|
|
--- |
|
|
license: apache-2.0 |
|
|
library_name: transformers |
|
|
--- |
|
|
# VisionMaster-Pro |
|
|
<!-- markdownlint-disable first-line-h1 --> |
|
|
<!-- markdownlint-disable html --> |
|
|
<!-- markdownlint-disable no-duplicate-header --> |
|
|
|
|
|
<div align="center"> |
|
|
<img src="figures/fig1.png" width="60%" alt="VisionMaster-Pro" /> |
|
|
</div> |
|
|
<hr> |
|
|
|
|
|
<div align="center" style="line-height: 1;"> |
|
|
<a href="LICENSE" style="margin: 2px;"> |
|
|
<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
|
|
</a> |
|
|
</div> |
|
|
|
|
|
## 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. |
|
|
|
|
|
<p align="center"> |
|
|
<img width="80%" src="figures/fig3.png"> |
|
|
</p> |
|
|
|
|
|
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 |
|
|
|
|
|
<div align="center"> |
|
|
|
|
|
| | 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 | |
|
|
|
|
|
</div> |
|
|
|
|
|
### 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. |
|
|
|
