<jupyter_start><jupyter_text>**Mask R-CNN: Instance segmentation for urine sticks**<jupyter_code>from google.colab import drive
drive.mount('/content/gdrive')
%cd /content/gdrive/My Drive/MRCNN/
!ls
!pip3 install -r requirements.txt
!python3 setup.py install
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))<jupyter_output>Num GPUs Available:  1<jupyter_text>**Start Training on stick datasat**<jupyter_code>!python3 ./urinestick/urinestick.py train --dataset="/content/gdrive/My Drive/MRCNN/dataset" --weights=coco<jupyter_output>/content/gdrive/My Drive/MRCNN
/content/gdrive/My Drive/MRCNN
2021-01-06 17:10:29.002158: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
Weights:  coco
Dataset:  /content/gdrive/My Drive/MRCNN/dataset
Logs:  /content/gdrive/My Drive/MRCNN/logs

Configurations:
BACKBONE                       resnet101
BACKBONE_STRIDES               [4, 8, 16, 32, 64]
BATCH_SIZE                     1
BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]
COMPUTE_BACKBONE_SHAPE         None
DETECTION_MAX_INSTANCES        100
DETECTION_MIN_CONFIDENCE       0.95
DETECTION_NMS_THRESHOLD        0.3
FPN_CLASSIF_FC_LAYERS_SIZE     1024
GPU_COUNT                      1
GRADIENT_CLIP_NORM             5.0
IMAGES_PER_GPU                 1
IMAGE_CHANNEL_COUNT            3
IMAGE_MAX_DIM                  1024
IMAGE_META_SIZE                14
IMAGE_MIN_DIM                  512
IMAGE_MIN_SCALE                0
IMAGE_RESIZE_MODE              square
IMAGE_S[...]
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