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