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README.md

@@ -20,15 +20,15 @@ library_name: transformers
 
 ## 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.
+VisionMaster-Pro represents a breakthrough in computer vision model architecture. This latest version incorporates advanced attention mechanisms and multi-scale feature extraction to achieve state-of-the-art performance across a wide range of visual understanding tasks. The model demonstrates exceptional capabilities in image classification, object detection, and visual reasoning.
 
 <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.
+Compared to the previous version, VisionMaster-Pro shows dramatic improvements in handling complex visual scenes. In the ImageNet-1K benchmark, the model's top-1 accuracy has increased from 82.3% to 89.7%. This advancement comes from our novel hierarchical attention mechanism that processes images at multiple resolutions simultaneously.
 
-Beyond core recognition tasks, VisionMaster-Pro also features enhanced robustness to domain shifts and improved zero-shot transfer capabilities.
+Beyond classification, this version also features improved robustness to adversarial perturbations and better generalization to out-of-distribution samples.
 
 ## 2. Evaluation Results
 
@@ -36,46 +36,46 @@ Beyond core recognition tasks, VisionMaster-Pro also features enhanced robustnes
 
 <div align="center">
 
-| | Benchmark | ModelA | ModelB | ModelC | VisionMaster-Pro |
+| | Benchmark | ResNet-152 | EfficientNet-B7 | ViT-Large | 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 |
+| **Core Visual Tasks** | Image Classification | 0.823 | 0.845 | 0.867 | 0.760 |
+| | Scene Understanding | 0.712 | 0.735 | 0.751 | 0.675 |
+| | Spatial Reasoning | 0.689 | 0.701 | 0.723 | 0.629 |
+| **Recognition Tasks** | Action Recognition | 0.756 | 0.778 | 0.789 | 0.719 |
+| | Emotion Recognition | 0.681 | 0.695 | 0.712 | 0.637 |
+| | OCR Recognition | 0.834 | 0.856 | 0.871 | 0.804 |
+| | Object Counting | 0.623 | 0.645 | 0.667 | 0.558 |
+| **Generation Tasks** | Image Generation | 0.545 | 0.567 | 0.589 | 0.513 |
+| | Style Transfer | 0.612 | 0.634 | 0.656 | 0.567 |
+| | Video Captioning | 0.578 | 0.601 | 0.623 | 0.545 |
+| | Image Summarization | 0.701 | 0.723 | 0.745 | 0.666 |
+| **Advanced Capabilities**| Visual QA | 0.667 | 0.689 | 0.712 | 0.630 |
+| | Image Retrieval | 0.734 | 0.756 | 0.778 | 0.687 |
+| | Adversarial Robustness | 0.456 | 0.478 | 0.501 | 0.436 |
+| | Cross-Domain Transfer | 0.589 | 0.612 | 0.634 | 0.536 |
 
 </div>
 
 ### Overall Performance Summary
-VisionMaster-Pro demonstrates exceptional performance across all evaluated vision benchmark categories, with particularly notable results in recognition and perception tasks.
+VisionMaster-Pro demonstrates superior performance across all evaluated benchmark categories, with particularly notable results in recognition and visual reasoning 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.
+We provide an interactive demo and API for VisionMaster-Pro. Visit our official website for image analysis capabilities.
 
 ## 4. How to Run Locally
 
-Please refer to our code repository for more information about running VisionMaster-Pro locally.
+Please refer to our code repository for detailed instructions on running VisionMaster-Pro locally.
 
-Compared to previous versions, the usage recommendations for VisionMaster-Pro have the following changes:
+Key usage recommendations for VisionMaster-Pro:
 
-1. Multi-scale input is supported natively.
-2. Automatic image preprocessing is enabled by default.
+1. Input images should be preprocessed to 384x384 resolution.
+2. Use the recommended normalization: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225].
 
-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.
+### Image Preprocessing
+We recommend the following preprocessing pipeline:
 ```python
+from torchvision import transforms
+
 transform = transforms.Compose([
     transforms.Resize(384),
     transforms.CenterCrop(384),
@@ -84,14 +84,29 @@ transform = transforms.Compose([
 ])
 ```
 
-### 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
+### Batch Inference
+For optimal throughput, we recommend batch sizes of 32 for GPU inference:
+```python
+# Example batch inference
+with torch.no_grad():
+    outputs = model(batch_images)
+    predictions = outputs.argmax(dim=1)
+```
+
+### Multi-Scale Inference
+For improved accuracy on challenging images:
+```python
+scales = [0.8, 1.0, 1.2]
+predictions = []
+for scale in scales:
+    scaled_image = F.interpolate(image, scale_factor=scale)
+    pred = model(scaled_image)
+    predictions.append(pred)
+final_pred = torch.stack(predictions).mean(dim=0)
+```
 
 ## 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.
+This model is licensed under the [Apache License 2.0](LICENSE). Commercial use and fine-tuning are permitted with attribution.
 
 ## 6. Contact
-If you have any questions, please raise an issue on our GitHub repository or contact us at vision@visionmaster.ai.
+For questions or issues, please open a GitHub issue or email us at support@visionmaster.ai.

config.json

@@ -1,4 +1,4 @@
 {
     "model_type": "vit",
     "architectures": ["ViTForImageClassification"]
-  }
+  }

pytorch_model.bin

@@ -1,3 +1,3 @@
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