I need to train a set of images that have already been labeled for object detection. The images have different resolutions, so I am wondering if it is possible to train them using the object detection API. Would specifying the min_dimension as the greatest width and max_dimension as the smallest height in the config file allow me to train the model successfully despite the varying sizes of the images?
It is not mandatory for all images to be the same size when utilizing the TensorFlow object detection API.
There is no need to resize the training images as the script handles it automatically. The configuration file internally resizes images to fit the specified dimensions.
Below is an example of a ssdmobilenet config file:
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
The image resizer will adjust all images to 300x300 for training.
If you aim for higher accuracy, you can modify these values in your configuration file, but note that doing so will increase the training time:
image_resizer {
fixed_shape_resizer {
height: 600
width: 800
}
Ensure that any values added to the image resizer are equal to or greater than the dimensions specified in the image resizer. Otherwise, you may encounter tensor shape mismatch errors.
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