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Internvit 6B 448px V2 5
InternViT-6B-448px-V2_5
I
Internvit 6B 448px V2 5
AI ModelImage Editing#Visual Model#Feature Extraction#Multimodal#OCR#Image RecognitionStandard PicksOpen Source
Overview :
InternViT-6B-448px-V2_5 is a visual model built upon InternViT-6B-448px-V1-5, which enhances the visual encoder's ability to extract visual features by utilizing ViT incremental learning and NTP loss (Stage 1.5). It particularly excels in domains where representation is lacking in large-scale network datasets, such as multilingual OCR data and mathematical charts. This model is part of the InternVL 2.5 series, maintaining the same 'ViT-MLP-LLM' architecture as its predecessor, while integrating the newly incrementally pretrained InternViT alongside various pretrained LLMs, including InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector.
Target Users :
The target audience includes researchers, developers, and enterprises, particularly those who need to handle tasks such as image recognition, classification, and semantic segmentation. Given the model's strengths in multilingual OCR and mathematical chart recognition, it is also ideal for educational institutions and academic researchers focusing on these specific data areas.
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Use Cases
Example 1: Using InternViT-6B-448px-V2_5 for image classification to identify the main objects within images.
Example 2: In multilingual document processing, utilizing the model for OCR data recognition and conversion.
Example 3: In the educational field, the model is employed to recognize and analyze mathematical charts, aiding teaching and learning.
Features
? Visual Feature Extraction: The model can extract visual features from images for classification and semantic segmentation.
? Incremental Learning: Enhanced ability to handle rare domain data through ViT incremental learning and NTP loss.
? Multilingual OCR Data Support: Excels in processing optical character recognition tasks across multiple languages.
? Mathematical Chart Recognition: Capable of recognizing and understanding mathematical charts, extending its application in academic and educational fields.
? Dynamic High-Resolution Training: Supports dynamic high-resolution training, capable of processing multi-image and video datasets.
? Cross-Modal Capabilities: Enhanced visual perception and multimodal abilities through three phases of training.
? Model Architecture Compatibility: Maintains the consistent 'ViT-MLP-LLM' architecture with previous generations, facilitating technical iteration and upgrades.
How to Use
1. Import the necessary libraries, such as torch and transformers.
2. Load the InternViT-6B-448px-V2_5 model from the Hugging Face model repository.
3. Prepare the input image by opening it with the PIL library and converting it to RGB format.
4. Process the image using CLIPImageProcessor to obtain pixel values.
5. Convert the pixel values to the data type required by the model and move them to the GPU.
6. Input the processed image data into the model to obtain the output.
7. Analyze the model output for subsequent image classification or semantic segmentation tasks.
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