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

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272

Hello,
Thanks you for this library .

I was using it and I had one edge case. my home folder name ends with .py it is /home/espoir.py so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code

Currently, there are following warnings when running tests:

test/test_models.py::test_quantized_classification_model[googlenet]
  /root/project/torchvision/models/googlenet.py:47: FutureWarning: The default weight initialization of GoogleNet will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11

Describe the bug
When exporting a brush annotation as a PNG, the output is not mapped by the background colors specified in (Settings > Labeling Interface). In addition, when exporting as a JSON, the background colors for the attributes are not specified anywhere, leaving the values that were selected in the interface as arbitrary and as not linked to any of the outputs.

To Reproduce