When I detect the object, the terminal displays object class rate(on the top picture).
The imagenet.py is on bottom picture.Can I use the imagenet.py to change terminal’s inormation ?
Not sure which source do you use.
But you can update the output from the source code like this:
train_dataset = datasets.ImageFolder(
num_classes = len(train_dataset.classes)
print('=> dataset classes: ' + str(num_classes) + ' ' + str(train_dataset.classes))
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
Tha labels are car,doormax,doormid, and doormin on googlenet.After training googlenet, I used googlenet to detect object. I make GPIO LED(
https://www.jetsonhacks.com/2019/06/07/jetson-nano-gpio/) with dection.If doormin and doormid recognition rate are closely, I want two leds light togehter.