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script.sh
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#!/bin/bash
DETECTOR_WEIGHTS=./model/dmpr_pretrained_weights.pth
TEST_DIRECTORY=./datasets/parking_slot/testing/all
LABEL_DIRECTORY=./datasets/parking_slot/ps_json_label/testing/all
IMAGE_DIRECTORY=./datasets/parking_slot/testing/all
python inference.py --mode image --detector_weights ./model/dmpr_pretrained_weights.pth --inference_slot
python evaluate.py --dataset_directory ./datasets/parking_slot/ps_json_label/testing/all --detector_weights ./model/dmpr_pretrained_weights.pth
python ps_evaluate.py --label_directory ./datasets/parking_slot/ps_json_label/testing/all --image_directory ./datasets/parking_slot/testing/all --detector_weights ./model/dmpr_pretrained_weights.pth
python inference.py --mode video --detector_weights ./model/dmpr_pretrained_weights.pth --video ./input.avi --inference_slot
python inference.py --mode video --detector_weights ./model/dmpr_pretrained_weights.pth --video ./sample_video_input/cropped_topview_1666775453007.mp4 --inference_slot --save --gpu_id 03
python inference.py --mode video --detector_weights ./weights/dp_detector_11.pth --video ./sample_video_input/topview_1666775453007.mp4 --inference_slot --save --gpu_id 0
# best case
python inference.py --mode image --detector_weights ./weights/dp_detector_11.pth --inference_slot --save
# detect and collect threshold from prediction
# train
python naive_train.py --dataset_directory datasets/parking_slot/annotations/train --gpu_ids 0,1,2,3 \