| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| - zh |
| base_model: Qwen/Qwen3.5-4B-Base |
| tags: |
| - image-classification |
| - decision-model |
| - multimodal |
| - lora |
| - custom-code |
| --- |
| # JEVision |
|
|
| Give JEVision an image, a question, and possible answers. It returns a probability for each answer. |
|
|
| - **Image + text input.** Designed for fast visual decisions. |
| - **About 0.4 seconds:** median 356 ms on our 100-request local test, using an RTX 5060 Ti 16 GB. Loading, network time, and queues are excluded. |
| - **135 MB download is the adapter and decision head.** The setup below also downloads the required Qwen3.5-4B base model. |
|
|
| ## Run it |
|
|
| Requirements: Linux or Windows WSL2, Python 3.10+, Docker with NVIDIA GPU support, and an NVIDIA GPU. Tested on an RTX 5060 Ti 16 GB. First setup downloads several GB and takes time. |
|
|
| Run these commands in a Linux/WSL terminal: |
|
|
| ```bash |
| python3 -m venv .venv |
| source .venv/bin/activate |
| python -m pip install --upgrade huggingface_hub |
| hf download heack/JEVision --local-dir jevision |
| cd jevision |
| docker build -t jevision-local . |
| docker run --rm -v "$PWD:/release" -w /release jevision-local download_base.py --root /release/data |
| docker run --rm --gpus all -v "$PWD:/release" -w /release jevision-local example.py --root /release/data --checkpoint /release |
| ``` |
|
|
| The example creates three colored images and checks all nine predictions. A successful run exits without an error and saves `verification.json`. |
|
|
| To test your own picture, copy it into the `jevision` folder as `photo.png`, then run: |
|
|
| ```bash |
| docker run --rm --gpus all -v "$PWD:/release" -w /release jevision-local example.py --root /release/data --checkpoint /release --image photo.png --question "Which animal is visible?" --choices cat,dog,bird |
| ``` |
|
|
| Choose answers that match your question. The output contains each answer's probability. Keep the `data` folder to avoid downloading the base again. |
|
|
| ## Results |
|
|
| Internal evaluation: **94.14% accuracy over 2,423 questions** from 900 labeled states. These are internal results, not an independent benchmark. The Docker runtime and image examples passed on the tested GPU. |
|
|
| [Evaluation](evaluation.json) · [Usage verification](verification-clean.json) · [Technical details](DETAILS.md) |
|
|
| ## Online API |
|
|
| No hosted endpoint is available yet. Hugging Face rejected this account's ZeroGPU deployment request with a PRO subscription requirement. The commands above run the model locally. |
|
|
| ## License |
|
|
| Noncommercial use: [CC BY-NC-SA 4.0](LICENSE). Commercial use: **US$1,000 once for a perpetual license to this model's purchased version**. Each model is licensed separately. Hosting and API usage are not included. Upstream licenses and prior valid rights remain in effect. |
|
|
| <p align="center"> |
| <a href="https://buymeacoffee.com/penecho"><img src="https://img.shields.io/badge/BUY_US_A_COFFEE-FFDD00?style=for-the-badge&logo=buymeacoffee&logoColor=000000" alt="Buy us a coffee"></a> |
| <a href="COMMERCIAL-LICENSE.md"><img src="https://img.shields.io/badge/COMMERCIAL_LICENSE_TERMS-087f83?style=for-the-badge" alt="Read commercial license terms"></a> |
| </p> |
|
|
| Select USD and include `heack/JEVision`, your Hugging Face username, and the licensee's name with your payment. [Read the commercial terms](COMMERCIAL-LICENSE.md). |
|
|
