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.
[Instruction]
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.
parameter:
--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.
7.7_kill_tensorboard.ipynb
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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