[LEADERG AI ZOO] Jupyter-Image-Object-Detection-EfficientDet-Keras

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繁體中文

 

 

 

[Introduction]

 

 

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.   

 

 

inference.png


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