🧠 AI powered image tagger backed by DeepDetect
Because sometimes, you have folders full of badly named pictures, and you want to be able to understand what you have in your hard drive.
Prerequisites & installation
You need DeepDetect installed, the easiest way is using docker:
docker pull beniz/deepdetect_cpu docker run -d -p 8080:8080 beniz/deepdetect_cpu
Right now, the only supported installation of DeepDetect that works with DeepSort is the deepdetect_cpu container,
because it contain the good path for the pre-installed
Then, download the latest DeepSort release from https://github.com/CorentinB/DeepSort/releases
Unzip your release, rename it
DeepSort and make it executable with:
chmod +x DeepSort
DeepSort support few different parameters, you're obliged to fill two of them:
-u that correspond to the URL of your DeepDetect server.
-i that correspond to your local folder full of images.
For more informations, refeer to the helper:
./DeepSort --help [-u|--url] is required usage: deepsort [-h|--help] -u|--url "<value>" -i|--input "<value>" [-o|--output "<value>"] [-n|--network (resnet-50|googlenet)] [-R|--recursive] [-j|--jobs <integer>] [-d|--dry-run] AI powered image tagger backed by DeepDetect Arguments: -h --help Print help information -u --url URL of your DeepDetect instance (i.e: http://localhost:8080) -i --input Your input folder. -o --output Your output folder, if output is set, original files will not be renamed, but the renamed version will be copied in the output folder. -n --network The pre-trained deep neural network you want to use, can be resnet-50 or googlenet. Default: resnet-50 -R --recursive Process files recursively. -j --jobs Number of parallel jobs. Default: 1 -d --dry-run Just classify images and return results, do not apply.
- Getting docker out of the loop (each user install his own DeepDetect)
- ResNet 50 integration
- Output folder (copy and not rename)
- NSFW tagging (Yahoo open_nsfw)
- XMP metadata writing
- GPU support