This solution can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, mask image analysis, etc.
[Instructions]
1_annotation_pascal_voc_xml.ipynb
Open the marking software. Prepare a png or jpg image for annotation. It is recommended that the image has the same aspect ratio.
2_delete_log.ipynb
Delete the log folder, which is the record read by tensorboard during training.
3_prepare_train_txt.ipynb
Prepare a list of training images.
- image_path = "data/train/images": Training image path.
- txt = "data/train.txt": The output training image list.
4_prepare_val_txt.ipynb
To prepare the verification image list.
- image_path = "data / train / images": verification image path.
- txt = "data/train.txt": The output verification image list.
5_create_tfrecord.ipynb
First set the category name to data/labels.txt, the first category name is background, and the second and below are the category names of your samples.
The category name must be exactly the same as the category name when labeling.
Then, run this ipynb to convert the data in data/train and data/val into tensorflow's tfrecord format.
6_train.ipynb
Train the model.
--num_epochs=100000: The number of training periods. num_classes=3: Set the number of categories.
7_kill_tensorboard.ipynb
Release the tensorboard that is not in this training.
8_tensorboard.ipynb
Turn on tensorboard to display the loss curve and other related information during training.
9_inference.ipynb
Infer a picture.
--ckpt_path=model/efficientdet-d0-pcb : Inferred model.
--input_image_size=512 : Image size.
--input_image=data/test/images/capacitor1-4.png : Inferred images.
--num_classes=3 : The number of categories of the model.
10_inference_folder.ipynb
Infer the images in the folder.
--input_image=data/train/images/ : Inference folder.
11_inference_folder1.ipynb
Infer the images in the folder and calculate the correct rate.
Whether it is correct or not is to compare the detected category name with the image file name.
The image file name format is: category name-xxx.png.
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.