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I am trying to do my own object detection using my own dataset. I started my first machine learning program from google tensorflow object detection api, the link is here:eager_few_shot_od_training_tf2_colab.ipynb

In the colab tutorial, the author use javascript label the images, the result like this:

 gt_boxes = [
         np.array([[0.436, 0.591, 0.629, 0.712]], dtype=np.float32),
         np.array([[0.539, 0.583, 0.73, 0.71]], dtype=np.float32),
         np.array([[0.464, 0.414, 0.626, 0.548]], dtype=np.float32),
         np.array([[0.313, 0.308, 0.648, 0.526]], dtype=np.float32),
         np.array([[0.256, 0.444, 0.484, 0.629]], dtype=np.float32)

]

When I run my own program, I use labelimg replace to javascript, but the dataset is not compatible.

Now I have two questions, the first one is what is the dataset type in colab tutorial? coco, yolo, voc, or any other? the second is how transform dataset between labelimg data and colab tutorial data? My target is using labelimg to label data then substitute in colab tutorial.

desertnaut
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littlec
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1 Answers1

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  1. The "data type" are just ratio values based on the height and width of the image. So the coordinates are just ratio values for where to start and end the bounding box. Since each image is going to be preprocessed, that is, it's dimensions are changed when fed into the model (batch,height,width,channel) the bounding box coordinates must have the correct ratio as the image might change dimensions from it's original size. Like for the example, the model expects images to be 640x640. So if you provide an image of 800x600 it has to be resized. Now if the model gave back the coordinates [100,100,150,150] for an 640x640, clearly that would not be the same for 800x600 images. However, to get this data format you should use PascalVOC when using labelImg.

  2. The typical way to do this is to create TFRecord files and decode them in your training script order to create datasets. However, you are free to choose whatever method you like Tensorflow dataset in order to train your model.

Hope this answered your questions.

Araw
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