Datasets, Transforms and Models specific to Computer Vision (by pytorch)

Vision Alternatives

Similar projects and alternatives to vision

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better vision alternative or higher similarity.

vision reviews and mentions

Posts with mentions or reviews of vision. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-26.
  • Reading a DL paper: YOLO summary and discussion
    2 projects | | 26 Feb 2023
    Found relevant code at + all code implementations here
  • Open discussion and useful links people trying to do Object Detection
    4 projects | | 18 Feb 2023
    * Why doesnt Pytorch have YOLO!
  • My Neural Net is stuck, I've run out of ideas
    2 projects | | 16 Feb 2023
    Sorry to be annoying but I thought it was nice to give you some news as well. I was confused as to why there isnt yolo in pytorch, here it is why
  • [Discussion] Stochastic Depth with BatchNorm ?
    2 projects | | 26 Dec 2022
    My question is more related to the variance of the batchs. If one batch contains samples that skip a connection and samples that do not ('row' mode in the Torchvision implementation), even if the values are ajusted to preserve the expected value, the variance will be much higher because we have in practice two distributions (for x_n and x_n + f(x_n)/p), which will mess up with the update of the batch normalization. Also, at inference time, all forward passes will be done as x_{n+1} = x_n + f(x_n), which has a different variance. The torchvision implementation also offers a 'batch' mode that kinda reduce this issue (because the global variance computed this way will be the mean of both distribution variances, instead of the variance of the joint distribution) but it does not seem to be the default mode (it does not even exist in the timm implementation).
  • Solution for "RuntimeError: Couldn't load custom C++ ops"
    2 projects | | 7 Sep 2022
    RuntimeError: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check for the compatibility matrix. Please check your PyTorch version with torch.version and your torchvision version with torchvision.version and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.
  • [D] My experience with running PyTorch on the M1 GPU
    4 projects | | 19 May 2022
    $ python --device "cuda" torch 1.11.0+cu102 device cuda Downloading to data/cifar-10-python.tar.gz 170499072it [00:46, 3628424.66it/s] Extracting data/cifar-10-python.tar.gz to data Downloading: "" to /home/md/.cache/torch/hub/ Epoch: 001/001 | Batch 0000/1406 | Loss: 2.6563 Epoch: 001/001 | Batch 0100/1406 | Loss: 2.4686 Epoch: 001/001 | Batch 0200/1406 | Loss: 2.1224 Epoch: 001/001 | Batch 0300/1406 | Loss: 2.1879 Epoch: 001/001 | Batch 0400/1406 | Loss: 2.1733 Epoch: 001/001 | Batch 0500/1406 | Loss: 2.2413 Epoch: 001/001 | Batch 0600/1406 | Loss: 2.0518 Epoch: 001/001 | Batch 0700/1406 | Loss: 2.1621 Epoch: 001/001 | Batch 0800/1406 | Loss: 1.9033 Epoch: 001/001 | Batch 0900/1406 | Loss: 1.8379 Epoch: 001/001 | Batch 1000/1406 | Loss: 1.9572 Epoch: 001/001 | Batch 1100/1406 | Loss: 1.8823 Epoch: 001/001 | Batch 1200/1406 | Loss: 1.7978 Epoch: 001/001 | Batch 1300/1406 | Loss: 2.0239 Epoch: 001/001 | Batch 1400/1406 | Loss: 1.8389 Time / epoch without evaluation: 6.75 min <------------------ Epoch: 001/001 | Train: 25.52% | Validation: 26.40% | Best Validation (Ep. 001): 26.40% Time elapsed: 9.03 min Total Training Time: 9.03 min Test accuracy 26.54% Total Time: 9.48 min
  • Hey, I'm trying to find some materials about object detection in Pytorch but I'm having a hard time finding it.
    2 projects | | 26 Nov 2021
    And there are explanations: it's the research articles as well as the blog articles talking about them. And 99% of the code you'll find is open sourced: - The official torchvision has various models described here with their reference papers and the code of these models is found on the gitub page - You can find almost-official YOLO implementation on this github page.
  • [D] Efficiently loading videos in PyTorch without extracting frames
    5 projects | | 26 Oct 2021
    Maybe VideoClips? see the discussion here:
  • PyTorch 1.10
    8 projects | | 22 Oct 2021
    haha, yes, but that requires you to modify existing code to do so (which isn't always possible!).

    There might also be other things you want to do (like add profiling after each op) that would be tedious to do manually, but can easily automated with FX (

    Another example is the recent support from torchvision for extracting intermediate feature activations ( Like, sure, it was probably possible to refactor all of their code to enable users to specify extracting an intermediate feature, but it's much cleaner to do with FX.

    8 projects | | 22 Oct 2021
    Not quite - it's more of a platform for users to write their own transformations on their PyTorch code.

    This can include things like doing operator fusion/lowering to a backend compiler, but can also include things like inserting profiling instrumentation ( or extracting intermediate features (

    Basically, if you want a graph representation of a PyTorch module that's really easy to modify, use torch.fx :)

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