| vit_l_32       |    76.972 |     93.07 |
| convnext_tiny  |     82.52 |    96.146 |
| convnext_small |    83.616 |     96.65 |
| convnext_base  |    84.062 |     96.87 |
| convnext_large |    84.414 |    96.976 |
The above models have been trained using an adjusted version of our new training recipe and this allows us to offer models with accuracies significantly higher than the ones on the original papers.
#4. GPU Video Decoding
In this release, we add support for GPU video decoding in the video reading API. To use hardware-accelerated decoding, we just need to pass a cuda device to the video reading API as shown below:
import torchvision

reader = torchvision.io.VideoReader(file_name, device="cuda:0")
for frame in reader:
    print(frame)

We also support seeking to anyframe or a keyframe in the video before reading, as shown below:
reader.seek(seek_time)
