VisionScope-R2 is an experimental, highly versatile vision suite designed for advanced image inference, spatial reasoning, and complex scene understanding. Built upon the powerful Qwen2.5-VL and Qwen2-VL architectures.
See the codeVisionScope-R2 is an experimental, highly versatile vision suite designed for advanced image inference, spatial reasoning, and complex scene understanding. Built upon the powerful Qwen2.5-VL and Qwen2-VL architectures, this application offers a modern, interactive web interface for processing a wide variety of visual inputs, including documents, receipts, screenshots, and complex real-world scenes. By integrating a diverse roster of state-of-the-art vision-language models—ranging from specialized captioners to spatial thinkers and OCR engines—the tool allows users to seamlessly extract text, estimate distances, and generate highly detailed scene descriptions. Fully GPU-accelerated and optimized for performance, VisionScope-R2 provides researchers and developers with granular control over text generation parameters, creating an ideal environment for testing and deploying next-generation vision-based AI workflows.
DeepCaption-VLA-7B, SkyCaptioner-V1, SpaceThinker-3B, coreOCR-7B-050325-preview, and SpaceOm-3B..txt file.kernels-community/flash-attn2 for optimized, memory-efficient inference on compatible GPUs.├── images/
│ ├── 1.jpg
│ ├── 2.jpeg
│ ├── 3.png
│ ├── 4.png
│ └── 5.jpg
├── app.py
├── LICENSE
├── pre-requirements.txt
├── README.md
└── requirements.txt
To run VisionScope-R2 locally, you need to configure a Python environment with the following dependencies. Ensure you have a compatible CUDA-enabled GPU for optimal performance.
1. Install Pre-requirements Run the following command to update pip to the required version:
pip install pip>=23.0.0
2. Install Core Requirements
Install the necessary machine learning and UI libraries. You can place these in a requirements.txt file and run pip install -r requirements.txt.
git+https://github.com/huggingface/[email protected]
git+https://github.com/huggingface/accelerate.git
git+https://github.com/huggingface/peft.git
transformers-stream-generator
huggingface_hub
qwen-vl-utils
sentencepiece
opencv-python
torch==2.8.0
torchvision
matplotlib
requests
kernels
hf_xet
spaces
pillow
gradio
av
Once your environment is set up and the dependencies are installed, you can launch the application by running the main Python script:
python app.py
After the script initializes the interface, it will provide a local web address (usually http://127.0.0.1:7860/) which you can open in your browser to interact with the models. Note that the selected models will be downloaded and loaded into VRAM upon their first invocation.
VisionScope-R2 is an experimental, highly versatile vision suite designed for advanced image inference, spatial reasoning, and complex scene understanding. Built upon the powerful Qwen2.5-VL and Qwen2-VL architectures.
See the codeVisionScope-R2 is an experimental, highly versatile vision suite designed for advanced image inference, spatial reasoning, and complex scene understanding. Built upon the powerful Qwen2.5-VL and Qwen2-VL architectures, this application offers a modern, interactive web interface for processing a wide variety of visual inputs, including documents, receipts, screenshots, and complex real-world scenes. By integrating a diverse roster of state-of-the-art vision-language models—ranging from specialized captioners to spatial thinkers and OCR engines—the tool allows users to seamlessly extract text, estimate distances, and generate highly detailed scene descriptions. Fully GPU-accelerated and optimized for performance, VisionScope-R2 provides researchers and developers with granular control over text generation parameters, creating an ideal environment for testing and deploying next-generation vision-based AI workflows.
DeepCaption-VLA-7B, SkyCaptioner-V1, SpaceThinker-3B, coreOCR-7B-050325-preview, and SpaceOm-3B..txt file.kernels-community/flash-attn2 for optimized, memory-efficient inference on compatible GPUs.├── images/
│ ├── 1.jpg
│ ├── 2.jpeg
│ ├── 3.png
│ ├── 4.png
│ └── 5.jpg
├── app.py
├── LICENSE
├── pre-requirements.txt
├── README.md
└── requirements.txt
To run VisionScope-R2 locally, you need to configure a Python environment with the following dependencies. Ensure you have a compatible CUDA-enabled GPU for optimal performance.
1. Install Pre-requirements Run the following command to update pip to the required version:
pip install pip>=23.0.0
2. Install Core Requirements
Install the necessary machine learning and UI libraries. You can place these in a requirements.txt file and run pip install -r requirements.txt.
git+https://github.com/huggingface/[email protected]
git+https://github.com/huggingface/accelerate.git
git+https://github.com/huggingface/peft.git
transformers-stream-generator
huggingface_hub
qwen-vl-utils
sentencepiece
opencv-python
torch==2.8.0
torchvision
matplotlib
requests
kernels
hf_xet
spaces
pillow
gradio
av
Once your environment is set up and the dependencies are installed, you can launch the application by running the main Python script:
python app.py
After the script initializes the interface, it will provide a local web address (usually http://127.0.0.1:7860/) which you can open in your browser to interact with the models. Note that the selected models will be downloaded and loaded into VRAM upon their first invocation.