110 lines
4.3 KiB
Markdown
110 lines
4.3 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2-VL-2B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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tags:
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- text-generation-inference
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- label
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---
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# **Caption-Pro**
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**Caption-Pro** is an advanced image caption and annotation generator optimized for generating detailed, structured JSON outputs. Built upon a powerful vision-language architecture with enhanced OCR and multilingual support, Caption-Pro extracts high-quality captions and annotations from images for seamless integration into your applications.
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#### Key Enhancements:
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* **Advanced Image Understanding**: Fine-tuned on millions of annotated images, Caption-Pro delivers precise comprehension and interpretation of visual content.
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* **Optimized for JSON Output**: Produces structured JSON data containing captions and detailed annotations—perfect for integration with databases, APIs, and automation pipelines.
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* **Enhanced OCR Capabilities**: Accurately extracts textual content from images in multiple languages, including English, Chinese, Japanese, Korean, Arabic, and more.
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* **Multimodal Processing**: Seamlessly handles both image and text inputs, generating comprehensive annotations based on the provided image.
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* **Multilingual Support**: Recognizes and processes text within images across various languages.
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* **Secure and Optimized Model Weights**: Employs safetensors for efficient and secure model loading.
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### How to Use
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```python
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# Load the Caption-Pro model with optimized parameters
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Caption-Pro", torch_dtype="auto", device_map="auto"
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)
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# Recommended acceleration for performance optimization:
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# model = Qwen2VLForConditionalGeneration.from_pretrained(
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# "prithivMLmods/Caption-Pro",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# Load the default processor for Caption-Pro
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processor = AutoProcessor.from_pretrained("prithivMLmods/Caption-Pro")
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# Define the input messages with both an image and a text prompt
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://flux-generated.com/sample_image.jpeg",
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},
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{"type": "text", "text": "Provide detailed captions and annotations for this image in JSON format."},
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],
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}
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]
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# Prepare the input for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Generate the output
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generated_ids = model.generate(**inputs, max_new_tokens=256)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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### **Key Features**
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1. **Annotation-Ready Training Data**
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- Trained using a diverse dataset of annotated images to ensure high-quality structured output.
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2. **Optical Character Recognition (OCR)**
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- Robustly extracts and processes text from images in various languages and scripts.
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3. **Structured JSON Output**
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- Generates detailed captions and annotations in standardized JSON format for easy downstream integration.
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4. **Image & Text Processing**
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- Capable of handling both visual and textual inputs, delivering comprehensive and context-aware annotations.
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5. **Conversational Annotation Generation**
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- Supports multi-turn interactions, enabling detailed and iterative refinement of annotations.
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6. **Secure and Efficient Model Weights**
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- Uses safetensors for enhanced security and optimized model performance.
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**Caption-Pro** streamlines the process of generating image captions and annotations, making it an ideal solution for applications that require detailed visual content analysis and structured data integration. |