NoMoreCatz is an AI-based Chromium plugin, blocking pictures with cats on it.
- Any Chromium-based browser (Chrom, Edge, Brave, Opera, etc).
- 4 core CPU with AVX support.
- Windows 10 x64.
- Download NoMoreCatz achive from here.
- Unpack archive in any folder by you choice (please - no spaces in path, as well as national chars).
- Open browser.
- Select "Extensions" from browser settings.
- Enable "Developer mode" switch on Extension page.
- Three buttons appears, press "Load Unpacked" one.
- Select folder "plugin" inside the folder you unpack archive and press "Open Folder".
- Extention named "NoMoreCatz" should be added into extensions list.
- At the NoMoreCatz extension card, find the field "ID:" followed with the text string. Copy it or write it down.
- Open command line at the folder you unpack archive.
- Issue command
nomorecatz -register <ID string you write down from extenstion card> - Done!
- Download and unpack archive to the same folder as before, overriting all files.
- Open browser, select "Extensions" from browser settings.
- At extension page, press "Reload" (circle) button at NoMoCatz extension card.
- Done!
Every picture browser download transferred to host application for check.
If cat detected on the picture, plugin block it and replace to dummy picture.
Depends of building variant, host application uses full TensorFlow 2.3 (you need to download it sepatarely, it's HUGE) or TensorFlowLite to check picture data against trained model.
For Tensorlow, it uses faster-rcnn model trained by Coco, Cat vs Dogs and Pets datasets.
For TensorflowLite, SSD-resnet model, trained by Coco dataset used. Unfortunately, it's not possible to train model using other datasets - TensorFlowLite conversion broken beyond repair in Tensorflow 2.3, so i need to wait until 2.4 release (i hope they fix it).
You can change blocking type into /plugin/background.js script - it have variable for it at the beginning. Don't forget to reload extenstion after you modify it.
Also, you can alter CUT_LEVEL variable into the same script - it defines minimal probability acceptable as a positive result. Value is between 0.0 (any) and 1.0 ( 100% only).
- AI blocking is about probability, so it can block approximately 90% of images (for full TensorFlow) or even 70% (for TensorflowLite). However, some images can be blocked by mistake (about 3% probability, depends on cut level).
- If many images loaded from page, blocking will be delayed die to heavy load. Common blocking time is about 300ms (for Lite), for full Tensorflow delay can be up to 4 seconds, but if many images processed at the same time it can be about 5-6 seconds (for lite) and 20-30 seconds for full.
- Full Tensorflow version require about 1Gb of RAM per process, so 16+Gb is reccomended. Lite version only uses around 100M per process, so lonly Lite version packed in binary.
- If full version compiled with GPU support, it also require CUDA 10.1 + CuDNN installed, and Nvidia GPU with at least 2Gb of VRAM.