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How to check Vision HOPE accuracy on ImageNet, COCO, or ADE20K with Vision HOPE

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

  • Research
  • Intermediate
  • 2 hours
  • From the README
  • Any agent

Who it is forA researcher who wants to confirm reported results on a local copy of ImageNet-1K, COCO 2017, or ADE20K before building on the checkpoint.

The problemPublished numbers are only useful if you can reproduce them on your own dataset copy and hardware.

The outcomeYou get the evaluation output from the matching test script for the checkpoint and dataset you chose.

What you need first

  • 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

Steps

  1. Install the environment for your task: python -m pip install -r requirements/classification.txt for ImageNet, or the detection or segmentation requirements for COCO or ADE20K.
  2. Download the checkpoint with hf download PSRben/VisionHOPE visionhope_small.pth --local-dir weights or the matching COCO or ADE20K filename.
  3. Arrange your dataset as data/imagenet/, data/coco/, or data/ade20k/, or set --data-root to its location.
  4. Run CUDA_VISIBLE_DEVICES=0 bash scripts/test/visionhope_small.sh --data-root data/imagenet --checkpoint weights/visionhope_small.pth for ImageNet. (inferred, check the README)
  5. Compare the printed metrics with the Main results table in the README and note any gap caused by precision or preprocessing settings.

Prompt for your agent

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.

Caveat. You must download ImageNet-1K, COCO 2017, or ADE20K from their official websites yourself, and those downloads are large and not included in the repo.

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