Use UNet to segment the image.
[Instructions]
1. 1_annotation_labelme_json.ipynb
Open the labeling tool LabelMe.
Setting parameters:
• image_folder: image folder
• annotation_path: output annotation file path
After the setting is completed, it can be executed.
2. 2_labelme_json_to_dataset.ipynb
Convert LabelMe label files into training datasets.
Setting parameters:
• label_path: label the folder of the file
• output_path: the path of the output file
• mask_path: the path of the mask
After the setting is completed, it can be executed.
3. 3_delete_log.ipynb
Delete the log file.
4. 4_train.ipynb
Perform training.
Setting parameters:
• model_file: the path of the output model file
• epoch_steps: the number of steps in each epoch
• num_epochs: the number of epochs
• log_dir: the path of the log folder
• train_folder: the path of the training folder
After the setting is completed, it can be executed.
5. 5_kill_tensorboard.ipynb
Delete the old tensorboard program.
6. 6_tensorboard.ipynb
Execute tensorboard.
7. 7_inference.ipynb
Perform inferences.
Setting parameters:
• model_file: input model file path
• image_file: Inferred image file path
• predict_file: the path of the inference result file
After the setting is completed, it can be executed.
8. 8_inference_folder.ipynb
Perform inferences on the folder.
Setting parameters:
• model_file: input model file path
• image_file: the path of the inferred image folder
• predict_file: the path of the inference result file
After the setting is completed, it can be executed.
After execution, you can see the results of UNet's inference.
9. 9_inference_api.ipynb
Use API to perform inference.
10. 10_inference_api_browser.ipynb
Open the browser to use the API.
Price: Welcome to contact us for a quote.
Thanks for our customers
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