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Accessibility 1. NVIDIA has provided a quick way to get you up and running with object detection using DIGITS. The new version of DIGITS includes an example neural network model architecture called DetectNet.
There is a very good blog post about this network called DetectNet: Deep Neural Network for Object Detection in DIGITS. The detection network architecture is based around a Fully-Convolutional Network FCN and is implemented in the Caffe framework.
For training, there are three important processes:. For training, input data can consist of images or video frame images that contain multiple objects.
For each object in the image the training label includes the class of the object and the coordinates of the corners of its bounding box. For each image, there needs to be an associated text file that includes a fixed 3-dimensional label format that enables the network to ingest images of any size with a variable number of objects present.
The NVIDIA DetectNet implementation uses the KITTI data format. This format along with the KITTI dataset can be downloaded here.
This dataset is useful for training your first FCN. The process used by the detection network to ingest labeled training images can be understood by visualized by considering a rectangular grid overlaid on top of the input image.
The grid box spacing should be slightly smaller than the smallest object i. This is a key concept when designing your detection network.
You will need to adjust the appropriate parameters to best match the range of object sizes you want to detect.
Of course, the input image resolution will also play a role in this decision. You can experiment with downsampling and up-sampling original images to determine the best detection performance for a specific target size.
You can imagine the detection network providing two pieces of information: the class of object in each grid square and the pixel coordinates of the corners of the bounding box of that object relative to the center of the grid square.
When no object is present in the grid square a "dontcare" class is used. Please clone the repo and follow the tutorial step by step for better understanding.
Note : The code snippets in this article highlight only a part of the actual script, please refer to the GitHub repo for complete code.
We build our FCN model by stacking convolution blocks consisting of 2D convolution layers Conv2D and the required regularization Dropout and BatchNormalization.
Regularization prevents overfitting and helps in quick convergence. We also add an activation layer to incorporate non-linearity.
Since the height and width of our input images are variable, we specify input shape as None, None, 3. The 3 is for the number of channels in our image which is fixed for colored images RGB.
If the input image size is too small then we might fall short of the minimum required height and width which should be greater than or equal to the kernel size for the next convolution block.
A trial and error way to determine the minimum input dimension is as follows:. After finding the minimum input dimension, we now need to pass the output of the last convolution block to the fully connected layers.
However, any input that has dimension greater than the minimum input dimension needs to be pooled down to satisfy the condition in step 4.
We understand how to do that using our main ingredient. The fully connected layers FC layers are the ones that will perform the classification tasks for us.
There are two ways in which we can build FC layers:. If we want to use dense layers then the model input dimensions have to be fixed because the number of parameters, which goes as input to the dense layer, has to be predefined to create a dense layer.
The number of filters is always going to be fixed as those values are defined by us in every convolution block. However, the input to the last layer Softmax activation layer , after the 1x1 convolutions, must be of fixed length number of classes.
The code includes dense layers commented out and 1x1 convolutions. After building and training the model with both the configurations here are some of my observations:.
The third point cannot be generalized because it depends on factors such as number of images in the dataset, data augmentation used, model initialization, etc.
However, these were the observations in my experiments. The flowers dataset being used in this tutorial is primarily intended to understand the challenges that we face while training a model with variable input dimensions.
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