[AppForAI 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. 







Open the marking software. Prepare a png or jpg image for annotation. It is recommended that the image has the same aspect ratio. 






Delete the log folder, which is the record read by tensorboard during training. 






Prepare a list of training images.


  • image_path = "data/train/images": Training image path.
  • txt = "data/train.txt": The output training image list.






To prepare the verification image list.


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





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. 






Release the tensorboard that is not in this training.   






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






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. 






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. 







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. 






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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