Validation accuracy for mobilenetv3_small
sjzhuo opened this issue · comments
I downloaded the small model and tested it, but only got accuracy 64.926%. Not sure is it because of preprocessing difference? Here is what I used:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=128, shuffle=False,
num_workers=1, pin_memory=True)
load the model will throw some errors, can you show me the codes that how to load the model ?thanks
I just use the DALI by Nvidia to read the dataset. For validation, I resize the short size to 256 and do center crop. Actually, I think it should be no difference. Maybe I will reproduce the model by dataloader in pytorch.
I downloaded the small model and tested it, but only got accuracy 64.926%. Not sure is it because of preprocessing difference? Here is what I used:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize, ])), batch_size=128, shuffle=False, num_workers=1, pin_memory=True)
Hi, I tried your preprocessing parameters but the accuracy remains very low. Could you share your source code and whole settings for validation? Thanks.