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README.md

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+---
+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.

config.json

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+{
+    "model_type": "vit",
+    "architectures": ["ViTForImageClassification"]
+  }

pytorch_model.bin

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