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In the case of multiple objects occupying the same number of pixels e. This is a good choice for car based cameras or low elevation angle ground based cameras i.
For each grid square, the detection network predicts whether an object is present and where the bounding box corners for that object are located relative to the center of the grid square.
In order to get the network to perform optimally for your given application, it is important to experiment with several parameters related to the overlay grid size, sensitivity, and bounding box determination.
Also, keep in mind that the variation in your bounding boxes may have a dependency on you input image resolution. You may need to dig into the source code to get a full understanding of how to make those adjustments.
To improve performance, several parameters can be adjusted during training. As mentioned above, the grid spacing and the stride in pixels are used to determine the object size sensitivity.
Input data augmentation parameters can also be adjusted. Augmentation is essential to successfully train for high sensitivity and accurate object detection.
Augmentation is a process that applies random transformations, like pixel shifts and flips, to the training images each time they are input into the training process.
The benefit of using augmentation is that the network never "sees" the same training image twice, so it is more resilient to overfitting.
Training your own FCN involves some patience and effort. However, it is well worth it to gain the insight an intuition needed for more complex models.
It is our experience that training an FCN is easier when only trying to detect a single object class e. Although an FCN can be trained for multiple object classes, it can often be best to just train multiple models and run them in parallel on your desired target dataset.
For this example we will use the NVIDIA DIGITS platform since it has an FCN model called DetectNet built in to the latest versions.
You should not feel restricted to this model, and it can often be useful to make modifications to better suit your intended application.
We have developed several modified version of this general network for both experimentation and application. New Coach Welcome Videos.
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