I am using pre-trained AlexNet network to validate some prior work.
The code is as follows:
import os
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
model = torch.hub.load('pytorch/vision:v0.6.0', 'alexnet', pretrained=True)
model.eval()
batchsize = 50000
workers = 1
dataset_path = 'data/imagenet_2012/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
val_data = datasets.ImageFolder(root=os.path.join(dataset_path, 'val'), transform=transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize,]))
val_loader = torch.utils.data.DataLoader(val_data, batch_size=batchsize, num_workers=workers)
batch = next(iter(val_loader))
images, labels = batch
with torch.no_grad():
output = model(images)
for i in output:
out_soft = torch.nn.functional.softmax(i, dim=0)
print(int(torch.argmax(out_soft)))
When I execute this and compare with ILSVRC2012_validation_ground_truth.txt, I get top-1 accuracy of 5% only.
What am I doing wrong here?
Thank you.
So, Pytorch/Caffe have their own "ground truth" files, which can be obtained from here: https://gist.github.com/ksimonyan/fd8800eeb36e276cd6f9#note
I manually tested the images in the validation folder of the Imagenet dataset against the val.txt file in the tar file provided at the link above to verify the order.
Update: New validation accuracy based on the groundtruth in the zip file in the link:
Top_1 = 56.522%
Top_5 = 79.066%