# AI use cases for Vision HOPE (PSRben/VisionHOPE) Repo: [PSRben/VisionHOPE](https://github.com/PSRben/VisionHOPE) | Use cases: 4 | Page: https://dedupedai.com/use-cases/psrben-visionhope/ 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. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Classify images with a pretrained Vision HOPE model: https://dedupedai.com/use-cases/psrben-visionhope/classify-images-with-visionhope-checkpoint/ - Add the Vision HOPE operator to your own vision network: https://dedupedai.com/use-cases/psrben-visionhope/drop-srnl-operator-into-custom-vision-model/ - Cut Vision HOPE inference time on an NVIDIA GPU: https://dedupedai.com/use-cases/psrben-visionhope/speed-up-visionhope-inference/ - Check Vision HOPE accuracy on ImageNet, COCO, or ADE20K: https://dedupedai.com/use-cases/psrben-visionhope/evaluate-visionhope-checkpoint-on-benchmark/ ## How to classify images with a pretrained Vision HOPE model with Vision HOPE Repo: [PSRben/VisionHOPE](https://github.com/PSRben/VisionHOPE) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Load a Vision HOPE ImageNet-1K checkpoint and get 1000-class logits for your own images. **Who it is for:** A researcher or engineer with a CUDA GPU who needs a quick ImageNet-style image classifier to test or compare against other backbones. **The problem:** Training a visual backbone from scratch takes far more time than a first test, so you need a working pretrained model to start from. **The outcome:** You 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. ### 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 to paste into your agent ```text 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 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. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/psrben-visionhope/classify-images-with-visionhope-checkpoint/ ## How to add the Vision HOPE operator to your own vision network with Vision HOPE Repo: [PSRben/VisionHOPE](https://github.com/PSRben/VisionHOPE) | Difficulty: Advanced | Time: 1 hour 15 minutes | Basis: From the README Use the standalone SRNL, VisionHOPEOperator, or VisionHOPEBlock modules inside a different PyTorch architecture. **Who it is for:** A deep learning researcher building a custom image model who wants to test the Vision HOPE operator as a component in place of a standard mixing layer. **The problem:** Replacing an existing block in an architecture is hard to test unless the new module has a clear input and output shape contract. **The outcome:** Your model runs with the Vision HOPE operator in one or more layers, and gradients flow through it during training. ### Steps 1. Install the classification requirements with `python -m pip install -r requirements/classification.txt` and `python -m pip install --no-deps -e .`. 2. Import the modules with `from visionhope.models import VisionHOPEOperator, VisionHOPEBlock` or `from visionhope.models import SRNL`. 3. Pass image features shaped [batch, channels, height, width] into `VisionHOPEOperator(dim=64, head_dim=16)` or `VisionHOPEBlock(dim=64, mixer_dim=32, head_dim=16)`; the output keeps the same shape. 4. For sequence inputs, pass tensors shaped [batch, tokens, dim] into `SRNL(dim=64, head_dim=16, chunk_size=64)`, where `dim` must be divisible by `head_dim`. 5. Run a small loss with `.backward()` on the output to confirm gradients reach your module's parameters. ### Prompt to paste into your agent ```text Using the VisionHOPE repo (PSRben/VisionHOPE), insert VisionHOPEBlock into 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. ``` ### Caveat All three components require CUDA, so CPU-only machines cannot run them even for a shape check. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/psrben-visionhope/drop-srnl-operator-into-custom-vision-model/ ## How to cut Vision HOPE inference time on an NVIDIA GPU with Vision HOPE Repo: [PSRben/VisionHOPE](https://github.com/PSRben/VisionHOPE) | Difficulty: Advanced | Time: 1 hour | Basis: From the README Turn on fast inference for standalone modules or prepare a full model for faster forward passes. **Who it is for:** An engineer deploying a Vision HOPE model for batch image processing who needs lower latency on an NVIDIA GPU. **The problem:** A model loaded for training runs slower than needed for inference, and the README has separate fast paths for modules and full models. **The outcome:** Inference runs with fast inference enabled and the weights stay the same, so outputs match the original model within normal numerical differences. ### Steps 1. Install the classification requirements with `python -m pip install -r requirements/classification.txt` and `python -m pip install --no-deps -e .`. 2. Load the model with `create_model("visionhope_small", checkpoint_path="weights/visionhope_small.pth").cuda().eval()`. (inferred, check the README) 3. Call `prepare_for_inference(model)` from `visionhope.inference.prepare` after loading the weights. 4. Run the forward pass inside `torch.inference_mode()` and `torch.autocast("cuda", dtype=torch.bfloat16)`. 5. Keep an unprepared copy of the model if you also plan to continue training, because the preparation fuses parameters in place. ### Prompt to paste into your agent ```text 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 . Report the average latency before and after preparation. Verify that the prepared model still returns logits shaped [1, 1000]. ``` ### Caveat The README says prepare_for_inference fuses parameters in place, so a model prepared for inference cannot be used for training without a separate unprepared copy. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/psrben-visionhope/speed-up-visionhope-inference/ ## How to check Vision HOPE accuracy on ImageNet, COCO, or ADE20K with Vision HOPE Repo: [PSRben/VisionHOPE](https://github.com/PSRben/VisionHOPE) | Difficulty: Intermediate | Time: 2 hours | Basis: From the README Run the matching test script on your downloaded dataset to measure a released checkpoint. **Who it is for:** A researcher who wants to confirm reported results on a local copy of ImageNet-1K, COCO 2017, or ADE20K before building on the checkpoint. **The problem:** Published numbers are only useful if you can reproduce them on your own dataset copy and hardware. **The outcome:** You get the evaluation output from the matching test script for the checkpoint and dataset you chose. ### 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 to paste into your agent ```text Using the VisionHOPE repo (PSRben/VisionHOPE), run scripts/test/visionhope_small.sh on the dataset at 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. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/psrben-visionhope/evaluate-visionhope-checkpoint-on-benchmark/