[MYAI Studio SDK] Image-Object-Detection-MobileNetV3-SSD512-PyTorch-Jupyter

Object detection using MobileNetV3 can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, and mask image analysis.


The main process is:

Annotate images -> Generate files needed for training -> Training -> Inference

1. 1_annotation_pascal_voc_xml.ipynb

Open the webpage for image annotation.


--port 8801 is the port used by the webpage. If the port is occupied by the user, please change another port value by yourself.

2. 2_prepare_train_txt.ipynb

The path file that generates the training image after running is one of the files needed for training.

3. 3_prepare_valid_txt.ipynb

The path file that generates the verification image after running is one of the files required for training.

4. 4_prepare_label_txt.ipynb

Generate label category name file, which is one of the files required for training.

The category name file comes from image_annotation_classes.txt in data/train/annottions.

5. 5_delete_log.ipynb

Delete the log file folder.

6. 6_train.ipynb

Start training.


Before using tensorboard, close the old tensorboard first.

8. 8_tensorboard.ipynb

Open tensorboard to check the training status.

9. 9_inference.ipynb

Inferring a single image.


10. 10_inference_folder.ipynb

Infer all images in the folder.

11. 11_inference_folder_1.ipynb

Infer all images in the folder, and judge whether it is overkill or underkill based on the file name.

This SDK is built in AppForAI - AI Dev Tools.

Purchase license separately: USD 600, permanent authorization, single APP authorization, single machine authorization, one-year activation, one-year download, one-year update, one-year email technical support.

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