Learning Day 57/Practical 5: Loss function — CrossEntropyLoss vs BCELoss in Pytorch; Softmax vs sigmoid; Loss calculation

CrossEntropyLoss vs BCELoss

1. Difference in purpose

Learning Day 66: Object detection 5 — YOLO v1, v2 and v3

YOLO v1 (You Only Look Once)

Learning Day 52: Back propagation — a simple example for hand calculation

A simple 2-layer NN for hand…

Learning Day 67: Semantic segmentation 1 — FCN; Deconvolution

Image segmentation

Learning Day 49: Take a break from reading, start practicing — building my own dataset in Pytorch

Learning Day 49/Practical 1: Building my own dataset in Pytorch from HDF5

Learning Day 70: 3D U-Net with 3D convolution layers, V-Net, DenseNet, FC-DenseNet

Learning Day 55: back propagation in CNN — pooling and conv layers

Building the knowledge on top of…

Learning Day 68: Semantic segmentation 2 — DeepLab, atrous/dilated convolution

Background

Learning Day 56/Practical 4: Retrieving raw scores from CNN models

Retrieving raw scores from output layers

Objectives

• To look at the raw scores and determine if the prediction is confident

Learning Day 63: Object detection 2— SPP-Net

SPP-Net

• Improve on the drawbacks of R-CNN: slow…