![]() ![]() $\quad$ 4.5 Route Layer / Shortcut Layers ![]() $\quad$ 4.2 Parsing the configuration file Complex Yolo, Yolo3d - YoloV3 / YoloV4ĥ2.1 Creating the layers of the network architecture - EN (Copy)ĥ2.2 Implementing the forward pass of the network - EN (Copy)ĥ2.4 Designing the input and the output pipelines - ENĥ3.1 Creating the layers of the network architecture - ENĥ3.2 Implementing the forward pass of the network - ENĥ3.4 Designing the input and the output pipelines - ENĥ4.1 Creating the layers of the network architecture - ENĥ4.2 Implementing the forward pass of the network - ENĥ4.4 Designing the input and the output pipelines - ENĥ5.1 Creating the layers of the network architecture - ENĥ5.2 Implementing the forward pass of the network - ENĥ5.4 Designing the input and the output pipelines - EN YoloV3 / YoloV4 for ImageĤ2.1 Creating the layers of the network architecture - ENĤ2.2 Implementing the forward pass of the network - ENĤ2.3 Confidence Thresholding and Non-maximum Suppression - ENĤ2.4 Designing the input and the output pipelines - ENĤ2.6 Designing the input and the output pipelines - ENĤ2.C Colab, YOLOv3 VOC Dataset with data engineering finished - ENĤ2.D Colab, YOLOv3 VOC Dataset with RAW data - ENĤ2.E Colab, YOLOv3 COCO 10K Dataset with data engineering finished - ENĤ2.F Colab, YOLOv3 COCO full Dataset with data engineering finished - ENĤ3.1 Creating the layers of the network architecture - ENĤ3.2 Implementing the forward pass of the network - ENĤ3.4 Designing the input and the output pipelines - ENĤ3.C Colab, YOLOv4 VOC Dataset with data engineering done - ENĤ3.D Colab, YOLOv4 VOC Dataset with RAW data - ENĤ3.E Colab, YOLOv4 COCO 10K Dataset with data engineering done - ENĤ3.F Colab, YOLOv4 COCO full Dataset with data engineering done - EN Building Block 2 : V3 ~ V4 Training / Evaluationģ2.02 PANet (Path Aggregation Network) - BoS - ENģ2.06 Class labeling smoothing - BoF - ENģ2.07 CmBN(Cross-Iteration Batch Normalization) - BoF - ENģ3.03 Training Process and Load Yolo Weight - ENģ3.04 Confidence thresholding, NMS mAP and Detection - ENģ3.B YOLOv3 proposed workflow-MNIST+fashion MNIST - EN IMPL_14.C Yolo V3 Training only - Drop Blocks IMPL_14.B Yolo V3 Training only - Focal Loss IMPL_13.B Yolo V2 Training only - Drop Blocks IMPL_12.C Yolo V1 Training only - Drop Blocks IMPL_12.B Yolo V1 Training only - Dense Neck IMPL_11.A Cifar-10 Classification - Modified WorkFlow Building Block 1 : V1 ~ V3 Training Architectureġ2.01 IoU (Intersection over Union) and GIoU - ENġ2.04 Model Architecture : Comparison Cifar-10 vs Yolo V1 - ENģ1.07 Mean Average Precision (mAP) Explained-ENġ3.03 Model Architecture : Yolo V1 vs Yolo V2 - ENġ4.01 Activation Functions : Leaky ReLU, Mish - ENġ4.04 FPN(Feature Pyramid Networks) - Neck - ENġ4.05 Model Architecture : Yolo V2 vs Yolo V3 - EN Viola–Jones object detection framework - EN ![]() ![]() Object Detection Models Explained - ENĠ1. Object Detection in 2022: The Definitive Guide Two-stage methods / R-CNN, FPN, Mask R-CNN - ENġ6. Milestones in state-of-the-art Object Detection - ENġ5. Survey on Deep Learning Object Detection - ENġ3. Operation Name : Hunt for Tough Elephant - ENġ2. A Gentle Introduction to Deep Learning - ENġ0. The Essential Guide to Neural Network Architectures - ENĠ5. Architecture Overview of Deep Learning Bible Series - EN ![]()
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