\n",
547
+ "33 33 neckline garment parts 2\n",
548
+ "34 34 buckle closures 2\n",
549
+ "35 35 zipper closures 2\n",
550
+ "36 36 applique decorations 2\n",
551
+ "37 37 bead decorations 2\n",
552
+ "38 38 bow decorations 2\n",
553
+ "39 39 flower decorations 2\n",
554
+ "40 40 fringe decorations 2\n",
555
+ "41 41 ribbon decorations 2\n",
556
+ "42 42 rivet decorations 2\n",
557
+ "43 43 ruffle decorations 2\n",
558
+ "44 44 sequin decorations 2\n",
559
+ "45 45 tassel decorations 2"
560
+ ]
561
+ },
562
+ "execution_count": 55,
563
+ "metadata": {},
564
+ "output_type": "execute_result"
565
+ }
566
+ ],
567
+ "source": [
568
+ "label_descriptions = json.load(open(DATA_PATH / \"label_descriptions.json\"))\n",
569
+ "\n",
570
+ "categories_df = pd.DataFrame(label_descriptions[\"categories\"])\n",
571
+ "categories_df"
572
+ ]
573
+ },
574
+ {
575
+ "cell_type": "code",
576
+ "execution_count": 56,
577
+ "id": "48d7ab2b",
578
+ "metadata": {},
579
+ "outputs": [
580
+ {
581
+ "data": {
582
+ "text/html": [
583
+ "<div>\n",
584
+ "<style scoped>\n",
585
+ " .dataframe tbody tr th:only-of-type {\n",
586
+ " vertical-align: middle;\n",
587
+ " }\n",
588
+ "\n",
589
+ " .dataframe tbody tr th {\n",
590
+ " vertical-align: top;\n",
591
+ " }\n",
592
+ "\n",
593
+ " .dataframe thead th {\n",
594
+ " text-align: right;\n",
595
+ " }\n",
596
+ "</style>\n",
597
+ "<table border=\"1\" class=\"dataframe\">\n",
598
+ " <thead>\n",
599
+ " <tr style=\"text-align: right;\">\n",
600
+ " <th></th>\n",
601
+ " <th>id</th>\n",
602
+ " <th>name</th>\n",
603
+ " <th>supercategory</th>\n",
604
+ " <th>level</th>\n",
605
+ " </tr>\n",
606
+ " </thead>\n",
607
+ " <tbody>\n",
608
+ " <tr>\n",
609
+ " <th>0</th>\n",
610
+ " <td>0</td>\n",
611
+ " <td>classic (t-shirt)</td>\n",
612
+ " <td>nickname</td>\n",
613
+ " <td>1</td>\n",
614
+ " </tr>\n",
615
+ " <tr>\n",
616
+ " <th>1</th>\n",
617
+ " <td>1</td>\n",
618
+ " <td>polo (shirt)</td>\n",
619
+ " <td>nickname</td>\n",
620
+ " <td>1</td>\n",
621
+ " </tr>\n",
622
+ " <tr>\n",
623
+ " <th>2</th>\n",
624
+ " <td>2</td>\n",
625
+ " <td>undershirt</td>\n",
626
+ " <td>nickname</td>\n",
627
+ " <td>1</td>\n",
628
+ " </tr>\n",
629
+ " <tr>\n",
630
+ " <th>3</th>\n",
631
+ " <td>3</td>\n",
632
+ " <td>henley (shirt)</td>\n",
633
+ " <td>nickname</td>\n",
634
+ " <td>1</td>\n",
635
+ " </tr>\n",
636
+ " <tr>\n",
637
+ " <th>4</th>\n",
638
+ " <td>4</td>\n",
639
+ " <td>ringer (t-shirt)</td>\n",
640
+ " <td>nickname</td>\n",
641
+ " <td>1</td>\n",
642
+ " </tr>\n",
643
+ " <tr>\n",
644
+ " <th>...</th>\n",
645
+ " <td>...</td>\n",
646
+ " <td>...</td>\n",
647
+ " <td>...</td>\n",
648
+ " <td>...</td>\n",
649
+ " </tr>\n",
650
+ " <tr>\n",
651
+ " <th>289</th>\n",
652
+ " <td>336</td>\n",
653
+ " <td>peacock</td>\n",
654
+ " <td>animal</td>\n",
655
+ " <td>2</td>\n",
656
+ " </tr>\n",
657
+ " <tr>\n",
658
+ " <th>290</th>\n",
659
+ " <td>337</td>\n",
660
+ " <td>zebra</td>\n",
661
+ " <td>animal</td>\n",
662
+ " <td>2</td>\n",
663
+ " </tr>\n",
664
+ " <tr>\n",
665
+ " <th>291</th>\n",
666
+ " <td>338</td>\n",
667
+ " <td>giraffe</td>\n",
668
+ " <td>animal</td>\n",
669
+ " <td>2</td>\n",
670
+ " </tr>\n",
671
+ " <tr>\n",
672
+ " <th>292</th>\n",
673
+ " <td>339</td>\n",
674
+ " <td>toile de jouy</td>\n",
675
+ " <td>textile pattern</td>\n",
676
+ " <td>1</td>\n",
677
+ " </tr>\n",
678
+ " <tr>\n",
679
+ " <th>293</th>\n",
680
+ " <td>340</td>\n",
681
+ " <td>plant</td>\n",
682
+ " <td>textile pattern</td>\n",
683
+ " <td>1</td>\n",
684
+ " </tr>\n",
685
+ " </tbody>\n",
686
+ "</table>\n",
687
+ "<p>294 rows × 4 columns</p>\n",
688
+ "</div>"
689
+ ],
690
+ "text/plain": [
691
+ " id name supercategory level\n",
692
+ "0 0 classic (t-shirt) nickname 1\n",
693
+ "1 1 polo (shirt) nickname 1\n",
694
+ "2 2 undershirt nickname 1\n",
695
+ "3 3 henley (shirt) nickname 1\n",
696
+ "4 4 ringer (t-shirt) nickname 1\n",
697
+ ".. ... ... ... ...\n",
698
+ "289 336 peacock animal 2\n",
699
+ "290 337 zebra animal 2\n",
700
+ "291 338 giraffe animal 2\n",
701
+ "292 339 toile de jouy textile pattern 1\n",
702
+ "293 340 plant textile pattern 1\n",
703
+ "\n",
704
+ "[294 rows x 4 columns]"
705
+ ]
706
+ },
707
+ "execution_count": 56,
708
+ "metadata": {},
709
+ "output_type": "execute_result"
710
+ }
711
+ ],
712
+ "source": [
713
+ "attributes_df = pd.DataFrame(label_descriptions[\"attributes\"])\n",
714
+ "attributes_df\n"
715
+ ]
716
+ },
717
+ {
718
+ "cell_type": "code",
719
+ "execution_count": 57,
720
+ "id": "b178ce00",
721
+ "metadata": {},
722
+ "outputs": [
723
+ {
724
+ "name": "stdout",
725
+ "output_type": "stream",
726
+ "text": [
727
+ "Found 0 potentially SUGGESTIVE categories\n",
728
+ "Found 4 potentially SUGGESTIVE attributes\n",
729
+ "\n",
730
+ "============================================================\n",
731
+ "POTENTIALLY SUGGESTIVE CATEGORIES:\n",
732
+ "============================================================\n",
733
+ "Empty DataFrame\n",
734
+ "Columns: [id, name, supercategory]\n",
735
+ "Index: []\n",
736
+ "\n",
737
+ "============================================================\n",
738
+ "POTENTIALLY SUGGESTIVE ATTRIBUTES:\n",
739
+ "============================================================\n",
740
+ " id name supercategory\n",
741
+ " 51 booty (shorts) nickname\n",
742
+ "106 bodycon (dress) nickname\n",
743
+ "148 micro (length) length\n",
744
+ "192 plunging (neckline) neckline type\n"
745
+ ]
746
+ }
747
+ ],
748
+ "source": [
749
+ "# ULTRA-RESTRICTIVE criteria for UNCERTAIN SUGGESTIVE content\n",
750
+ "# Only items that are clearly and unambiguously SUGGESTIVE/revealing\n",
751
+ "# Removed: crop tops, halter tops, tube tops, mini length, tight fit, etc. - too many false positives\n",
752
+ "\n",
753
+ "# Only the most obviously SUGGESTIVE items\n",
754
+ "revealing_keywords = {\n",
755
+ " 'categories': [\n",
756
+ " # Only inherently revealing categories - removed most as they had false positives\n",
757
+ " # 'booty', # removed - checking attribute instead\n",
758
+ " # 'bodycon', # removed - checking attribute instead\n",
759
+ " ],\n",
760
+ " 'attributes': [\n",
761
+ " # ONLY the most clearly SUGGESTIVE items\n",
762
+ " 'booty (shorts)', # very specific and clearly SUGGESTIVE\n",
763
+ " 'bodycon (dress)', # form-fitting, often revealing\n",
764
+ " # Removed: crop (top), halter (top), tube (top), camisole, slip (dress) - too many false positives\n",
765
+ " ]\n",
766
+ "}\n",
767
+ "\n",
768
+ "# Only the most revealing patterns\n",
769
+ "revealing_patterns = {\n",
770
+ " 'length': [\n",
771
+ " # Removed: mini (length) - too broad, many modest mini skirts\n",
772
+ " 'micro (length)', # only very short - but still might have false positives\n",
773
+ " ],\n",
774
+ " 'neckline type': [\n",
775
+ " 'plunging (neckline)', # only the most revealing neckline\n",
776
+ " # Removed: off-the-shoulder, one shoulder - can be modest\n",
777
+ " ],\n",
778
+ " 'silhouette': [\n",
779
+ " # Removed: tight (fit) - way too broad, many normal clothes are tight\n",
780
+ " ],\n",
781
+ " 'nickname': [\n",
782
+ " # Only the most clearly SUGGESTIVE\n",
783
+ " 'booty (shorts)',\n",
784
+ " 'bodycon (dress)',\n",
785
+ " ]\n",
786
+ "}\n",
787
+ "\n",
788
+ "def is_potentially_SUGGESTIVE(name: str, supercategory: str = None) -> bool:\n",
789
+ " \"\"\"Check if a category or attribute name suggests potentially SUGGESTIVE content.\n",
790
+ " ULTRA-RESTRICTIVE: Only matches clearly SUGGESTIVE items to avoid false positives.\"\"\"\n",
791
+ " name_lower = name.lower()\n",
792
+ " \n",
793
+ " # Special case: check for \"booty\" in any context (shorts) - very specific\n",
794
+ " if 'booty' in name_lower:\n",
795
+ " return True\n",
796
+ " \n",
797
+ " # Check for revealing patterns by supercategory - very restrictive\n",
798
+ " if supercategory:\n",
799
+ " if supercategory == 'length':\n",
800
+ " # Only micro length (very short) - removed mini as too broad\n",
801
+ " if 'micro (length)' in name_lower:\n",
802
+ " return True\n",
803
+ " elif supercategory == 'neckline type':\n",
804
+ " # Only plunging neckline - most revealing\n",
805
+ " if 'plunging (neckline)' in name_lower:\n",
806
+ " return True\n",
807
+ " elif supercategory == 'nickname':\n",
808
+ " # Only the most clearly SUGGESTIVE styles\n",
809
+ " for pattern in revealing_patterns['nickname']:\n",
810
+ " if pattern.lower() in name_lower:\n",
811
+ " return True\n",
812
+ " \n",
813
+ " # Check categories for inherently revealing items\n",
814
+ " for keyword in revealing_keywords['categories']:\n",
815
+ " if keyword.lower() in name_lower:\n",
816
+ " return True\n",
817
+ " \n",
818
+ " # Check attributes for inherently revealing items\n",
819
+ " for keyword in revealing_keywords['attributes']:\n",
820
+ " if keyword.lower() in name_lower:\n",
821
+ " return True\n",
822
+ " \n",
823
+ " return False\n",
824
+ "\n",
825
+ "# Filter categories\n",
826
+ "SUGGESTIVE_categories = categories_df[\n",
827
+ " categories_df['name'].apply(lambda x: is_potentially_SUGGESTIVE(x))\n",
828
+ "].copy()\n",
829
+ "\n",
830
+ "# Filter attributes\n",
831
+ "SUGGESTIVE_attributes = attributes_df[\n",
832
+ " attributes_df.apply(lambda row: is_potentially_SUGGESTIVE(row['name'], row['supercategory']), axis=1)\n",
833
+ "].copy()\n",
834
+ "\n",
835
+ "print(f\"Found {len(SUGGESTIVE_categories)} potentially SUGGESTIVE categories\")\n",
836
+ "print(f\"Found {len(SUGGESTIVE_attributes)} potentially SUGGESTIVE attributes\")\n",
837
+ "print(\"\\n\" + \"=\"*60)\n",
838
+ "print(\"POTENTIALLY SUGGESTIVE CATEGORIES:\")\n",
839
+ "print(\"=\"*60)\n",
840
+ "print(SUGGESTIVE_categories[['id', 'name', 'supercategory']].to_string(index=False))\n",
841
+ "print(\"\\n\" + \"=\"*60)\n",
842
+ "print(\"POTENTIALLY SUGGESTIVE ATTRIBUTES:\")\n",
843
+ "print(\"=\"*60)\n",
844
+ "print(SUGGESTIVE_attributes[['id', 'name', 'supercategory']].to_string(index=False))\n"
845
+ ]
846
+ },
847
+ {
848
+ "cell_type": "code",
849
+ "execution_count": 58,
850
+ "id": "7ff15a1c",
851
+ "metadata": {},
852
+ "outputs": [
853
+ {
854
+ "name": "stdout",
855
+ "output_type": "stream",
856
+ "text": [
857
+ "============================================================\n",
858
+ "BREAKDOWN BY SUPERCATEGORY:\n",
859
+ "============================================================\n",
860
+ "\n",
861
+ "Attributes by supercategory:\n",
862
+ "supercategory\n",
863
+ "nickname 2\n",
864
+ "length 1\n",
865
+ "neckline type 1\n",
866
+ "dtype: int64\n",
867
+ "\n",
868
+ "Detailed attribute breakdown:\n",
869
+ "\n",
870
+ "nickname:\n",
871
+ " - booty (shorts) (id: 51)\n",
872
+ " - bodycon (dress) (id: 106)\n",
873
+ "\n",
874
+ "length:\n",
875
+ " - micro (length) (id: 148)\n",
876
+ "\n",
877
+ "neckline type:\n",
878
+ " - plunging (neckline) (id: 192)\n",
879
+ "\n",
880
+ "============================================================\n",
881
+ "SUMMARY DATAFRAME (for export):\n",
882
+ "============================================================\n",
883
+ " type id name supercategory\n",
884
+ "0 attribute 51 booty (shorts) nickname\n",
885
+ "1 attribute 106 bodycon (dress) nickname\n",
886
+ "2 attribute 148 micro (length) length\n",
887
+ "3 attribute 192 plunging (neckline) neckline type\n"
888
+ ]
889
+ }
890
+ ],
891
+ "source": [
892
+ "# Create a detailed breakdown by supercategory\n",
893
+ "print(\"=\"*60)\n",
894
+ "print(\"BREAKDOWN BY SUPERCATEGORY:\")\n",
895
+ "print(\"=\"*60)\n",
896
+ "\n",
897
+ "if len(SUGGESTIVE_attributes) > 0:\n",
898
+ " print(\"\\nAttributes by supercategory:\")\n",
899
+ " print(SUGGESTIVE_attributes.groupby('supercategory').size().sort_values(ascending=False))\n",
900
+ " \n",
901
+ " print(\"\\nDetailed attribute breakdown:\")\n",
902
+ " for supercat in SUGGESTIVE_attributes['supercategory'].unique():\n",
903
+ " print(f\"\\n{supercat}:\")\n",
904
+ " subset = SUGGESTIVE_attributes[SUGGESTIVE_attributes['supercategory'] == supercat]\n",
905
+ " for _, row in subset.iterrows():\n",
906
+ " print(f\" - {row['name']} (id: {row['id']})\")\n",
907
+ "\n",
908
+ "# Create summary DataFrames for export\n",
909
+ "SUGGESTIVE_summary = {\n",
910
+ " 'type': ['category'] * len(SUGGESTIVE_categories) + ['attribute'] * len(SUGGESTIVE_attributes),\n",
911
+ " 'id': list(SUGGESTIVE_categories['id']) + list(SUGGESTIVE_attributes['id']),\n",
912
+ " 'name': list(SUGGESTIVE_categories['name']) + list(SUGGESTIVE_attributes['name']),\n",
913
+ " 'supercategory': list(SUGGESTIVE_categories['supercategory']) + list(SUGGESTIVE_attributes['supercategory'])\n",
914
+ "}\n",
915
+ "\n",
916
+ "SUGGESTIVE_summary_df = pd.DataFrame(SUGGESTIVE_summary)\n",
917
+ "print(\"\\n\" + \"=\"*60)\n",
918
+ "print(\"SUMMARY DATAFRAME (for export):\")\n",
919
+ "print(\"=\"*60)\n",
920
+ "print(SUGGESTIVE_summary_df)\n",
921
+ "\n",
922
+ "# Optionally save to CSV\n",
923
+ "# SUGGESTIVE_summary_df.to_csv(DATA_PATH / \"SUGGESTIVE_labels.csv\", index=False)\n"
924
+ ]
925
+ },
926
+ {
927
+ "cell_type": "code",
928
+ "execution_count": 59,
929
+ "id": "a46d616c",
930
+ "metadata": {},
931
+ "outputs": [
932
+ {
933
+ "name": "stdout",
934
+ "output_type": "stream",
935
+ "text": [
936
+ "SUGGESTIVE category IDs: set()\n",
937
+ "SUGGESTIVE attribute IDs: {192, 106, 51, 148}\n",
938
+ "\n",
939
+ "Total unique SUGGESTIVE category IDs: 0\n",
940
+ "Total unique SUGGESTIVE attribute IDs: 4\n",
941
+ "\n",
942
+ "============================================================\n",
943
+ "FILTERING RESULTS:\n",
944
+ "============================================================\n",
945
+ "Total images in fashion_df: 333401\n",
946
+ "Images with SUGGESTIVE content: 5218\n",
947
+ "Percentage: 1.57%\n",
948
+ "\n",
949
+ "Breakdown:\n",
950
+ " - Matched by category only: 0\n",
951
+ " - Matched by attribute only: 5218\n",
952
+ " - Matched by both: 0\n",
953
+ "\n",
954
+ "============================================================\n",
955
+ "SAMPLE OF SUGGESTIVE IMAGES (first 10 rows):\n",
956
+ "============================================================\n",
957
+ " ImageId ClassId \\\n",
958
+ "49 000b3a87508b0fa185fbd53ecbe2e4c6 33 \n",
959
+ "147 001a66b16b12f12dc45e2bba40e04683 10 \n",
960
+ "180 00211c06b1fe730097dde122cd4d3f8c 7 \n",
961
+ "304 003ae3da258f7ba7267af5f159dd3502 10 \n",
962
+ "369 0048f6c47de85cc4dc263912bd0ff6f5 33 \n",
963
+ "372 0048f6c47de85cc4dc263912bd0ff6f5 7 \n",
964
+ "445 005380bd939eb68085af3f804d387824 10 \n",
965
+ "456 0054564ae183ad9a1b152eef0bc11e1d 10 \n",
966
+ "465 0055347a114b215f8f469fec9e38c272 10 \n",
967
+ "526 005e9b75edcee7d655c390ea5416641d 33 \n",
968
+ "\n",
969
+ " AttributesIds \n",
970
+ "49 192 \n",
971
+ "147 106,115,127,142,149,229,295,316 \n",
972
+ "180 50,115,136,142,148,230,295,300,317 \n",
973
+ "304 106,127,141,150,295,316,317 \n",
974
+ "369 192 \n",
975
+ "372 50,115,136,142,148,317 \n",
976
+ "445 106,114,127,142,150,229,295,311,317 \n",
977
+ "456 106,115,127,142,149,229,295,316,317 \n",
978
+ "465 106,115,127,142,149,229,295,316,317 \n",
979
+ "526 192 \n"
980
+ ]
981
+ }
982
+ ],
983
+ "source": [
984
+ "# Get IDs of SUGGESTIVE categories and attributes\n",
985
+ "SUGGESTIVE_category_ids = set(SUGGESTIVE_categories['id'].tolist())\n",
986
+ "SUGGESTIVE_attribute_ids = set(SUGGESTIVE_attributes['id'].tolist())\n",
987
+ "\n",
988
+ "print(f\"SUGGESTIVE category IDs: {SUGGESTIVE_category_ids}\")\n",
989
+ "print(f\"SUGGESTIVE attribute IDs: {SUGGESTIVE_attribute_ids}\")\n",
990
+ "print(f\"\\nTotal unique SUGGESTIVE category IDs: {len(SUGGESTIVE_category_ids)}\")\n",
991
+ "print(f\"Total unique SUGGESTIVE attribute IDs: {len(SUGGESTIVE_attribute_ids)}\")\n",
992
+ "\n",
993
+ "# Function to check if AttributesIds string contains any SUGGESTIVE attribute ID\n",
994
+ "def has_SUGGESTIVE_attribute(attributes_str: str) -> bool:\n",
995
+ " \"\"\"Check if the comma-separated attributes string contains any SUGGESTIVE attribute ID.\"\"\"\n",
996
+ " if pd.isna(attributes_str) or attributes_str == '':\n",
997
+ " return False\n",
998
+ " # Parse comma-separated string and convert to integers\n",
999
+ " try:\n",
1000
+ " attr_ids = [int(x.strip()) for x in str(attributes_str).split(',')]\n",
1001
+ " return bool(SUGGESTIVE_attribute_ids.intersection(set(attr_ids)))\n",
1002
+ " except (ValueError, AttributeError):\n",
1003
+ " return False\n",
1004
+ "\n",
1005
+ "# Filter fashion_df for SUGGESTIVE images\n",
1006
+ "# An image is SUGGESTIVE if:\n",
1007
+ "# 1. Its ClassId matches a SUGGESTIVE category, OR\n",
1008
+ "# 2. Its AttributesIds contains any SUGGESTIVE attribute ID\n",
1009
+ "\n",
1010
+ "SUGGESTIVE_mask = (\n",
1011
+ " fashion_df['ClassId'].isin(SUGGESTIVE_category_ids) |\n",
1012
+ " fashion_df['AttributesIds'].apply(has_SUGGESTIVE_attribute)\n",
1013
+ ")\n",
1014
+ "\n",
1015
+ "SUGGESTIVE_fashion_df = fashion_df[SUGGESTIVE_mask].copy()\n",
1016
+ "\n",
1017
+ "print(\"\\n\" + \"=\"*60)\n",
1018
+ "print(\"FILTERING RESULTS:\")\n",
1019
+ "print(\"=\"*60)\n",
1020
+ "print(f\"Total images in fashion_df: {len(fashion_df)}\")\n",
1021
+ "print(f\"Images with SUGGESTIVE content: {len(SUGGESTIVE_fashion_df)}\")\n",
1022
+ "print(f\"Percentage: {len(SUGGESTIVE_fashion_df) / len(fashion_df) * 100:.2f}%\")\n",
1023
+ "\n",
1024
+ "# Show breakdown by type of match\n",
1025
+ "category_matches = fashion_df['ClassId'].isin(SUGGESTIVE_category_ids).sum()\n",
1026
+ "attribute_matches = fashion_df['AttributesIds'].apply(has_SUGGESTIVE_attribute).sum()\n",
1027
+ "both_matches = ((fashion_df['ClassId'].isin(SUGGESTIVE_category_ids)) & \n",
1028
+ " (fashion_df['AttributesIds'].apply(has_SUGGESTIVE_attribute))).sum()\n",
1029
+ "\n",
1030
+ "print(f\"\\nBreakdown:\")\n",
1031
+ "print(f\" - Matched by category only: {category_matches - both_matches}\")\n",
1032
+ "print(f\" - Matched by attribute only: {attribute_matches - both_matches}\")\n",
1033
+ "print(f\" - Matched by both: {both_matches}\")\n",
1034
+ "\n",
1035
+ "# Show sample of SUGGESTIVE images\n",
1036
+ "print(\"\\n\" + \"=\"*60)\n",
1037
+ "print(\"SAMPLE OF SUGGESTIVE IMAGES (first 10 rows):\")\n",
1038
+ "print(\"=\"*60)\n",
1039
+ "print(SUGGESTIVE_fashion_df[['ImageId', 'ClassId', 'AttributesIds']].head(10))\n"
1040
+ ]
1041
+ },
1042
+ {
1043
+ "cell_type": "code",
1044
+ "execution_count": 60,
1045
+ "id": "0e1b67fa",
1046
+ "metadata": {},
1047
+ "outputs": [
1048
+ {
1049
+ "name": "stdout",
1050
+ "output_type": "stream",
1051
+ "text": [
1052
+ "============================================================\n",
1053
+ "DIAGNOSTIC: BREAKDOWN OF MATCHES\n",
1054
+ "============================================================\n",
1055
+ "\n",
1056
+ "1. Matches by Category (ClassId):\n",
1057
+ " No category matches\n",
1058
+ "\n",
1059
+ "2. Matches by Attribute (AttributesIds):\n",
1060
+ " - micro (length) (id: 148): 3190 matches\n",
1061
+ " - bodycon (dress) (id: 106): 1144 matches\n",
1062
+ " - plunging (neckline) (id: 192): 966 matches\n",
1063
+ " - booty (shorts) (id: 51): 20 matches\n",
1064
+ "\n",
1065
+ "3. Sample rows with their matched attributes/categories:\n",
1066
+ " (First 5 rows showing ImageId, ClassId, and matched attributes)\n",
1067
+ "\n",
1068
+ " ImageId: 000b3a87508b0fa185fbd53ecbe2e4c6\n",
1069
+ " ClassId: 33 -> neckline\n",
1070
+ " Matched Attributes: ['plunging (neckline)']\n",
1071
+ "\n",
1072
+ " ImageId: 001a66b16b12f12dc45e2bba40e04683\n",
1073
+ " ClassId: 10 -> dress\n",
1074
+ " Matched Attributes: ['bodycon (dress)']\n",
1075
+ "\n",
1076
+ " ImageId: 00211c06b1fe730097dde122cd4d3f8c\n",
1077
+ " ClassId: 7 -> shorts\n",
1078
+ " Matched Attributes: ['micro (length)']\n",
1079
+ "\n",
1080
+ " ImageId: 003ae3da258f7ba7267af5f159dd3502\n",
1081
+ " ClassId: 10 -> dress\n",
1082
+ " Matched Attributes: ['bodycon (dress)']\n",
1083
+ "\n",
1084
+ " ImageId: 0048f6c47de85cc4dc263912bd0ff6f5\n",
1085
+ " ClassId: 33 -> neckline\n",
1086
+ " Matched Attributes: ['plunging (neckline)']\n"
1087
+ ]
1088
+ }
1089
+ ],
1090
+ "source": [
1091
+ "# DIAGNOSTIC: Show what's actually being matched\n",
1092
+ "# This helps identify which attributes/categories are causing matches\n",
1093
+ "\n",
1094
+ "print(\"=\"*60)\n",
1095
+ "print(\"DIAGNOSTIC: BREAKDOWN OF MATCHES\")\n",
1096
+ "print(\"=\"*60)\n",
1097
+ "\n",
1098
+ "# Create a mapping of attribute IDs to names\n",
1099
+ "attr_id_to_name = dict(zip(attributes_df['id'], attributes_df['name']))\n",
1100
+ "cat_id_to_name = dict(zip(categories_df['id'], categories_df['name']))\n",
1101
+ "\n",
1102
+ "# Analyze what's matching in the SUGGESTIVE_fashion_df\n",
1103
+ "print(\"\\n1. Matches by Category (ClassId):\")\n",
1104
+ "category_matches = SUGGESTIVE_fashion_df[SUGGESTIVE_fashion_df['ClassId'].isin(SUGGESTIVE_category_ids)]\n",
1105
+ "if len(category_matches) > 0:\n",
1106
+ " cat_counts = category_matches['ClassId'].value_counts()\n",
1107
+ " for cat_id, count in cat_counts.items():\n",
1108
+ " cat_name = cat_id_to_name.get(cat_id, f\"Unknown (id: {cat_id})\")\n",
1109
+ " print(f\" - {cat_name} (id: {cat_id}): {count} matches\")\n",
1110
+ "else:\n",
1111
+ " print(\" No category matches\")\n",
1112
+ "\n",
1113
+ "print(\"\\n2. Matches by Attribute (AttributesIds):\")\n",
1114
+ "# Find which attributes are matching\n",
1115
+ "matching_attributes = {}\n",
1116
+ "for idx, row in SUGGESTIVE_fashion_df.iterrows():\n",
1117
+ " if pd.notna(row['AttributesIds']) and row['AttributesIds'] != '':\n",
1118
+ " try:\n",
1119
+ " attr_ids = [int(x.strip()) for x in str(row['AttributesIds']).split(',')]\n",
1120
+ " matching_attr_ids = SUGGESTIVE_attribute_ids.intersection(set(attr_ids))\n",
1121
+ " for attr_id in matching_attr_ids:\n",
1122
+ " matching_attributes[attr_id] = matching_attributes.get(attr_id, 0) + 1\n",
1123
+ " except:\n",
1124
+ " pass\n",
1125
+ "\n",
1126
+ "if matching_attributes:\n",
1127
+ " for attr_id, count in sorted(matching_attributes.items(), key=lambda x: x[1], reverse=True):\n",
1128
+ " attr_name = attr_id_to_name.get(attr_id, f\"Unknown (id: {attr_id})\")\n",
1129
+ " print(f\" - {attr_name} (id: {attr_id}): {count} matches\")\n",
1130
+ "else:\n",
1131
+ " print(\" No attribute matches\")\n",
1132
+ "\n",
1133
+ "print(\"\\n3. Sample rows with their matched attributes/categories:\")\n",
1134
+ "print(\" (First 5 rows showing ImageId, ClassId, and matched attributes)\")\n",
1135
+ "for idx, row in SUGGESTIVE_fashion_df.head(5).iterrows():\n",
1136
+ " print(f\"\\n ImageId: {row['ImageId']}\")\n",
1137
+ " print(f\" ClassId: {row['ClassId']} -> {cat_id_to_name.get(row['ClassId'], 'Unknown')}\")\n",
1138
+ " if pd.notna(row['AttributesIds']) and row['AttributesIds'] != '':\n",
1139
+ " try:\n",
1140
+ " attr_ids = [int(x.strip()) for x in str(row['AttributesIds']).split(',')]\n",
1141
+ " matching_attr_ids = SUGGESTIVE_attribute_ids.intersection(set(attr_ids))\n",
1142
+ " if matching_attr_ids:\n",
1143
+ " print(f\" Matched Attributes: {[attr_id_to_name.get(aid, f'id:{aid}') for aid in matching_attr_ids]}\")\n",
1144
+ " except:\n",
1145
+ " pass\n"
1146
+ ]
1147
+ },
1148
+ {
1149
+ "cell_type": "code",
1150
+ "execution_count": 61,
1151
+ "id": "aaaf424a",
1152
+ "metadata": {},
1153
+ "outputs": [
1154
+ {
1155
+ "name": "stdout",
1156
+ "output_type": "stream",
1157
+ "text": [
1158
+ "============================================================\n",
1159
+ "UNIQUE IMAGE ANALYSIS:\n",
1160
+ "============================================================\n",
1161
+ "Total unique images in dataset: 45623\n",
1162
+ "Unique images with SUGGESTIVE content: 5079\n",
1163
+ "Percentage of unique images: 11.13%\n",
1164
+ "\n",
1165
+ "Average annotations per SUGGESTIVE image: 1.03\n",
1166
+ "Max annotations for a single image: 4\n",
1167
+ "Min annotations for a single image: 1\n",
1168
+ "\n",
1169
+ "============================================================\n",
1170
+ "DISTRIBUTION OF SUGGESTIVE CATEGORIES IN FILTERED DATA:\n",
1171
+ "============================================================\n",
1172
+ "ClassId\n",
1173
+ "10 1502\n",
1174
+ "33 965\n",
1175
+ "7 857\n",
1176
+ "4 684\n",
1177
+ "1 322\n",
1178
+ "0 317\n",
1179
+ "9 215\n",
1180
+ "8 123\n",
1181
+ "3 83\n",
1182
+ "2 79\n",
1183
+ "11 51\n",
1184
+ "5 11\n",
1185
+ "12 8\n",
1186
+ "37 1\n",
1187
+ "Name: count, dtype: int64\n",
1188
+ "\n",
1189
+ "Top SUGGESTIVE categories by count:\n",
1190
+ " - dress (id: 10): 1502 annotations\n",
1191
+ " - neckline (id: 33): 965 annotations\n",
1192
+ " - shorts (id: 7): 857 annotations\n",
1193
+ " - jacket (id: 4): 684 annotations\n",
1194
+ " - top, t-shirt, sweatshirt (id: 1): 322 annotations\n",
1195
+ " - shirt, blouse (id: 0): 317 annotations\n",
1196
+ " - coat (id: 9): 215 annotations\n",
1197
+ " - skirt (id: 8): 123 annotations\n",
1198
+ " - cardigan (id: 3): 83 annotations\n",
1199
+ " - sweater (id: 2): 79 annotations\n"
1200
+ ]
1201
+ }
1202
+ ],
1203
+ "source": [
1204
+ "# Get unique image IDs (since same image can have multiple annotations)\n",
1205
+ "unique_SUGGESTIVE_image_ids = SUGGESTIVE_fashion_df['ImageId'].unique()\n",
1206
+ "unique_total_image_ids = fashion_df['ImageId'].unique()\n",
1207
+ "\n",
1208
+ "print(\"=\"*60)\n",
1209
+ "print(\"UNIQUE IMAGE ANALYSIS:\")\n",
1210
+ "print(\"=\"*60)\n",
1211
+ "print(f\"Total unique images in dataset: {len(unique_total_image_ids)}\")\n",
1212
+ "print(f\"Unique images with SUGGESTIVE content: {len(unique_SUGGESTIVE_image_ids)}\")\n",
1213
+ "print(f\"Percentage of unique images: {len(unique_SUGGESTIVE_image_ids) / len(unique_total_image_ids) * 100:.2f}%\")\n",
1214
+ "\n",
1215
+ "# Count how many annotations per SUGGESTIVE image\n",
1216
+ "annotations_per_image = SUGGESTIVE_fashion_df.groupby('ImageId').size().sort_values(ascending=False)\n",
1217
+ "print(f\"\\nAverage annotations per SUGGESTIVE image: {annotations_per_image.mean():.2f}\")\n",
1218
+ "print(f\"Max annotations for a single image: {annotations_per_image.max()}\")\n",
1219
+ "print(f\"Min annotations for a single image: {annotations_per_image.min()}\")\n",
1220
+ "\n",
1221
+ "# Show distribution of SUGGESTIVE categories in the filtered data\n",
1222
+ "print(\"\\n\" + \"=\"*60)\n",
1223
+ "print(\"DISTRIBUTION OF SUGGESTIVE CATEGORIES IN FILTERED DATA:\")\n",
1224
+ "print(\"=\"*60)\n",
1225
+ "category_counts = SUGGESTIVE_fashion_df['ClassId'].value_counts()\n",
1226
+ "print(category_counts)\n",
1227
+ "\n",
1228
+ "# Map category IDs to names for better readability\n",
1229
+ "category_id_to_name = dict(zip(categories_df['id'], categories_df['name']))\n",
1230
+ "print(\"\\nTop SUGGESTIVE categories by count:\")\n",
1231
+ "for cat_id, count in category_counts.head(10).items():\n",
1232
+ " cat_name = category_id_to_name.get(cat_id, f\"Unknown (id: {cat_id})\")\n",
1233
+ " print(f\" - {cat_name} (id: {cat_id}): {count} annotations\")\n",
1234
+ "\n",
1235
+ "# Save the filtered DataFrame\n",
1236
+ "# SUGGESTIVE_fashion_df.to_csv(DATA_PATH / \"SUGGESTIVE_train.csv\", index=False)\n",
1237
+ "# pd.Series(unique_SUGGESTIVE_image_ids).to_csv(DATA_PATH / \"SUGGESTIVE_image_ids.csv\", index=False, header=['ImageId'])\n"
1238
+ ]
1239
+ },
1240
+ {
1241
+ "cell_type": "code",
1242
+ "execution_count": 62,
1243
+ "id": "a4736cf0",
1244
+ "metadata": {},
1245
+ "outputs": [
1246
+ {
1247
+ "name": "stdout",
1248
+ "output_type": "stream",
1249
+ "text": [
1250
+ "============================================================\n",
1251
+ "CREATING NEW DATASET\n",
1252
+ "============================================================\n",
1253
+ "Total unique SUGGESTIVE image IDs: 5079\n",
1254
+ "\n",
1255
+ "Created folder: /Users/youniss/Documents/GitHub/haram-police/data/new_dataset\n",
1256
+ "\n",
1257
+ "Copying images from /Users/youniss/Documents/GitHub/haram-police/data/train and /Users/youniss/Documents/GitHub/haram-police/data/test...\n",
1258
+ "\n",
1259
+ "✓ Successfully copied: 5079 images\n",
1260
+ "\n",
1261
+ "✓ Saved DataFrame to: /Users/youniss/Documents/GitHub/haram-police/data/SUGGESTIVE_fashion.csv\n",
1262
+ " Total rows: 5218\n",
1263
+ "\n",
1264
+ "============================================================\n",
1265
+ "SUMMARY:\n",
1266
+ "============================================================\n",
1267
+ " - Images folder: /Users/youniss/Documents/GitHub/haram-police/data/new_dataset\n",
1268
+ " - Images copied: 5079\n",
1269
+ " - CSV file: /Users/youniss/Documents/GitHub/haram-police/data/SUGGESTIVE_fashion.csv\n",
1270
+ " - CSV rows: 5218\n",
1271
+ " - Unique images: 5079\n"
1272
+ ]
1273
+ }
1274
+ ],
1275
+ "source": [
1276
+ "# Create new_dataset folder and copy all SUGGESTIVE images from train and test\n",
1277
+ "import shutil\n",
1278
+ "\n",
1279
+ "# Get unique image IDs from SUGGESTIVE_fashion_df\n",
1280
+ "unique_SUGGESTIVE_image_ids = set(SUGGESTIVE_fashion_df['ImageId'].unique())\n",
1281
+ "\n",
1282
+ "print(\"=\"*60)\n",
1283
+ "print(\"CREATING NEW DATASET\")\n",
1284
+ "print(\"=\"*60)\n",
1285
+ "print(f\"Total unique SUGGESTIVE image IDs: {len(unique_SUGGESTIVE_image_ids)}\")\n",
1286
+ "\n",
1287
+ "# Create new_dataset folder\n",
1288
+ "NEW_DATASET_PATH = DATA_PATH / \"new_dataset\"\n",
1289
+ "NEW_DATASET_PATH.mkdir(exist_ok=True)\n",
1290
+ "print(f\"\\nCreated folder: {NEW_DATASET_PATH}\")\n",
1291
+ "\n",
1292
+ "# Paths to source folders\n",
1293
+ "TRAIN_IMAGE_PATH = DATA_PATH / \"train\"\n",
1294
+ "TEST_IMAGE_PATH = DATA_PATH / \"test\"\n",
1295
+ "\n",
1296
+ "# Copy images from train and test folders\n",
1297
+ "copied_count = 0\n",
1298
+ "not_found_count = 0\n",
1299
+ "not_found_ids = []\n",
1300
+ "\n",
1301
+ "print(f\"\\nCopying images from {TRAIN_IMAGE_PATH} and {TEST_IMAGE_PATH}...\")\n",
1302
+ "\n",
1303
+ "for image_id in unique_SUGGESTIVE_image_ids:\n",
1304
+ " image_filename = f\"{image_id}.jpg\"\n",
1305
+ " source_path = None\n",
1306
+ " \n",
1307
+ " # Try train folder first\n",
1308
+ " train_path = TRAIN_IMAGE_PATH / image_filename\n",
1309
+ " if train_path.exists():\n",
1310
+ " source_path = train_path\n",
1311
+ " else:\n",
1312
+ " # Try test folder\n",
1313
+ " test_path = TEST_IMAGE_PATH / image_filename\n",
1314
+ " if test_path.exists():\n",
1315
+ " source_path = test_path\n",
1316
+ " \n",
1317
+ " if source_path:\n",
1318
+ " dest_path = NEW_DATASET_PATH / image_filename\n",
1319
+ " shutil.copy2(source_path, dest_path)\n",
1320
+ " copied_count += 1\n",
1321
+ " else:\n",
1322
+ " not_found_count += 1\n",
1323
+ " not_found_ids.append(image_id)\n",
1324
+ "\n",
1325
+ "print(f\"\\n✓ Successfully copied: {copied_count} images\")\n",
1326
+ "if not_found_count > 0:\n",
1327
+ " print(f\"⚠ Not found: {not_found_count} images\")\n",
1328
+ " print(f\" First 10 missing IDs: {not_found_ids[:10]}\")\n",
1329
+ "\n",
1330
+ "# Save the SUGGESTIVE_fashion_df to CSV\n",
1331
+ "csv_path = DATA_PATH / \"SUGGESTIVE_fashion.csv\"\n",
1332
+ "SUGGESTIVE_fashion_df.to_csv(csv_path, index=False)\n",
1333
+ "print(f\"\\n✓ Saved DataFrame to: {csv_path}\")\n",
1334
+ "print(f\" Total rows: {len(SUGGESTIVE_fashion_df)}\")\n",
1335
+ "\n",
1336
+ "print(\"\\n\" + \"=\"*60)\n",
1337
+ "print(\"SUMMARY:\")\n",
1338
+ "print(\"=\"*60)\n",
1339
+ "print(f\" - Images folder: {NEW_DATASET_PATH}\")\n",
1340
+ "print(f\" - Images copied: {copied_count}\")\n",
1341
+ "print(f\" - CSV file: {csv_path}\")\n",
1342
+ "print(f\" - CSV rows: {len(SUGGESTIVE_fashion_df)}\")\n",
1343
+ "print(f\" - Unique images: {len(unique_SUGGESTIVE_image_ids)}\")\n"
1344
+ ]
1345
+ },
1346
+ {
1347
+ "cell_type": "code",
1348
+ "execution_count": 63,
1349
+ "id": "a4bdc53c",
1350
+ "metadata": {},
1351
+ "outputs": [
1352
+ {
1353
+ "data": {
1354
+ "text/html": [
1355
+ "<div>\n",
1356
+ "<style scoped>\n",
1357
+ " .dataframe tbody tr th:only-of-type {\n",
1358
+ " vertical-align: middle;\n",
1359
+ " }\n",
1360
+ "\n",
1361
+ " .dataframe tbody tr th {\n",
1362
+ " vertical-align: top;\n",
1363
+ " }\n",
1364
+ "\n",
1365
+ " .dataframe thead th {\n",
1366
+ " text-align: right;\n",
1367
+ " }\n",
1368
+ "</style>\n",
1369
+ "<table border=\"1\" class=\"dataframe\">\n",
1370
+ " <thead>\n",
1371
+ " <tr style=\"text-align: right;\">\n",
1372
+ " <th></th>\n",
1373
+ " <th>ImageId</th>\n",
1374
+ " <th>EncodedPixels</th>\n",
1375
+ " <th>Height</th>\n",
1376
+ " <th>Width</th>\n",
1377
+ " <th>ClassId</th>\n",
1378
+ " <th>AttributesIds</th>\n",
1379
+ " </tr>\n",
1380
+ " </thead>\n",
1381
+ " <tbody>\n",
1382
+ " <tr>\n",
1383
+ " <th>49</th>\n",
1384
+ " <td>000b3a87508b0fa185fbd53ecbe2e4c6</td>\n",
1385
+ " <td>457283 2 458562 6 459841 9 461120 13 462400 15...</td>\n",
1386
+ " <td>1280</td>\n",
1387
+ " <td>852</td>\n",
1388
+ " <td>33</td>\n",
1389
+ " <td>192</td>\n",
1390
+ " </tr>\n",
1391
+ " <tr>\n",
1392
+ " <th>147</th>\n",
1393
+ " <td>001a66b16b12f12dc45e2bba40e04683</td>\n",
1394
+ " <td>64049 3 64548 10 65048 17 65548 23 65754 36 66...</td>\n",
1395
+ " <td>500</td>\n",
1396
+ " <td>375</td>\n",
1397
+ " <td>10</td>\n",
1398
+ " <td>106,115,127,142,149,229,295,316</td>\n",
1399
+ " </tr>\n",
1400
+ " <tr>\n",
1401
+ " <th>180</th>\n",
1402
+ " <td>00211c06b1fe730097dde122cd4d3f8c</td>\n",
1403
+ " <td>296470 1 297469 3 298468 5 299467 8 300466 10 ...</td>\n",
1404
+ " <td>1000</td>\n",
1405
+ " <td>665</td>\n",
1406
+ " <td>7</td>\n",
1407
+ " <td>50,115,136,142,148,230,295,300,317</td>\n",
1408
+ " </tr>\n",
1409
+ " <tr>\n",
1410
+ " <th>304</th>\n",
1411
+ " <td>003ae3da258f7ba7267af5f159dd3502</td>\n",
1412
+ " <td>129565 3 130583 9 131602 14 132621 19 133641 2...</td>\n",
1413
+ " <td>1024</td>\n",
1414
+ " <td>683</td>\n",
1415
+ " <td>10</td>\n",
1416
+ " <td>106,127,141,150,295,316,317</td>\n",
1417
+ " </tr>\n",
1418
+ " <tr>\n",
1419
+ " <th>369</th>\n",
1420
+ " <td>0048f6c47de85cc4dc263912bd0ff6f5</td>\n",
1421
+ " <td>4777361 1 4781320 3 4785279 5 4789239 7 479319...</td>\n",
1422
+ " <td>3960</td>\n",
1423
+ " <td>2640</td>\n",
1424
+ " <td>33</td>\n",
1425
+ " <td>192</td>\n",
1426
+ " </tr>\n",
1427
+ " <tr>\n",
1428
+ " <th>372</th>\n",
1429
+ " <td>0048f6c47de85cc4dc263912bd0ff6f5</td>\n",
1430
+ " <td>3982550 2 3986509 8 3990469 13 3994429 18 3998...</td>\n",
1431
+ " <td>3960</td>\n",
1432
+ " <td>2640</td>\n",
1433
+ " <td>7</td>\n",
1434
+ " <td>50,115,136,142,148,317</td>\n",
1435
+ " </tr>\n",
1436
+ " <tr>\n",
1437
+ " <th>445</th>\n",
1438
+ " <td>005380bd939eb68085af3f804d387824</td>\n",
1439
+ " <td>2317673 15 2320644 45 2323624 67 2326613 79 23...</td>\n",
1440
+ " <td>3000</td>\n",
1441
+ " <td>2001</td>\n",
1442
+ " <td>10</td>\n",
1443
+ " <td>106,114,127,142,150,229,295,311,317</td>\n",
1444
+ " </tr>\n",
1445
+ " <tr>\n",
1446
+ " <th>456</th>\n",
1447
+ " <td>0054564ae183ad9a1b152eef0bc11e1d</td>\n",
1448
+ " <td>195071 2 196093 5 197115 8 198134 13 199151 20...</td>\n",
1449
+ " <td>1024</td>\n",
1450
+ " <td>683</td>\n",
1451
+ " <td>10</td>\n",
1452
+ " <td>106,115,127,142,149,229,295,316,317</td>\n",
1453
+ " </tr>\n",
1454
+ " <tr>\n",
1455
+ " <th>465</th>\n",
1456
+ " <td>0055347a114b215f8f469fec9e38c272</td>\n",
1457
+ " <td>236337 20 237832 26 239327 33 240823 38 242320...</td>\n",
1458
+ " <td>1500</td>\n",
1459
+ " <td>1000</td>\n",
1460
+ " <td>10</td>\n",
1461
+ " <td>106,115,127,142,149,229,295,316,317</td>\n",
1462
+ " </tr>\n",
1463
+ " <tr>\n",
1464
+ " <th>526</th>\n",
1465
+ " <td>005e9b75edcee7d655c390ea5416641d</td>\n",
1466
+ " <td>480863 2 481943 3 483023 4 484102 6 485182 7 4...</td>\n",
1467
+ " <td>1080</td>\n",
1468
+ " <td>1080</td>\n",
1469
+ " <td>33</td>\n",
1470
+ " <td>192</td>\n",
1471
+ " </tr>\n",
1472
+ " <tr>\n",
1473
+ " <th>593</th>\n",
1474
+ " <td>006bb85ca0935680110f4ce67d88b4ee</td>\n",
1475
+ " <td>2461619 6 2463717 17 2465814 24 2467912 23 247...</td>\n",
1476
+ " <td>2096</td>\n",
1477
+ " <td>3000</td>\n",
1478
+ " <td>10</td>\n",
1479
+ " <td>106,115,127,142,149,295,316,317</td>\n",
1480
+ " </tr>\n",
1481
+ " <tr>\n",
1482
+ " <th>802</th>\n",
1483
+ " <td>009447b79fce7da1ee19a54401517cde</td>\n",
1484
+ " <td>23802163 9 23807451 27 23812746 37 23818049 40...</td>\n",
1485
+ " <td>5304</td>\n",
1486
+ " <td>7952</td>\n",
1487
+ " <td>7</td>\n",
1488
+ " <td>50,115,136,142,148,230,295,298,317</td>\n",
1489
+ " </tr>\n",
1490
+ " <tr>\n",
1491
+ " <th>884</th>\n",
1492
+ " <td>00af8f65bb93f4131499dc9807129a24</td>\n",
1493
+ " <td>1313044 41 1315994 123 1318943 207 1321893 289...</td>\n",
1494
+ " <td>3000</td>\n",
1495
+ " <td>2000</td>\n",
1496
+ " <td>4</td>\n",
1497
+ " <td>17,115,135,145,148,225,281,311,317</td>\n",
1498
+ " </tr>\n",
1499
+ " <tr>\n",
1500
+ " <th>1201</th>\n",
1501
+ " <td>00f7d06a8db722b86961d911fb9f1d96</td>\n",
1502
+ " <td>54821 5 55316 16 55811 27 56307 37 56802 48 57...</td>\n",
1503
+ " <td>500</td>\n",
1504
+ " <td>375</td>\n",
1505
+ " <td>10</td>\n",
1506
+ " <td>106,115,127,142,151,229,283,311</td>\n",
1507
+ " </tr>\n",
1508
+ " <tr>\n",
1509
+ " <th>1217</th>\n",
1510
+ " <td>00f843a44365248e179ad2a489734913</td>\n",
1511
+ " <td>1690956 5 1693572 11 1696188 15 1698804 18 170...</td>\n",
1512
+ " <td>2617</td>\n",
1513
+ " <td>1500</td>\n",
1514
+ " <td>33</td>\n",
1515
+ " <td>192</td>\n",
1516
+ " </tr>\n",
1517
+ " <tr>\n",
1518
+ " <th>1260</th>\n",
1519
+ " <td>01098396b79639e29db8de146c2d0064</td>\n",
1520
+ " <td>959078 8 962065 22 965051 38 968037 54 971024 ...</td>\n",
1521
+ " <td>3000</td>\n",
1522
+ " <td>2000</td>\n",
1523
+ " <td>7</td>\n",
1524
+ " <td>50,148,234,295,316,317</td>\n",
1525
+ " </tr>\n",
1526
+ " <tr>\n",
1527
+ " <th>1274</th>\n",
1528
+ " <td>010db49ecc226102e63815fcf5627319</td>\n",
1529
+ " <td>1205717 31 1208132 69 1210571 82 1213011 94 12...</td>\n",
1530
+ " <td>2448</td>\n",
1531
+ " <td>2448</td>\n",
1532
+ " <td>10</td>\n",
1533
+ " <td>112,115,119,145,148,229,295,306,323,325</td>\n",
1534
+ " </tr>\n",
1535
+ " <tr>\n",
1536
+ " <th>1311</th>\n",
1537
+ " <td>0116a12304c7f94686978f86100076f3</td>\n",
1538
+ " <td>32646 31 33110 78 33587 109 34065 136 34542 15...</td>\n",
1539
+ " <td>492</td>\n",
1540
+ " <td>354</td>\n",
1541
+ " <td>10</td>\n",
1542
+ " <td>106,115,127,142,150,229,295,316,317</td>\n",
1543
+ " </tr>\n",
1544
+ " <tr>\n",
1545
+ " <th>1356</th>\n",
1546
+ " <td>011afec5e443599a79261ece1a662043</td>\n",
1547
+ " <td>629287 14 631022 16 632757 18 634492 21 636227...</td>\n",
1548
+ " <td>1737</td>\n",
1549
+ " <td>1157</td>\n",
1550
+ " <td>4</td>\n",
1551
+ " <td>17,148,225,281,311,317</td>\n",
1552
+ " </tr>\n",
1553
+ " <tr>\n",
1554
+ " <th>1371</th>\n",
1555
+ " <td>011c59f7c25d18027f4f9b2b1cffd44a</td>\n",
1556
+ " <td>168345 3 169142 7 169938 12 170735 17 171531 2...</td>\n",
1557
+ " <td>800</td>\n",
1558
+ " <td>800</td>\n",
1559
+ " <td>10</td>\n",
1560
+ " <td>101,115,129,145,148,289,301,317</td>\n",
1561
+ " </tr>\n",
1562
+ " </tbody>\n",
1563
+ "</table>\n",
1564
+ "</div>"
1565
+ ],
1566
+ "text/plain": [
1567
+ " ImageId \\\n",
1568
+ "49 000b3a87508b0fa185fbd53ecbe2e4c6 \n",
1569
+ "147 001a66b16b12f12dc45e2bba40e04683 \n",
1570
+ "180 00211c06b1fe730097dde122cd4d3f8c \n",
1571
+ "304 003ae3da258f7ba7267af5f159dd3502 \n",
1572
+ "369 0048f6c47de85cc4dc263912bd0ff6f5 \n",
1573
+ "372 0048f6c47de85cc4dc263912bd0ff6f5 \n",
1574
+ "445 005380bd939eb68085af3f804d387824 \n",
1575
+ "456 0054564ae183ad9a1b152eef0bc11e1d \n",
1576
+ "465 0055347a114b215f8f469fec9e38c272 \n",
1577
+ "526 005e9b75edcee7d655c390ea5416641d \n",
1578
+ "593 006bb85ca0935680110f4ce67d88b4ee \n",
1579
+ "802 009447b79fce7da1ee19a54401517cde \n",
1580
+ "884 00af8f65bb93f4131499dc9807129a24 \n",
1581
+ "1201 00f7d06a8db722b86961d911fb9f1d96 \n",
1582
+ "1217 00f843a44365248e179ad2a489734913 \n",
1583
+ "1260 01098396b79639e29db8de146c2d0064 \n",
1584
+ "1274 010db49ecc226102e63815fcf5627319 \n",
1585
+ "1311 0116a12304c7f94686978f86100076f3 \n",
1586
+ "1356 011afec5e443599a79261ece1a662043 \n",
1587
+ "1371 011c59f7c25d18027f4f9b2b1cffd44a \n",
1588
+ "\n",
1589
+ " EncodedPixels Height Width \\\n",
1590
+ "49 457283 2 458562 6 459841 9 461120 13 462400 15... 1280 852 \n",
1591
+ "147 64049 3 64548 10 65048 17 65548 23 65754 36 66... 500 375 \n",
1592
+ "180 296470 1 297469 3 298468 5 299467 8 300466 10 ... 1000 665 \n",
1593
+ "304 129565 3 130583 9 131602 14 132621 19 133641 2... 1024 683 \n",
1594
+ "369 4777361 1 4781320 3 4785279 5 4789239 7 479319... 3960 2640 \n",
1595
+ "372 3982550 2 3986509 8 3990469 13 3994429 18 3998... 3960 2640 \n",
1596
+ "445 2317673 15 2320644 45 2323624 67 2326613 79 23... 3000 2001 \n",
1597
+ "456 195071 2 196093 5 197115 8 198134 13 199151 20... 1024 683 \n",
1598
+ "465 236337 20 237832 26 239327 33 240823 38 242320... 1500 1000 \n",
1599
+ "526 480863 2 481943 3 483023 4 484102 6 485182 7 4... 1080 1080 \n",
1600
+ "593 2461619 6 2463717 17 2465814 24 2467912 23 247... 2096 3000 \n",
1601
+ "802 23802163 9 23807451 27 23812746 37 23818049 40... 5304 7952 \n",
1602
+ "884 1313044 41 1315994 123 1318943 207 1321893 289... 3000 2000 \n",
1603
+ "1201 54821 5 55316 16 55811 27 56307 37 56802 48 57... 500 375 \n",
1604
+ "1217 1690956 5 1693572 11 1696188 15 1698804 18 170... 2617 1500 \n",
1605
+ "1260 959078 8 962065 22 965051 38 968037 54 971024 ... 3000 2000 \n",
1606
+ "1274 1205717 31 1208132 69 1210571 82 1213011 94 12... 2448 2448 \n",
1607
+ "1311 32646 31 33110 78 33587 109 34065 136 34542 15... 492 354 \n",
1608
+ "1356 629287 14 631022 16 632757 18 634492 21 636227... 1737 1157 \n",
1609
+ "1371 168345 3 169142 7 169938 12 170735 17 171531 2... 800 800 \n",
1610
+ "\n",
1611
+ " ClassId AttributesIds \n",
1612
+ "49 33 192 \n",
1613
+ "147 10 106,115,127,142,149,229,295,316 \n",
1614
+ "180 7 50,115,136,142,148,230,295,300,317 \n",
1615
+ "304 10 106,127,141,150,295,316,317 \n",
1616
+ "369 33 192 \n",
1617
+ "372 7 50,115,136,142,148,317 \n",
1618
+ "445 10 106,114,127,142,150,229,295,311,317 \n",
1619
+ "456 10 106,115,127,142,149,229,295,316,317 \n",
1620
+ "465 10 106,115,127,142,149,229,295,316,317 \n",
1621
+ "526 33 192 \n",
1622
+ "593 10 106,115,127,142,149,295,316,317 \n",
1623
+ "802 7 50,115,136,142,148,230,295,298,317 \n",
1624
+ "884 4 17,115,135,145,148,225,281,311,317 \n",
1625
+ "1201 10 106,115,127,142,151,229,283,311 \n",
1626
+ "1217 33 192 \n",
1627
+ "1260 7 50,148,234,295,316,317 \n",
1628
+ "1274 10 112,115,119,145,148,229,295,306,323,325 \n",
1629
+ "1311 10 106,115,127,142,150,229,295,316,317 \n",
1630
+ "1356 4 17,148,225,281,311,317 \n",
1631
+ "1371 10 101,115,129,145,148,289,301,317 "
1632
+ ]
1633
+ },
1634
+ "execution_count": 63,
1635
+ "metadata": {},
1636
+ "output_type": "execute_result"
1637
+ }
1638
+ ],
1639
+ "source": [
1640
+ "SUGGESTIVE_fashion_df.head(20)"
1641
+ ]
1642
+ },
1643
+ {
1644
+ "cell_type": "code",
1645
+ "execution_count": 64,
1646
+ "id": "a9e2133d",
1647
+ "metadata": {},
1648
+ "outputs": [],
1649
+ "source": [
1650
+ "# Show the \"EncodedPixels\" feature of the first SUGGESTIVE_fashion_df \n",
1651
+ "from PIL import Image\n",
1652
+ "\n",
1653
+ "# Get the first image ID from the SUGGESTIVE_fashion_df\n",
1654
+ "first_image_id = SUGGESTIVE_fashion_df.iloc[0]['ImageId']\n",
1655
+ "\n",
1656
+ "# Load the image\n",
1657
+ "image = Image.open((DATA_PATH / \"train\" / f\"{first_image_id}.jpg\"))\n",
1658
+ "image.show()"
1659
+ ]
1660
+ },
1661
+ {
1662
+ "cell_type": "code",
1663
+ "execution_count": null,
1664
+ "id": "2d6351ab",
1665
+ "metadata": {},
1666
+ "outputs": [],
1667
+ "source": []
1668
+ }
1669
+ ],
1670
+ "metadata": {
1671
+ "kernelspec": {
1672
+ "display_name": ".venv",
1673
+ "language": "python",
1674
+ "name": "python3"
1675
+ },
1676
+ "language_info": {
1677
+ "codemirror_mode": {
1678
+ "name": "ipython",
1679
+ "version": 3
1680
+ },
1681
+ "file_extension": ".py",
1682
+ "mimetype": "text/x-python",
1683
+ "name": "python",
1684
+ "nbconvert_exporter": "python",
1685
+ "pygments_lexer": "ipython3",
1686
+ "version": "3.13.5"
1687
+ }
1688
+ },
1689
+ "nbformat": 4,
1690
+ "nbformat_minor": 5
1691
+ }
pyproject.toml ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [project]
2
+ name = "haram-police"
3
+ version = "0.1.0"
4
+ description = "Add your description here"
5
+ readme = "README.md"
6
+ requires-python = ">=3.13"
7
+ dependencies = [
8
+ "datasets>=4.4.2",
9
+ "huggingface-hub>=0.36.0",
10
+ "ipykernel>=7.1.0",
11
+ "joblib>=1.4.2",
12
+ "matplotlib>=3.10.8",
13
+ "pandas>=2.3.3",
14
+ "pillow>=12.0.0",
15
+ "psycopg2>=2.9.11",
16
+ "python-dotenv>=1.2.1",
17
+ "rich>=14.2.0",
18
+ "scikit-learn>=1.8.0",
19
+ "scipy>=1.14.1",
20
+ "seaborn>=0.13.2",
21
+ "shap>=0.46.0",
22
+ "supabase>=2.27.0",
23
+ "torch>=2.9.1",
24
+ "transformers>=4.57.3",
25
+ ]
src/baseline.py ADDED
@@ -0,0 +1,145 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from dotenv import load_dotenv
2
+ from pathlib import Path
3
+ import os
4
+ import json
5
+ import pandas as pd
6
+ from huggingface_hub import InferenceClient
7
+ from rich import print
8
+ from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
9
+
10
+ load_dotenv()
11
+
12
+ DATA_PATH = Path(__file__).parent.parent / "data"
13
+ RESULTS_PATH = DATA_PATH / "results"
14
+ IMAGES_PATH = DATA_PATH / "imgs"
15
+
16
+ with open(DATA_PATH / "annotations.json", "r") as f:
17
+ annotations = json.load(f)
18
+
19
+ print(f"[bold]Loaded {len(annotations)} annotations from Label Studio[/bold]")
20
+
21
+ # Extract annotated images with their labels
22
+ # Files should now be in data/imgs/ with their file_upload names
23
+ annotated_images = []
24
+ for ann in annotations:
25
+ file_upload = ann.get("file_upload")
26
+ if not file_upload:
27
+ continue
28
+
29
+ # Get the annotation choice
30
+ choice = None
31
+ if ann.get("annotations") and len(ann["annotations"]) > 0:
32
+ result = ann["annotations"][0].get("result", [])
33
+ if result and len(result) > 0:
34
+ choices = result[0].get("value", {}).get("choices", [])
35
+ if choices:
36
+ choice = choices[0]
37
+
38
+ # File should be in data/imgs/ with the file_upload name
39
+ file_path = IMAGES_PATH / file_upload
40
+
41
+ annotated_images.append({
42
+ "file_upload": file_upload,
43
+ "file_path": file_path,
44
+ "choice": choice,
45
+ "annotation_id": ann.get("id")
46
+ })
47
+
48
+ # Check how many files actually exist
49
+ existing_files = [img for img in annotated_images if img["file_path"].exists()]
50
+ print(
51
+ f"[bold]Found {len(existing_files)}/{len(annotated_images)} annotated image files[/bold]")
52
+
53
+ # Initialize client
54
+ client = InferenceClient(
55
+ provider="hf-inference",
56
+ api_key=os.environ.get("HF_TOKEN"),
57
+ )
58
+
59
+ if not os.environ.get("HF_TOKEN"):
60
+ raise ValueError(
61
+ "HF_TOKEN environment variable not set. Please set it in .env file.")
62
+
63
+ # Filter to only images that exist
64
+ images_to_process = [
65
+ img for img in annotated_images if img["file_path"].exists()]
66
+
67
+ if not images_to_process:
68
+ print("[red]✗ No annotated image files found![/red]")
69
+ print("[yellow]Please copy files from Label Studio media directory first.[/yellow]")
70
+ print(f"[dim]Expected location: {IMAGES_PATH}[/dim]")
71
+
72
+ predictions = []
73
+ errors = []
74
+
75
+ print(f"[bold]Processing {len(images_to_process)} annotated images...[/bold]")
76
+
77
+ with Progress(
78
+ SpinnerColumn(),
79
+ TextColumn("[progress.description]{task.description}"),
80
+ BarColumn(),
81
+ TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
82
+ console=None,
83
+ ) as progress:
84
+ task = progress.add_task("Classifying images",
85
+ total=len(images_to_process))
86
+
87
+ for ann_img in images_to_process:
88
+ file_path = ann_img["file_path"]
89
+ file_upload = ann_img["file_upload"]
90
+ choice = ann_img.get("choice")
91
+ annotation_id = ann_img.get("annotation_id")
92
+
93
+ try:
94
+ if not file_path or not file_path.exists():
95
+ errors.append({
96
+ "annotation_id": annotation_id,
97
+ "file_upload": file_upload,
98
+ "error": "File not found"
99
+ })
100
+ progress.update(task, advance=1)
101
+ continue
102
+
103
+ # Classify image
104
+ output = client.image_classification(
105
+ str(file_path),
106
+ model="Falconsai/nsfw_image_detection"
107
+ )
108
+
109
+ # Flatten the output (list of dicts) and add metadata
110
+ result = {
111
+ "annotation_id": annotation_id,
112
+ "file_upload": file_upload,
113
+ "actual_filename": file_path.name,
114
+ "label_studio_choice": choice,
115
+ **{f"label_{i}": pred["label"] for i, pred in enumerate(output)},
116
+ **{f"score_{i}": pred["score"] for i, pred in enumerate(output)}
117
+ }
118
+ predictions.append(result)
119
+
120
+ except Exception as e:
121
+ errors.append({
122
+ "annotation_id": annotation_id,
123
+ "file_upload": file_upload,
124
+ "error": str(e)
125
+ })
126
+ print(f"[red]Error processing {file_upload}: {e}[/red]")
127
+
128
+ finally:
129
+ progress.update(task, advance=1)
130
+
131
+ # Save predictions
132
+ if predictions:
133
+ predictions_df = pd.DataFrame(predictions)
134
+ predictions_df.to_csv(RESULTS_PATH / "baseline.csv", index=False)
135
+ print(
136
+ f"[green]✓ Saved {len(predictions)} predictions to baseline.csv[/green]")
137
+ else:
138
+ print("[red]✗ No predictions generated[/red]")
139
+
140
+ # Save errors if any
141
+ if errors:
142
+ errors_df = pd.DataFrame(errors)
143
+ errors_df.to_csv(DATA_PATH / "falcons_errors.csv", index=False)
144
+ print(
145
+ f"[yellow]⚠ Saved {len(errors)} errors to falcons_errors.csv[/yellow]")
src/dataset.py ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+ import pandas as pd
3
+ from datasets import Dataset, DatasetDict
4
+ from sklearn.model_selection import train_test_split
5
+ import torch
6
+ from torch.utils.data import Dataset as TorchDataset
7
+ import numpy as np
8
+ from PIL import Image
9
+ from transformers import CLIPProcessor
10
+
11
+
12
+ DATA_PATH = Path(__file__).parent.parent / "data"
13
+ IMAGES_PATH = DATA_PATH / "imgs"
14
+ LABELS_CSV = DATA_PATH / "labels.csv"
15
+
16
+
17
+ def load_golden_dataset() -> pd.DataFrame:
18
+ df = pd.read_csv(LABELS_CSV)
19
+
20
+ # Convert image paths from /data/imgs/... to actual file paths
21
+ df["image"] = df["image"].apply(
22
+ lambda x: str(IMAGES_PATH / Path(x).name)
23
+ )
24
+
25
+ # Verify files exist
26
+ existing = df["image"].apply(lambda x: Path(x).exists())
27
+ missing_count = (~existing).sum()
28
+ if missing_count > 0:
29
+ print(f"Warning: {missing_count} image files not found")
30
+ df = df[existing].copy()
31
+
32
+ # Preprocess labels: combine UNCERTAIN with FAMILY_SAFE (0), SUGGESTIVE (1)
33
+ # 0 = FAMILY_SAFE/UNCERTAIN (closer to 0 means FAMILY_SAFE/UNCERTAIN)
34
+ # 1 = SUGGESTIVE (closer to 1 means SUGGESTIVE)
35
+ df["label"] = df["choice"].apply(
36
+ lambda x: 0 if x in ["FAMILY_SAFE", "UNCERTAIN"] else 1
37
+ )
38
+
39
+ return df
40
+
41
+
42
+ def create_dataset_splits(
43
+ train_size: float = 0.7,
44
+ test_size: float = 0.15,
45
+ val_size: float = 0.15,
46
+ random_state: int = 42
47
+ ) -> DatasetDict:
48
+ # Validate split sizes
49
+ assert abs(train_size + test_size + val_size - 1.0) < 1e-6, \
50
+ "Split sizes must sum to 1.0"
51
+
52
+ # Load data
53
+ df = load_golden_dataset()
54
+
55
+ print(f"Loaded {len(df)} golden self-labelled images")
56
+ print("Original label distribution:")
57
+ print(df["choice"].value_counts())
58
+ print("\nBinary label distribution (after preprocessing):")
59
+ print(df["label"].value_counts())
60
+ print(" (0 = FAMILY_SAFE/UNCERTAIN, 1 = SUGGESTIVE)")
61
+
62
+ # First split: train vs (test + val)
63
+ # Stratify by binary label to maintain distribution
64
+ train_df, temp_df = train_test_split(
65
+ df,
66
+ test_size=(test_size + val_size),
67
+ stratify=df["label"],
68
+ random_state=random_state
69
+ )
70
+
71
+ # Second split: test vs val
72
+ # Adjust test_size for the remaining data
73
+ test_proportion = test_size / (test_size + val_size)
74
+ test_df, val_df = train_test_split(
75
+ temp_df,
76
+ test_size=(1 - test_proportion),
77
+ stratify=temp_df["label"],
78
+ random_state=random_state
79
+ )
80
+
81
+ print("\nSplit sizes:")
82
+ print(f" Train: {len(train_df)} ({len(train_df)/len(df)*100:.1f}%)")
83
+ print(f" Test: {len(test_df)} ({len(test_df)/len(df)*100:.1f}%)")
84
+ print(f" val: {len(val_df)} ({len(val_df)/len(df)*100:.1f}%)")
85
+
86
+ # Convert to HuggingFace Datasets
87
+ train_ds = Dataset.from_pandas(train_df)
88
+ test_ds = Dataset.from_pandas(test_df)
89
+ val_ds = Dataset.from_pandas(val_df)
90
+
91
+ # Create DatasetDict
92
+ dataset_dict = DatasetDict({
93
+ "train": train_ds,
94
+ "test": test_ds,
95
+ "val": val_ds
96
+ })
97
+
98
+ return dataset_dict
99
+
100
+
101
+ def get_dataset(
102
+ train_size: float = 0.7,
103
+ test_size: float = 0.15,
104
+ val_size: float = 0.15,
105
+ random_state: int = 42
106
+ ) -> DatasetDict:
107
+ return create_dataset_splits(
108
+ train_size=train_size,
109
+ test_size=test_size,
110
+ val_size=val_size,
111
+ random_state=random_state
112
+ )
113
+
114
+
115
+ class ImageDataset(TorchDataset):
116
+ """PyTorch Dataset for image classification."""
117
+
118
+ def __init__(self, image_paths: list[str], labels: np.ndarray, processor: CLIPProcessor):
119
+ self.image_paths = image_paths
120
+ self.labels = torch.tensor(labels, dtype=torch.long)
121
+ self.processor = processor
122
+
123
+ def __len__(self) -> int:
124
+ return len(self.image_paths)
125
+
126
+ def __getitem__(self, idx: int) -> tuple[dict, torch.Tensor]:
127
+ # Single item fetching (PyTorch DataLoader handles batching automatically)
128
+ img_path = self.image_paths[idx]
129
+ image = Image.open(img_path).convert("RGB")
130
+
131
+ # Process image with CLIP processor
132
+ inputs = self.processor(images=image, return_tensors="pt")
133
+ # Remove batch dimension from processor output
134
+ pixel_values = inputs["pixel_values"].squeeze(0)
135
+
136
+ label = self.labels[idx]
137
+ return {"pixel_values": pixel_values}, label
src/inference.py ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import json
3
+ from pathlib import Path
4
+ from transformers import CLIPProcessor
5
+ from src.model import DISCO
6
+ from PIL import Image
7
+ from typing import Tuple, Optional
8
+
9
+ MODELS_DIR = Path(__file__).parent.parent / "models"
10
+
11
+
12
+ def load_model(
13
+ device: Optional[str] = None,
14
+ compile_model: bool = False
15
+ ) -> Tuple[DISCO, CLIPProcessor, float, dict]:
16
+ """
17
+ Load trained DISCO model, processor, threshold, and metadata.
18
+
19
+ Args:
20
+ device: Device to load model on (None = auto-detect)
21
+ compile_model: Whether to compile model with torch.compile (not implemented)
22
+
23
+ Returns:
24
+ Tuple of (model, processor, threshold, metadata)
25
+ """
26
+ if device is None:
27
+ device = "mps" if torch.backends.mps.is_available() else (
28
+ "cuda" if torch.cuda.is_available() else "cpu"
29
+ )
30
+
31
+ # Load model
32
+ model = DISCO.from_pretrained(MODELS_DIR)
33
+ model = model.to(device)
34
+ model.eval()
35
+
36
+ # Load processor
37
+ processor = CLIPProcessor.from_pretrained(MODELS_DIR)
38
+
39
+ # Load metadata for threshold and other info
40
+ metadata_path = MODELS_DIR / "model_metadata.json"
41
+ if not metadata_path.exists():
42
+ raise FileNotFoundError(
43
+ f"Model metadata not found at {metadata_path}. "
44
+ "Please run 'python src/train.py' first."
45
+ )
46
+
47
+ with open(metadata_path, "r") as f:
48
+ metadata = json.load(f)
49
+
50
+ threshold = metadata.get("threshold", 0.5)
51
+
52
+ # Store device for easy access
53
+ model._device = device
54
+
55
+ if compile_model:
56
+ # Future: could use torch.compile here if needed
57
+ pass
58
+
59
+ return model, processor, threshold, metadata
60
+
61
+
62
+ # Lazy-loaded default model (loaded on first use, not at import time)
63
+ _default_model = None
64
+ _default_processor = None
65
+ _default_threshold = None
66
+
67
+
68
+ def _get_default_model():
69
+ """Lazy-load default model on first use."""
70
+ global _default_model, _default_processor, _default_threshold
71
+ if _default_model is None:
72
+ _default_model, _default_processor, _default_threshold, _ = load_model()
73
+ return _default_model, _default_processor, _default_threshold
74
+
75
+
76
+ def run_DISCO(
77
+ image_path: str,
78
+ model: Optional[DISCO] = None,
79
+ processor: Optional[CLIPProcessor] = None,
80
+ threshold: Optional[float] = None
81
+ ) -> float:
82
+ """
83
+ Run DISCO inference on a single image.
84
+
85
+ Args:
86
+ image_path: Path to image file
87
+ model: DISCO model (uses default if None)
88
+ processor: CLIPProcessor (uses default if None)
89
+ threshold: Classification threshold (uses model default if None)
90
+
91
+ Returns:
92
+ Probability of SUGGESTIVE class (0-1)
93
+ """
94
+ # Use defaults if not provided
95
+ if model is None or processor is None:
96
+ default_model, default_processor, default_threshold = _get_default_model()
97
+ model = model or default_model
98
+ processor = processor or default_processor
99
+ threshold = threshold or default_threshold
100
+
101
+ # Get device from model
102
+ device = getattr(model, '_device', next(model.parameters()).device)
103
+
104
+ # Load and preprocess image
105
+ image = Image.open(image_path).convert("RGB")
106
+ inputs = processor(images=image, return_tensors="pt")
107
+ inputs = {k: v.to(device) for k, v in inputs.items()}
108
+
109
+ # Run inference
110
+ with torch.no_grad():
111
+ logits = model(**inputs)
112
+ proba = torch.softmax(logits, dim=-1)[0, 1].item()
113
+
114
+ return proba
src/model.py ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+ from transformers import CLIPModel, PreTrainedModel, PretrainedConfig
4
+
5
+
6
+ class DISCOConfig(PretrainedConfig):
7
+ """Configuration for DISCO model."""
8
+ model_type = "clip_nsfw_detector"
9
+
10
+ def __init__(
11
+ self,
12
+ clip_model_name: str = "openai/clip-vit-base-patch32",
13
+ num_classes: int = 2,
14
+ threshold: float = 0.5,
15
+ **kwargs
16
+ ):
17
+ super().__init__(**kwargs)
18
+ self.clip_model_name = clip_model_name
19
+ self.num_classes = num_classes
20
+ self.threshold = threshold
21
+
22
+
23
+ class DISCO(PreTrainedModel):
24
+ """
25
+ DISCO model combining CLIP image encoder and classification head.
26
+ """
27
+ config_class = DISCOConfig
28
+
29
+ def __init__(self, config: DISCOConfig):
30
+ super().__init__(config)
31
+ self.clip_model = CLIPModel.from_pretrained(config.clip_model_name)
32
+ self.clip_model.eval() # Freeze CLIP, only classifier is trainable
33
+
34
+ # Get embedding dimension from CLIP config
35
+ embedding_dim = self.clip_model.config.projection_dim
36
+ # Direct linear classifier layer (equivalent to logistic regression)
37
+ self.classifier = nn.Linear(embedding_dim, config.num_classes)
38
+ self.threshold = config.threshold
39
+
40
+ def forward(self, pixel_values: torch.Tensor, **kwargs) -> torch.Tensor:
41
+ """
42
+ Forward pass through CLIP and classifier.
43
+
44
+ Args:
45
+ pixel_values: Preprocessed image tensors (batch_size, channels, height, width)
46
+
47
+ Returns:
48
+ Logits for binary classification
49
+ """
50
+ # Get image features from CLIP
51
+ with torch.no_grad():
52
+ image_features = self.clip_model.get_image_features(
53
+ pixel_values=pixel_values)
54
+ # Normalize embeddings (CLIP uses normalized embeddings)
55
+ image_features = image_features / \
56
+ image_features.norm(dim=-1, keepdim=True)
57
+
58
+ # Pass through classifier
59
+ logits = self.classifier(image_features)
60
+ return logits
61
+
62
+ def predict_proba(self, pixel_values: torch.Tensor) -> torch.Tensor:
63
+ """Get probability predictions."""
64
+ logits = self.forward(pixel_values)
65
+ return torch.softmax(logits, dim=-1)
66
+
67
+ def predict(self, pixel_values: torch.Tensor, threshold: float = None) -> torch.Tensor:
68
+ """Get binary predictions."""
69
+ if threshold is None:
70
+ threshold = self.threshold
71
+ proba = self.predict_proba(pixel_values)
72
+ return (proba[:, 1] >= threshold).long()
src/train.py ADDED
@@ -0,0 +1,389 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Train DISCO model using PyTorch end-to-end training.
3
+
4
+ This script trains the CLIP-based classifier directly in PyTorch,
5
+ avoiding the sklearn intermediate step.
6
+ """
7
+
8
+ import torch
9
+ import torch.nn as nn
10
+ import torch.optim as optim
11
+ from torch.utils.data import DataLoader
12
+ import numpy as np
13
+ import json
14
+ from pathlib import Path
15
+ from sklearn.metrics import (
16
+ roc_auc_score, average_precision_score, roc_curve, classification_report
17
+ )
18
+ from transformers import CLIPProcessor
19
+ from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeElapsedColumn
20
+ from src.dataset import get_dataset, ImageDataset
21
+ from src.model import DISCO, DISCOConfig
22
+
23
+
24
+ def tune_threshold(y_true: np.ndarray, y_scores: np.ndarray, metric: str = "f1") -> tuple[float, dict]:
25
+ """
26
+ Tune classification threshold on validation set.
27
+
28
+ Args:
29
+ y_true: Ground truth binary labels
30
+ y_scores: Predicted probability scores
31
+ metric: Metric to optimize ("f1", "precision", "recall", "balanced_accuracy")
32
+
33
+ Returns:
34
+ Best threshold and metrics at that threshold
35
+ """
36
+ fpr, tpr, thresholds = roc_curve(y_true, y_scores)
37
+
38
+ best_threshold = 0.5
39
+ best_score = 0.0
40
+ best_metrics = {}
41
+
42
+ for threshold in thresholds:
43
+ y_pred = (y_scores >= threshold).astype(int)
44
+
45
+ # Compute metrics
46
+ tp = np.sum((y_pred == 1) & (y_true == 1))
47
+ fp = np.sum((y_pred == 1) & (y_true == 0))
48
+ fn = np.sum((y_pred == 0) & (y_true == 1))
49
+ tn = np.sum((y_pred == 0) & (y_true == 0))
50
+
51
+ precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
52
+ recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
53
+ f1 = 2 * (precision * recall) / (precision +
54
+ recall) if (precision + recall) > 0 else 0.0
55
+ balanced_accuracy = (tpr[np.argmax(thresholds >= threshold)] +
56
+ (1 - fpr[np.argmax(thresholds >= threshold)])) / 2
57
+
58
+ score_map = {
59
+ "f1": f1,
60
+ "precision": precision,
61
+ "recall": recall,
62
+ "balanced_accuracy": balanced_accuracy
63
+ }
64
+
65
+ score = score_map.get(metric, f1)
66
+
67
+ if score > best_score:
68
+ best_score = score
69
+ best_threshold = threshold
70
+ best_metrics = {
71
+ "threshold": threshold,
72
+ "precision": precision,
73
+ "recall": recall,
74
+ "f1": f1,
75
+ "balanced_accuracy": balanced_accuracy,
76
+ "tp": int(tp),
77
+ "fp": int(fp),
78
+ "tn": int(tn),
79
+ "fn": int(fn)
80
+ }
81
+
82
+ return best_threshold, best_metrics
83
+
84
+
85
+ def train_epoch(model: nn.Module, dataloader: DataLoader, criterion: nn.Module,
86
+ optimizer: optim.Optimizer, device: str) -> float:
87
+ """Train for one epoch."""
88
+ model.train()
89
+ total_loss = 0.0
90
+ num_batches = 0
91
+
92
+ for inputs, labels in dataloader:
93
+ pixel_values = inputs["pixel_values"].to(device)
94
+ labels = labels.to(device)
95
+
96
+ # Forward pass
97
+ optimizer.zero_grad()
98
+ logits = model(pixel_values=pixel_values)
99
+ loss = criterion(logits, labels)
100
+
101
+ # Backward pass
102
+ loss.backward()
103
+ optimizer.step()
104
+
105
+ total_loss += loss.item()
106
+ num_batches += 1
107
+
108
+ return total_loss / num_batches if num_batches > 0 else 0.0
109
+
110
+
111
+ def evaluate(model: nn.Module, dataloader: DataLoader, device: str) -> tuple[np.ndarray, np.ndarray]:
112
+ """Evaluate model and return predictions and labels."""
113
+ model.eval()
114
+ all_proba = []
115
+ all_labels = []
116
+
117
+ with torch.no_grad():
118
+ for inputs, labels in dataloader:
119
+ pixel_values = inputs["pixel_values"].to(device)
120
+ labels = labels.to(device)
121
+
122
+ # Get predictions
123
+ proba = model.predict_proba(pixel_values)
124
+ all_proba.append(proba.cpu().numpy())
125
+ all_labels.append(labels.cpu().numpy())
126
+
127
+ proba = np.vstack(all_proba)
128
+ labels = np.concatenate(all_labels)
129
+
130
+ return proba, labels
131
+
132
+
133
+ def train(
134
+ num_epochs: int = 10,
135
+ batch_size: int = 32,
136
+ learning_rate: float = 1e-3,
137
+ weight_decay: float = 1e-4,
138
+ class_weight: str = "balanced"
139
+ ):
140
+ """
141
+ Train DISCO model using PyTorch.
142
+
143
+ Args:
144
+ num_epochs: Number of training epochs
145
+ batch_size: Batch size for training
146
+ learning_rate: Learning rate for optimizer
147
+ weight_decay: Weight decay (L2 regularization)
148
+ class_weight: Class weighting strategy ("balanced" or None)
149
+ """
150
+ print("=" * 60)
151
+ print("DISCO Model Training (PyTorch)")
152
+ print("=" * 60)
153
+
154
+ # Setup device
155
+ device = "mps" if torch.backends.mps.is_available() else (
156
+ "cuda" if torch.cuda.is_available() else "cpu")
157
+ print(f"\nUsing device: {device}")
158
+
159
+ # Load dataset splits
160
+ print("\n[1/6] Loading dataset splits...")
161
+ dataset = get_dataset()
162
+
163
+ train_paths = [str(Path(img_path))
164
+ for img_path in dataset["train"]["image"]]
165
+ val_paths = [str(Path(img_path)) for img_path in dataset["val"]["image"]]
166
+ test_paths = [str(Path(img_path)) for img_path in dataset["test"]["image"]]
167
+
168
+ train_labels = np.array(dataset["train"]["label"])
169
+ val_labels = np.array(dataset["val"]["label"])
170
+ test_labels = np.array(dataset["test"]["label"])
171
+
172
+ print(f" Train: {len(train_paths)} images")
173
+ print(f" Val: {len(val_paths)} images")
174
+ print(f" Test: {len(test_paths)} images")
175
+
176
+ # Load CLIP processor
177
+ print("\n[2/6] Loading CLIP processor...")
178
+ model_name = "openai/clip-vit-base-patch32"
179
+ processor = CLIPProcessor.from_pretrained(model_name)
180
+ print(f" Model: {model_name}")
181
+
182
+ # Create datasets and dataloaders
183
+ print("\n[3/6] Creating datasets and dataloaders...")
184
+ train_dataset = ImageDataset(train_paths, train_labels, processor)
185
+ val_dataset = ImageDataset(val_paths, val_labels, processor)
186
+ test_dataset = ImageDataset(test_paths, test_labels, processor)
187
+
188
+ train_loader = DataLoader(
189
+ train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
190
+ val_loader = DataLoader(
191
+ val_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
192
+ test_loader = DataLoader(
193
+ test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
194
+
195
+ # Initialize model
196
+ print("\n[4/6] Initializing model...")
197
+ config = DISCOConfig(
198
+ clip_model_name=model_name,
199
+ num_classes=2,
200
+ threshold=0.5
201
+ )
202
+ model = DISCO(config).to(device)
203
+
204
+ # Only train the classifier, keep CLIP frozen
205
+ optimizer = optim.AdamW(
206
+ model.classifier.parameters(),
207
+ lr=learning_rate,
208
+ weight_decay=weight_decay
209
+ )
210
+
211
+ # Setup loss function with class weights if needed
212
+ if class_weight == "balanced":
213
+ # Compute class weights from training data
214
+ class_counts = np.bincount(train_labels)
215
+ total = len(train_labels)
216
+ class_weights = torch.tensor([
217
+ total / (2 * class_counts[0]),
218
+ total / (2 * class_counts[1])
219
+ ], dtype=torch.float32).to(device)
220
+ criterion = nn.CrossEntropyLoss(weight=class_weights)
221
+ print(f" Using balanced class weights: {class_weights.cpu().numpy()}")
222
+ else:
223
+ criterion = nn.CrossEntropyLoss()
224
+ print(" Using uniform class weights")
225
+
226
+ print(
227
+ f" Trainable parameters: {sum(p.numel() for p in model.classifier.parameters() if p.requires_grad):,}")
228
+
229
+ # Training loop
230
+ print("\n[5/6] Training model...")
231
+ best_val_f1 = 0.0
232
+ best_model_state = None
233
+
234
+ with Progress(
235
+ SpinnerColumn(),
236
+ TextColumn("[progress.description]{task.description}"),
237
+ BarColumn(),
238
+ TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
239
+ TimeElapsedColumn(),
240
+ console=None,
241
+ ) as progress:
242
+ train_task = progress.add_task("Training", total=num_epochs)
243
+
244
+ for epoch in range(num_epochs):
245
+ # Train
246
+ train_loss = train_epoch(
247
+ model, train_loader, criterion, optimizer, device)
248
+
249
+ # Validate
250
+ val_proba, val_labels_np = evaluate(model, val_loader, device)
251
+ val_scores = val_proba[:, 1]
252
+ val_roc_auc = roc_auc_score(val_labels_np, val_scores)
253
+
254
+ # Compute F1 at default threshold
255
+ val_pred = (val_scores >= 0.5).astype(int)
256
+ tp = np.sum((val_pred == 1) & (val_labels_np == 1))
257
+ fp = np.sum((val_pred == 1) & (val_labels_np == 0))
258
+ fn = np.sum((val_pred == 0) & (val_labels_np == 1))
259
+ precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
260
+ recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
261
+
262
+ val_f1 = 2 * (precision * recall) / (precision +
263
+ recall) if (precision + recall) > 0 else 0.0
264
+
265
+ progress.update(train_task, advance=1, description=f"Epoch {epoch+1}/{num_epochs} | Loss: {train_loss:.4f} | "
266
+ f"Val ROC-AUC: {val_roc_auc:.4f} | Val F1: {val_f1:.4f}")
267
+
268
+ # Save best model
269
+ if val_f1 > best_val_f1:
270
+ best_val_f1 = val_f1
271
+ best_model_state = model.state_dict().copy()
272
+
273
+ # Load best model
274
+ if best_model_state is not None:
275
+ model.load_state_dict(best_model_state)
276
+ print(f"\n Best validation F1: {best_val_f1:.4f}")
277
+
278
+ # Tune threshold on validation set
279
+ print("\n[6/6] Tuning threshold on validation set...")
280
+ val_proba, val_labels_np = evaluate(model, val_loader, device)
281
+ val_scores = val_proba[:, 1]
282
+ best_threshold, threshold_metrics = tune_threshold(
283
+ val_labels_np, val_scores, metric="f1")
284
+ print(f" Best threshold: {best_threshold:.4f}")
285
+ print(" Validation metrics at best threshold:")
286
+ print(f" Precision: {threshold_metrics['precision']:.4f}")
287
+ print(f" Recall: {threshold_metrics['recall']:.4f}")
288
+ print(f" F1: {threshold_metrics['f1']:.4f}")
289
+ print(
290
+ f" Balanced Accuracy: {threshold_metrics['balanced_accuracy']:.4f}")
291
+
292
+ # Update model threshold
293
+ model.threshold = best_threshold
294
+ config.threshold = best_threshold
295
+
296
+ # Evaluate on test set
297
+ print("\n" + "=" * 60)
298
+ print("Test Set Evaluation")
299
+ print("=" * 60)
300
+
301
+ test_proba, test_labels_np = evaluate(model, test_loader, device)
302
+ test_scores = test_proba[:, 1]
303
+ test_roc_auc = roc_auc_score(test_labels_np, test_scores)
304
+ test_pr_auc = average_precision_score(test_labels_np, test_scores)
305
+
306
+ print("\nTest Set Metrics (probability scores):")
307
+ print(f" ROC AUC: {test_roc_auc:.4f}")
308
+ print(f" PR AUC: {test_pr_auc:.4f}")
309
+
310
+ # Apply best threshold
311
+ test_pred = (test_scores >= best_threshold).astype(int)
312
+
313
+ print(f"\nTest Set Metrics (with threshold={best_threshold:.4f}):")
314
+ print(classification_report(test_labels_np, test_pred,
315
+ target_names=["FAMILY_SAFE/UNCERTAIN", "SUGGESTIVE"]))
316
+
317
+ # Confusion matrix
318
+ tp = np.sum((test_pred == 1) & (test_labels_np == 1))
319
+ fp = np.sum((test_pred == 1) & (test_labels_np == 0))
320
+ tn = np.sum((test_pred == 0) & (test_labels_np == 0))
321
+ fn = np.sum((test_pred == 0) & (test_labels_np == 1))
322
+
323
+ print("\nConfusion Matrix:")
324
+ print(" Predicted")
325
+ print(" FAMILY_SAFE SUGGESTIVE")
326
+ print(f"Actual FAMILY_SAFE {tn:4d} {fp:4d}")
327
+ print(f" SUGGESTIVE {fn:4d} {tp:4d}")
328
+
329
+ # Save model and metadata
330
+ print("\n" + "=" * 60)
331
+ print("Saving Model")
332
+ print("=" * 60)
333
+
334
+ models_dir = Path(__file__).parent.parent / "models"
335
+ models_dir.mkdir(exist_ok=True)
336
+
337
+ # Save Hugging Face model
338
+ config.save_pretrained(models_dir)
339
+ model.save_pretrained(models_dir)
340
+ print(f" Saved Hugging Face model to: {models_dir}")
341
+
342
+ # Save processor
343
+ processor.save_pretrained(models_dir)
344
+ print(f" Saved processor to: {models_dir}")
345
+
346
+ # Save metadata
347
+ metadata = {
348
+ "model_name": model_name,
349
+ "threshold": float(best_threshold),
350
+ "test_roc_auc": float(test_roc_auc),
351
+ "test_pr_auc": float(test_pr_auc),
352
+ "val_roc_auc": float(roc_auc_score(val_labels_np, val_scores)),
353
+ "val_pr_auc": float(average_precision_score(val_labels_np, val_scores)),
354
+ "threshold_metrics": {
355
+ k: float(v) if isinstance(v, (int, float, np.number)) else v
356
+ for k, v in threshold_metrics.items()
357
+ },
358
+ "embedding_dim": int(model.clip_model.config.projection_dim),
359
+ "model_type": "clip_nsfw_detector",
360
+ "framework": "pytorch",
361
+ "training_config": {
362
+ "num_epochs": num_epochs,
363
+ "batch_size": batch_size,
364
+ "learning_rate": learning_rate,
365
+ "weight_decay": weight_decay,
366
+ "class_weight": class_weight
367
+ }
368
+ }
369
+
370
+ metadata_path = models_dir / "model_metadata.json"
371
+ with open(metadata_path, "w") as f:
372
+ json.dump(metadata, f, indent=2)
373
+ print(f" Saved metadata to: {metadata_path}")
374
+
375
+ print("\nModel saved successfully!")
376
+ print(f"\nModel is ready for Hugging Face upload from: {models_dir}")
377
+
378
+ return {
379
+ "model": model,
380
+ "threshold": best_threshold,
381
+ "test_roc_auc": test_roc_auc,
382
+ "test_pr_auc": test_pr_auc,
383
+ "threshold_metrics": threshold_metrics,
384
+ "metadata_path": metadata_path
385
+ }
386
+
387
+
388
+ if __name__ == "__main__":
389
+ results = train()
uv.lock ADDED
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