An artificial neural network implemented with Tensorflow 2. Contains only conventional layers. Network trained on 6000 images (75 x 75 px) from Oxford Flowers 102.
To start model training type command:
python train.py
-h, --help show this help message and exit
-e EPOCHS, --epochs EPOCHS Number of training epochs
--path PATH Models directory path
--name NAME Model name
-bs BATCH_SIZE, --batch-size BATCH_SIZE Batch size
-f {RGB,HSV,LAB}, --format {RGB,HSV,LAB} Output image format
-da, --data-ag Data agumentation
To test pretrained models type command:
python test.py
-h, --help show this help message and exit
--path PATH Models directory path
--name NAME Model name
-bs BATCH_SIZE, --batch-size BATCH_SIZE Batch size
During the training open next command line and type, to start Tensorboard:
tensorboard --logdir models/<Your model name><Image format>/logs
Then open url localhost:6006/ to see generated summaries.
The jupyter-notebook examples.ipynb contains code, that allows to test pretrained model on images given by user.
To start jupyter server type:
jupyter-notebook examples.ipynb
| Original image | Input Image |
|---|---|
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| RGB | HSV | LAB |
|---|---|---|
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