clipseg VS YOLOX

Following the comments to this old post, I tried to use in-painting with manual mask selection. I didn't get beautiful results but I'm sure with some tweaking, I could make it better. The main problem I had was having to manually select the area where I wanted to place the logo and trying to resize my logo mask to the fit the segment. I tried some automatic segmentation tools (Clipseg and Segment Anything). I couldn't tell the segmentation models to find a good area to for logo placement (i.e. some small flat surface). Given the complexity of what I was dealing with, I think there could be a better way (XY problem).

The addon utilizes clipseg for region masking, which was trained on "an extended version of the PhraseCut dataset" (many thousands of images.)

You'll also need to make sure to update your clipseg repo. The script won't do this for you. Namely you just need to update this models/clipseg.py file to ensure your clipseg has support for the new model.

For localisation at search time I ended up using OWL-ViT. This worked really well. I did not try Detic or CLIPseg but would be interested to hear if anyone else has tried these?

clipseg is an image segmentation method used to find a mask for an image from a prompt. I implemented it as an executor for dalle-flow and added it to my bot yasd-discord-bot.

Automated Replacement (txt2mask) by clipseg

So I am trying to do my best and train YOLOX for an object detection case using Google Colab.

https://github.com/Megvii-BaseDetection/YOLOX I have only tried the pretrained yolo X nano. I get corner responses even if the inference image is padded with a large margin which is unexpected

* Nice implemention of Yolo that is BSD license (not GPL) https://github.com/Megvii-BaseDetection/YOLOX

I wanted to have a few choices getting localization into image search (index and search time). I immediately thought of using a region proposal network (rpn) from mask-rcnn to create patches that can also be indexed and searched (and add the localisation). I figured it might be somewhat agnostic to classes. I did not want to use mmdetection or detectron2 due to their dependencies and just getting the rpn was not worth it. I was encouraged by the PyTorch native implementations of detection/segmentation models but ended up finding yolox the best.

Megvii-BaseDetection/YOLOX

Modern video can be broken into a series of still problems. AI vision models can make these types of classification in as fast as video. Here is a particularly there is a controversial company from China that does this very well on faces in video and they have open sourced the models: https://github.com/Megvii-BaseDetection/YOLOX

Not really a problem, see results here. Just use yolox_x. Thank you for your attention.

From what I understand the swin transformer outputs a single dimension feature vector and the yolo head takes inputs from 3 different layers from the backbone?? and I think I will need to write the backbone implementation here.