This repository is forked from https://github.com/MaybeShewill-CV/bisenetv2-tensorflow/
Pourpose of this repository
- generate arbitrary input size model
- retrain more easily
generate arbitrary input size model
I strongly recommend this process in docker container.
docker pull tensorflow/tensorflow:1.15.4-gpu-py3
docker run --rm --gpus all -v /home/<path_to_working_folder>/:/work -ti tensorflow/tensorflow:1.15.4-gpu-py3
CityPass
Not yet
CelebAMask
Downloaded the pretrained model from
here into ./checkpoint
(1) create frozen model
$ export PYTHONPATH=$PYTHONPATH:`pwd`
$ pip install PyYAML
$ python3 tools/celebamask_hq/freeze_celebamaskhq_bisenetv2_model.py \
--weights_path checkpoint/celebamaskhq.ckpt \
--frozen_pb_file_path ./checkpoint/bisenetv2_celebamask_frozen.pb \
--optimized_pb_file_path ./checkpoint/bisenetv2_celebamask_optimized.pb
* In my environment, PyYAML version is 5.3.1
(2) convert to tensorflowjs
$ pip install tensorflowjs
$ tensorflowjs_converter --input_format tf_frozen_model \
--output_node_names final_output \
checkpoint/bisenetv2_celebamask_optimized.pb \
webmodel
* In my environment, tensorflowjs version is 2.7.0
Demo
https://flect-lab-web.s3-us-west-2.amazonaws.com/P01_wokers/t08_bisenetv2-celebamask/index.html
Demo repository
https://github.com/w-okada/image-analyze-workers
retrain more easily
Not yet ...