Computer vision

I figured out a way to get the (x,y,z) data points for each frame from one hand previously. but im not sure how to do that for the new holistic model that they released. I am trying to get the all landmark data points for both hands as well as parts of the chest and face. does anyone know how to extract the holistic landmark data/print it to a text file? or at least give me some directions as to h

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

Describe the bug

Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

Steps to reproduce the bug

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("

🚀 The feature

specify the dep typing_extensions is only needed for python < 3.8

Motivation, pitch

specify the dep typing_extensions is only needed for python < 3.8

Alternatives

No response

Additional context

According to https://github.com/pytorch/vision/search?q=typing_extensions, torchvision only uses typing_extensions.Literal, which is available since python 3.8, see

If there is a hot key (move the image from left to right) can get the feature when I was annotating the polygon.
The scroll of mouse can achieve the up and down direction of image, but if I need the move the image from left to right, I have to drag the bottom bar.
If there is a hotkey to drag the whole image or to move the image horizontal?
Thank you!