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could you please help me with following problem: I used TF2 for MaskRCNN. The first cell gives such error. I did it looking here

first cell

!apt-get update
!pip3 install scikit-image==0.16.2
!pip3 install opencv-python
!pip3 install tensorflow==2.2.0
!pip3 install keras==2.3.1

ERROR

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
xarray-einstats 0.4.0 requires scipy>=1.6, but you have scipy 1.4.1 which is incompatible.
plotnine 0.8.0 requires scipy>=1.5.0, but you have scipy 1.4.1 which is incompatible.
jaxlib 0.3.25+cuda11.cudnn805 requires scipy>=1.5, but you have scipy 1.4.1 which is incompatible.
jax 0.3.25 requires scipy>=1.5, but you have scipy 1.4.1 which is incompatible.
google-api-core 2.11.0 requires google-auth<3.0dev,>=2.14.1, but you have google-auth 1.35.0 which is incompatible.

screenshot of this error

Then I run the same cell again without changing anything and it works without error. I can’t understand why it does this and why it doesn’t work the first time, but the second.

By the way I tried to download the necessary packages indicating the version, anyway, the second cell worked without error on the second restart, I also tried not to download the packages at all and they were by default in Colab there, the same story, the second cell worked without error on the second restart.

and in the same project I have another problem and if they are related please tell me. I can't figure out why Google Collab doesn't use the GPU to train the model. The training for epoch 2 lasted 2 hours. Colab uses only RAM and no GPU, although I indicated Runtime type - GPU.

The configuration is like this and I have 300 images for training with size 640*480.

class CbcConfig(mrcnn.config.Config):
    NAME = "cbc_cfg"

    # Train on 1 GPU and 2 images per GPU. We can put multiple images on each
    # GPU because the images are small. Batch size is 2 (GPUs * images/GPU).
    GPU_COUNT = 1
    IMAGES_PER_GPU = 2
        # number of classes (background + 3 blood cells)
    NUM_CLASSES = 1+3
    
    LEARNING_RATE = 0.001

    STEPS_PER_EPOCH = 100
Fatikha Work3
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