[MYAI Studio SDK] Image-Object-Detection-VGG16-SSD512-PyTorch-Jupyter

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 /val/images": verification image path.
  • txt = "data/val.txt": The output verification image list.   

 

 

5_train.ipynb

 

Before starting to train the model, please set the number of categories and the category name. 

 

Set the category name to data/labels.txt, the first category name should be fixed __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. 

 

Set NUM_CLASSES in data/vgg_ssd512.yaml, and set other training related parameters here. 

 

Execute this ipynb to train the model. 

 

 

 

6_kill_tensorboard.ipynb

 

Release the tensorboard that is not in this training.   

 

 

 

7_tensorboard.ipynb

 

Turn on tensorboard to display the loss curve and other related information during training. 

 

 

 

8_inference.ipynb

 

Infer a picture. 

 

--image_path "data/val/images/inclusion-2.jpg" : Inferred images. 

--ckpt model/model_007000.pth : Inferred model.

--score_threshold 0.3 : The threshold of inference. 

--debugImage 1 : Switch to display the image of the inference result. 

 

 

 

9_inference_folder.ipynb

 

Infer the images in the folder. 

 

--images_dir data/val/images/ : Inference folder. 

--ckpt model/model_007000.pth : Inferred model.

--score_threshold 0.3 : The threshold of inference. 

--debugImage 1 : Switch to display the image of the inference result. 

 

 

 

10_inference_api.ipynb

11_inference_api_browser.ipynb

 

Inference API, run 10_inference_api.ipynb to open the server, load model/model_007000.pth, and then run 11_inference_api_browser.ipynb, jump out of the browser, you can select the picture for inference. 

 

 

 

inference.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.

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