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Bump torchvision from 0.10.0 to 0.13.0

Open dependabot[bot] opened this issue 3 years ago • 1 comments

Bumps torchvision from 0.10.0 to 0.13.0.

Release notes

Sourced from torchvision's releases.

TorchVision 0.13, including new Multi-weights API, new pre-trained weights, and more

Highlights

Models

Multi-weight support API

TorchVision v0.13 offers a new Multi-weight support API for loading different weights to the existing model builder methods:

from torchvision.models import *

Old weights with accuracy 76.130%

resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)

New weights with accuracy 80.858%

resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)

Best available weights (currently alias for IMAGENET1K_V2)

Note that these weights may change across versions

resnet50(weights=ResNet50_Weights.DEFAULT)

Strings are also supported

resnet50(weights="IMAGENET1K_V2")

No weights - random initialization

resnet50(weights=None)

The new API bundles along with the weights important details such as the preprocessing transforms and meta-data such as labels. Here is how to make the most out of it:

from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights

img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")

Step 1: Initialize model with the best available weights

weights = ResNet50_Weights.DEFAULT model = resnet50(weights=weights) model.eval()

Step 2: Initialize the inference transforms

preprocess = weights.transforms()

</tr></table>

... (truncated)

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dependabot[bot] avatar Jul 01 '22 21:07 dependabot[bot]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

stale[bot] avatar Aug 31 '22 00:08 stale[bot]

Superseded by #384.

dependabot[bot] avatar Sep 01 '22 21:09 dependabot[bot]