InternViT-6B-448px-V2_5
This model enhances visual feature extraction for image classification, OCR, and math chart analysis, supporting dynamic high-resolution training and multi-language capabilities.
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What is InternViT-6B-448px-V2_5?
InternViT-6B-448px-V2_5 is an advanced visual model based on InternViT-6B-448px-V1-5. It enhances the ability of the visual encoder to extract features by using ViT incremental learning and NTP loss (stage 1.5). This improvement is particularly beneficial for handling data from less represented areas like multi-language OCR and mathematical diagrams.
This model is part of the InternVL 2.5 series, retaining the "ViT-MLP-LLM" architecture similar to its predecessor while integrating newly pre-trained InternViT and various pre-trained LLMs such as InternLM 2.5 and Qwen 2.5 with a randomly initialized MLP projector.
Who Can Benefit from This Model?
Researchers, developers, and enterprises can benefit from this model, especially those working on image recognition, classification, and semantic segmentation tasks. Educational institutions and academic researchers will find it useful for processing specific data like multi-language OCR and mathematical diagrams.
Example Scenarios:
Use InternViT-6B-448px-V2_5 for classifying images and identifying primary objects.
Utilize the model for recognizing and converting text in multi-language documents through OCR.
Employ the model in educational settings for analyzing and interpreting mathematical diagrams to support teaching and learning.
Key Features:
Enhanced Visual Feature Extraction: The model extracts key visual features for image classification and semantic segmentation.
Incremental Learning: Improved handling of rare domain data through ViT incremental learning and NTP loss.
Multi-Language OCR Support: Effective in recognizing and processing text in multiple languages.
Mathematical Diagram Recognition: Capable of understanding and interpreting mathematical diagrams, expanding its use in academic and educational fields.
Dynamic High-Resolution Training: Supports dynamic high-resolution training for handling complex image and video datasets.
Multimodal Capability: Trained across three stages to enhance visual perception and multimodal abilities.
Architecture Compatibility: Maintains the "ViT-MLP-LLM" architecture consistent with previous models, easing technological updates and upgrades.
How to Use InternViT-6B-448px-V2_5:
1. Import necessary libraries such as torch and transformers.
2. Load the InternViT-6B-448px-V2_5 model from Hugging Face's model repository.
3. Prepare input images using the PIL library to open and convert them to RGB format.
4. Process the images using CLIPImageProcessor to get pixel values.
5. Convert pixel values to the required data type and move them to the GPU.
6. Input the processed image data into the model to obtain outputs.
7. Analyze the model output for subsequent image classification or semantic segmentation tasks.
