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setting up retinanet for detection

pip install torchvision pip install matplotlib

Downloading: "https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth" to /home/drew/.cache/torch/hub/checkpoints/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth

self.font = core.getfont(

File "/code/py_env/wff/lib/python3.10/site-packages/PIL/ImageFont.py", line 274, in init self.font = core.getfont( OSError: cannot open resource

Couldn't use Arial

https://stackoverflow.com/questions/65141291/get-a-list-of-all-available-fonts-in-pil

searching for best face detection

https://medium.com/pythons-gurus/what-is-the-best-face-detector-ab650d8c1225

This suggests RetinaFace is very good but they claim it can miss on large faces, which exceed the size of the image causing partial occlusion. Since it can work on partial occulsion, it must be that it just wasn't trained on partial occlusion where it's at the edge of the frame ( or it doesn't pad to allow this )

https://www.codegenes.net/blog/pytorch-retinaface/

recent pytorch retinaface code

searching for "generate embeddings retiaface"

https://github.com/serengil/retinaface

this looked good.

https://github.com/serengil/deepface

this wraps their retinaface and allows for searching.

We'll want a different db to allow clustering.

this uses arcface to recognize then detect using retinaface.

other

clip

pip install open_clip_torch

directions: https://huggingface.co/docs/hub/en/open_clip

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