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understand google object detection API

在 Model Garden garden 裡大致有分official 和 research ,official 裡的object detection API 似乎主要是從頭開始train(還不確定model 是否會根據input image 而變),是直接用python 去執行script。由於我需要fine tune...,等多變化,自己設定執行步驟,所以不是用official 裡的東西

research 裡有各種model (Model Zoo),執行到一半的checkpoint,model config。適合做各種train 方法,步驟的更換,以jupyter notebook 為主,這才是我要用的。

train

python3 "/mnt/c/Users/insleker/Google Drive/workspace/riceCV/models/research/object_detection/model_main_tf2.py" --pipeline_config_path=./pipeline.config --model_dir=. --alsologtostderr

eval

python3 "/mnt/c/Users/insleker/Google Drive/workspace/riceCV/models/research/object_detection/model_main_tf2.py" --pipeline_config_path=./pipeline.config --model_dir=. --checkpoint_dir=. --alsologtostderr

然後開tensorboar

tensorboard --logdir=.

export

TensorFlow Object Detection API: Best Practices to Training, Evaluation & Deployment

python3 "/mnt/c/Users/insleker/Google Drive/workspace/riceCV/models/research/object_detection/exporter_main_v2.py" --pipeline_config_path=./pipeline.config --trained_checkpoint_dir=. --output_directory="../exported_models/?" --input_type=image_tensor

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