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In the second phase the pruned network is retrained. Following the first phase, we prune the network removing channels whose kernel norms are below the pruning threshold. In the first phase, the network is trained with regularization to facilitate pruning. The training is carried out in two phases. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. This model was trained using the DetectNet_v2 entrypoint in TAO. The raw normalized bounding-box and confidence detections need to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. These models are based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. Three categories of objects detected by these models are – persons, bags and faces. Continued abuse of our services will cause your IP address to be blocked indefinitely.The models described in this card detect one or more physical objects from three categories within an image and return a box around each object, as well as a category label for each object. Please fill out the CAPTCHA below and then click the button to indicate that you agree to these terms. If you wish to be unblocked, you must agree that you will take immediate steps to rectify this issue. If you do not understand what is causing this behavior, please contact us here. If you promise to stop (by clicking the Agree button below), we'll unblock your connection for now, but we will immediately re-block it if we detect additional bad behavior.
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