Please note this config setting only controls the size of the YOLO meta architecture and the size of the feature extractor has nothing to do with this config field. 0, 1 or 2 2 loss_loc_weight, loss_neg_obj_weights, and loss_class_weights Those loss weights can be configured as float ...
First of all, let’s talk about how this network look like at a high-level diagram (Although, the network architecture is the least time-consuming part of implementation). The whole system can be divided into two major components: Feature Extractor and Detector; both are multi-scale. When a...
Different from ResNet, the overall architecture of CSP-Ghost-ResNet refers to CSPNet, and the stacked network is changed to the Ghost module. Figure 6 shows the ResNet and CSP-Ghost-ResNet networks. Figure 6 Network of ResNet and CSP-Ghost-ResNet. (a) represents the network of ResNet,...
Based on the YOLOv3 architecture shown in Fig. 1, a densely connected architecture proposed by Huang et al.30 was incorporated for better feature reuse and representation. This enables more compact and accurate models for detection30. An overview of the modified tomato detection model is shown in...
Part 2 (This one): Creating the layers of the network architecture Part 3 : Implementing the the forward pass of the network Part 4 : Objectness Confidence Thresholding and Non-maximum Suppression Part 5 : Designing the input and the output pipelines ...
Image Credits: Karol Majek. Check out his YOLO v3 real time detection videohere This is Part 3 of the tutorial on implementing a YOLO v3 detector from scratch. In the last part, we implemented the layers used in YOLO's architecture, and in this part, we are going to implement the...
Figure 5. Diagram of a single object reconstruction network architecture branch. For the given depth frame, the depth encoder creates a bottleneck, which is then directly connected to VAE node, the resulting sampler is connected into voxel decoder. The voxel decoder layer outputs a 32×32×32×...