Getting model class labels from torchvision pretrained models
I am using a pre-trained Alexnet model (without fine-tuning) from torchvision. The issue is that even though I am able to run the model on some data and get the output probability distribution, I am unable to find class labels to map it to.
Following this official documentation
import torch
model = torch.hub.load('pytorch/vision:v0.6.0', 'alexnet', pretrained=True)
model.eval()

AlexNet(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(inplace=True)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(inplace=True)
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(inplace=True)
    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(inplace=True)
    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
  (classifier): Sequential(
    (0): Dropout(p=0.5, inplace=False)
    (1): Linear(in_features=9216, out_features=4096, bias=True)
    (2): ReLU(inplace=True)
    (3): Dropout(p=0.5, inplace=False)
    (4): Linear(in_features=4096, out_features=4096, bias=True)
    (5): ReLU(inplace=True)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

Following some steps on processing the image, I am able to use it to get an output for a single image as a (1,1000) dim vector which I will use a softmax on to get a probability distribution -
#Output - 

tensor([-1.6531e+00, -4.3505e+00, -1.8172e+00, -4.2143e+00, -3.1914e+00,
         3.4163e-01,  1.0877e+00,  5.9350e+00,  8.0425e+00, -7.0242e-01,
        -9.4130e-01, -6.0822e-01, -2.4097e-01, -1.9946e+00, -1.5288e+00,
        -3.2656e+00, -5.5800e-01,  1.0524e+00,  1.9211e-01, -4.7202e+00,
        -3.3880e+00,  4.3048e+00, -1.0997e+00,  4.6132e+00, -5.7404e-03,
        -5.3437e+00, -4.7378e+00, -3.3974e+00, -4.1287e+00,  2.9064e-01,
        -3.2955e+00, -6.7051e+00, -4.7232e+00, -4.1778e+00, -2.1859e+00,
        -2.9469e+00,  3.0465e+00, -3.5882e+00, -6.3890e+00, -4.4203e+00,
        -3.3685e+00, -5.0983e+00, -4.9006e+00, -5.5235e+00, -3.7233e+00,
        -4.0204e+00,  2.6998e-01, -4.4702e+00, -5.6617e+00, -5.4880e+00,
        -2.6801e+00, -3.2129e+00, -1.6294e+00, -5.2289e+00, -2.7495e+00,
        -2.6286e+00, -1.8206e+00, -2.3196e+00, -5.2806e+00, -3.7652e+00,
        -3.0987e+00, -4.1421e+00, -5.2531e+00, -4.6505e+00, -3.5815e+00,
        -4.0189e+00, -4.0008e+00, -4.5512e+00, -3.2248e+00, -7.7903e+00,
        -1.4484e+00, -3.8347e+00, -4.5611e+00, -4.3681e+00,  2.7234e-01,
        -4.0162e+00, -4.2136e+00, -5.4524e+00,  1.1744e+00, -4.7785e+00,
        -1.8335e+00,  4.1288e-01,  2.2239e+00, -9.9919e-02,  4.8216e+00,
        -8.4304e-01,  5.6911e-01, -4.0484e+00, -3.3013e+00,  2.8698e+00,
        -1.1419e+00, -9.1690e-01, -2.9284e+00, -2.6097e+00, -1.8213e-01,
        -2.5429e+00, -2.1095e+00,  2.2419e+00, -1.6280e+00,  7.4458e+00,
         2.3184e+00, -5.7408e+00, -7.4332e-01, -5.4066e+00,  1.5177e+01,
        -4.4737e-02,  1.8237e+00, -3.7741e+00,  9.2271e-01, -4.3687e-01,
        -1.4003e+00, -4.3026e+00,  6.3782e-01, -1.0808e+00, -1.4173e+00,
         2.6194e+00, -3.8418e+00,  1.1598e+00, -2.6876e+00, -3.6103e+00,
        -4.9281e+00, -4.1411e+00, -3.3603e+00, -3.4296e+00, -1.4997e+00,
        -2.8381e+00, -1.2843e+00,  1.5745e+00, -1.7449e+00,  4.2903e-01,
         3.1234e-01, -2.8206e+00,  3.6688e-01, -2.1033e+00,  1.6481e+00,
         1.4222e+00, -2.7303e+00, -3.6292e+00,  1.2864e+00, -2.5541e+00,
        -2.9663e+00, -4.1575e+00, -3.1954e+00, -4.6487e-01,  1.8916e+00,
        -7.4721e-01,  4.5986e+00, -2.5443e+00, -6.2003e+00, -1.3215e+00,
        -2.6225e+00,  9.9639e+00,  9.7772e+00,  9.6715e+00,  9.0857e+00,...

Where do I get the class labels from? I couldn't find any method that let me get that from the model object.

You cannot, unfortunately, get class label names directly from the torchvision models. However, these models are trained on the ImageNet dataset (hence the 1000 classes).
You have to get the class name mapping off the web as far as I know; there's no way to get it off torch. Previously, you could download ImageNet directly using torchvision.datasets.ImageNet, which had a built-in label to class name converter. Now the download link isn't publicly available and requires a manual download, before it can be used by datasets.ImageNet.
So you can simply search for the class to label mapping of ImageNet online, rather than downloading the data or attempting with torch. Try here for example.
