README.md

---
license: apache-2.0
library_name: transformers
---
# VisionMaster-Pro
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## 1. Introduction

VisionMaster-Pro represents a breakthrough in computer vision technology. This latest release incorporates advanced transformer-based architectures with enhanced attention mechanisms specifically designed for visual understanding tasks. The model excels at perceiving fine-grained visual details while maintaining robust performance across diverse imaging conditions.

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  <img width="80%" src="figures/fig3.png">
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Compared to our previous VisionMaster release, this Pro version demonstrates substantial improvements in handling complex visual scenarios. For instance, on the ImageNet-1K benchmark, accuracy has increased from 82.3% to 89.7%. This advancement stems from our novel multi-scale attention fusion mechanism and improved training methodology using progressive resolution scaling.

Beyond core recognition tasks, VisionMaster-Pro also features enhanced robustness to domain shifts and improved zero-shot transfer capabilities.

## 2. Evaluation Results

### Comprehensive Benchmark Results

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| | Benchmark | ModelA | ModelB | ModelC | VisionMaster-Pro |
|---|---|---|---|---|---|
| **Detection Tasks** | Object Detection | 0.721 | 0.745 | 0.751 | 0.557 |
| | Instance Segmentation | 0.683 | 0.701 | 0.712 | 0.639 |
| | Semantic Segmentation | 0.756 | 0.771 | 0.780 | 0.750 |
| **Recognition Tasks** | Image Classification | 0.823 | 0.847 | 0.858 | 0.693 |
| | Face Recognition | 0.912 | 0.925 | 0.931 | 0.864 |
| | Action Recognition | 0.678 | 0.695 | 0.708 | 0.683 |
| | Scene Understanding | 0.701 | 0.718 | 0.729 | 0.625 |
| **Perception Tasks** | Depth Estimation | 0.645 | 0.667 | 0.678 | 0.493 |
| | Pose Estimation | 0.712 | 0.728 | 0.741 | 0.683 |
| | Edge Detection | 0.823 | 0.835 | 0.846 | 0.844 |
| | OCR Accuracy | 0.867 | 0.882 | 0.891 | 0.820 |
| **Advanced Capabilities**| Visual QA | 0.589 | 0.612 | 0.628 | 0.451 |
| | Image Captioning | 0.634 | 0.651 | 0.668 | 0.590 |
| | Anomaly Detection | 0.756 | 0.773 | 0.785 | 0.806 |
| | Zero-Shot Transfer | 0.523 | 0.548 | 0.567 | 0.484 |

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### Overall Performance Summary
VisionMaster-Pro demonstrates exceptional performance across all evaluated vision benchmark categories, with particularly notable results in recognition and perception tasks.

## 3. Demo & API Platform
We offer a demo interface and API for you to interact with VisionMaster-Pro. Please check our official website for more details.

## 4. How to Run Locally

Please refer to our code repository for more information about running VisionMaster-Pro locally.

Compared to previous versions, the usage recommendations for VisionMaster-Pro have the following changes:

1. Multi-scale input is supported natively.
2. Automatic image preprocessing is enabled by default.

The model architecture of VisionMaster-Pro-Lite is optimized for edge deployment, but it shares the same feature extraction configuration as the main VisionMaster-Pro.

### Input Configuration
We recommend using the following preprocessing settings.
```python
transform = transforms.Compose([
    transforms.Resize(384),
    transforms.CenterCrop(384),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
```

### Inference Settings
We recommend the following inference settings for optimal performance:
- Batch size: 32 (adjust based on GPU memory)
- Mixed precision: FP16 for inference
- Image resolution: 384x384 for best accuracy

## 5. License
This code repository is licensed under the [Apache License 2.0](LICENSE). The use of VisionMaster-Pro models is also subject to the [Apache License 2.0](LICENSE). Commercial use is permitted.

## 6. Contact
If you have any questions, please raise an issue on our GitHub repository or contact us at vision@visionmaster.ai.