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How to classify images with a pretrained Vision HOPE model with Vision HOPE

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

  • Top pick
  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forA researcher or engineer with a CUDA GPU who needs a quick ImageNet-style image classifier to test or compare against other backbones.

The problemTraining a visual backbone from scratch takes far more time than a first test, so you need a working pretrained model to start from.

The outcomeYou have a loaded VisionHOPE-S model that returns logits of shape [1, 1000] for an input image tensor, ready for your own preprocessing and labels.

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. 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)
  2. Run python -m pip install -r requirements/classification.txt and then python -m pip install --no-deps -e ..
  3. Run python -m pip install --upgrade huggingface_hub and then hf download PSRben/VisionHOPE visionhope_small.pth --local-dir weights.
  4. In Python, call create_model("visionhope_small", checkpoint_path="weights/visionhope_small.pth").cuda().eval() from visionhope.models. (inferred, check the README)
  5. Run a forward pass under torch.inference_mode() on a [1, 3, 224, 224] CUDA tensor and check that the output shape is [1, 1000].

Prompt for your agent

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].

Caveat. The install requires an NVIDIA GPU with CUDA 12.1 and a working nvcc, and the CUDA extensions compile on first use, so setup can be slow on a fresh machine.

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