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What you can do with Vision HOPE

VisionHOPE is an official PyTorch implementation of a visual backbone built on self-referential nested learning, with ImageNet classification, COCO detection and segmentation, and ADE20K segmentation code plus standalone SRNL and operator modules.

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PSRben/VisionHOPE on GitHub

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What do you want to do?

4 use cases

  • Classify images with a pretrained Vision HOPE model

    Load a Vision HOPE ImageNet-1K checkpoint and get 1000-class logits for your own images.

    View prompt
    Using the VisionHOPE repo (PSRben/VisionHOPE), install the classification requirements, download visionhope_small.pth into weights/, and load it with create_model. Run one forward pass on <image path> resized to 224 x 224 and report the top-5 ImageNet class indices. Verify the output tensor has shape [1, 1000].

  • Add the Vision HOPE operator to your own vision network

    Use the standalone SRNL, VisionHOPEOperator, or VisionHOPEBlock modules inside a different PyTorch architecture.

    View prompt
    Using the VisionHOPE repo (PSRben/VisionHOPE), insert VisionHOPEBlock into <target model file> in place of its existing mixing layer, keeping input and output shapes as [batch, channels, height, width]. Run one training step on a random CUDA tensor and report the output shape and loss. Verify that the backward pass completes without error.

  • Cut Vision HOPE inference time on an NVIDIA GPU

    Turn on fast inference for standalone modules or prepare a full model for faster forward passes.

    View prompt
    Using the VisionHOPE repo (PSRben/VisionHOPE), load visionhope_small.pth from weights/, call prepare_for_inference on it, and time 100 forward passes on random CUDA inputs of shape [1, 3, 224, 224] for <target hardware>. Report the average latency before and after preparation. Verify that the prepared model still returns logits shaped [1, 1000].

  • Check Vision HOPE accuracy on ImageNet, COCO, or ADE20K

    Run the matching test script on your downloaded dataset to measure a released checkpoint.

    View prompt
    Using the VisionHOPE repo (PSRben/VisionHOPE), run scripts/test/visionhope_small.sh on the dataset at <dataset path> with checkpoint weights/visionhope_small.pth. Report the evaluation metrics and compare them with the Main results section of the README. Verify that the dataset folders match the layout in the Datasets section before the run starts.

How to use Vision HOPE

  1. Check you have what it needs: Linux with an NVIDIA GPU, the CUDA toolkit (including nvcc), and a C++ compiler; Python 3.10 (recommended) and CUDA 12.1; Pretrained checkpoint from Hugging Face, GitHub Releases, or Baidu Netdisk.
  2. Open PSRben/VisionHOPE on GitHub and read the README and license.
  3. Run python -m pip install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu121 from the repository root. (inferred, check the README)
  4. Run python -m pip install -r requirements/classification.txt and then python -m pip install --no-deps -e ..
  5. Run python -m pip install --upgrade huggingface_hub and then hf download PSRben/VisionHOPE visionhope_small.pth --local-dir weights.
  6. In Python, call create_model("visionhope_small", checkpoint_path="weights/visionhope_small.pth").cuda().eval() from visionhope.models. (inferred, check the README)

These steps come from our top use case, Classify images with a pretrained Vision HOPE model, which also has a copy-paste agent prompt.

Vision HOPE alternatives · Best AI repos for Research

About this repo

  • Who it is forComputer vision researchers and ML engineers with an NVIDIA GPU who want to run, evaluate, or reuse the VisionHOPE backbone and its operator modules.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/PSRben/VisionHOPE · Back to Hot repos

Requirements

  • Linux with an NVIDIA GPU, the CUDA toolkit (including nvcc), and a C++ compiler
  • Python 3.10 (recommended) and CUDA 12.1
  • Pretrained checkpoint from Hugging Face, GitHub Releases, or Baidu Netdisk
  • For dataset tasks: ImageNet-1K, COCO 2017, or ADE20K downloaded from their official websites
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Questions about Vision HOPE

What is Vision HOPE?

VisionHOPE is an official PyTorch implementation of a visual backbone built on self-referential nested learning, with ImageNet classification, COCO detection and segmentation, and ADE20K segmentation code plus standalone SRNL and operator modules. It is an open-source project on GitHub: https://github.com/PSRben/VisionHOPE.

Who is Vision HOPE for?

Computer vision researchers and ML engineers with an NVIDIA GPU who want to run, evaluate, or reuse the VisionHOPE backbone and its operator modules.

What can I do with Vision HOPE?

4 use cases so far, for example: Classify images with a pretrained Vision HOPE model; Add the Vision HOPE operator to your own vision network; Cut Vision HOPE inference time on an NVIDIA GPU. Each has steps and a copy-paste agent prompt.

What do I need to run Vision HOPE?

Linux with an NVIDIA GPU, the CUDA toolkit (including nvcc), and a C++ compiler; Python 3.10 (recommended) and CUDA 12.1; Pretrained checkpoint from Hugging Face, GitHub Releases, or Baidu Netdisk; For dataset tasks: ImageNet-1K, COCO 2017, or ADE20K downloaded from their official websites.

What are alternatives to Vision HOPE?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Vision HOPE safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.