In YOLO 2, the training is done on different image sizes starting from ______ to ________*1 point32x32 , 512x51264x64 , 1024x 102432x320 , 608x680320x320 ,608x608

Which of the Following is feature of YOLO algorithm*1 pointSingle CNN simultaneously predicts the bounding boxes and class probabilities for these boxes.Extremely fast. (Base network runs at 45fps with no batch processing)You only(Network) looks at the input image and predicts what objects are presentAll the above

Suppose you are using YOLO on a 19×19 grid, on a detection problem with 20 classes, and with 5 anchor boxes. During training, for each image you will need to construct an output volume y as the target value for the neural network; this corresponds to the last layer of the neural network. What is the dimension of this output volume?            a.19x19x(5×20)b.19x19x(5×25)c.19x19x(25×20)d.19x19x(20×25)

In YOLO grid size is 17x17, the number of anchor boxes are 5 and able to detect 80 classes .What will be the size of the output layer.*1 point2427632946476852250

A 512×512512×512 raw grayscale image with each pixel value ranging from 0 to 255 requires approximately _____ kb of memory storage.

# The resolution of the resized images is 64 x 64 img_resolution = (64, 64) # We can create an iterater using the dataloaders and take a random sample images, labels = next(iter(trainloader)) # You may need to reshape the images batch to 16 x 1 x 28 x 28 ??? # Visualize dataset using torchvision grid ???