Update README.md

README.md

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 The **BLIP3-OCR-200M** dataset is designed to address the limitations of current Vision-Language Models (VLMs) in processing and interpreting text-rich images, such as documents and charts. Traditional image-text datasets often struggle to capture nuanced textual information, which is crucial for tasks requiring complex text comprehension and reasoning. 
 
-<img src="blip3_ocr_200m_examples/blip3_ocr_200m.png" alt="Art" width=600>
-<!-- <img src="blip3_ocr_200m.png" alt="Art" width=500> -->
 
 ### Key Features
 - **OCR Integration**: The dataset incorporates Optical Character Recognition (OCR) data during the pre-training phase of VLMs. This integration enhances vision-language alignment by providing detailed textual information alongside visual data.
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   - **uid**: A unique identifier for the OCR data associated with each image.
   - **ocr_num_token_larger_than_confidence_threshold**: The number of OCR tokens that exceed a specified confidence threshold（0.9 in this case).
 
-### Downloading the Original DataComp Images
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-If you want to download the original images for each sample based on the `url` entry in the dataset, you can use the [`img2dataset`](https://github.com/rom1504/img2dataset) tool. This tool efficiently downloads images from URLs and stores them in a specified format. You can refer to the script provided by the DataComp project for downloading images. The script is available [here](https://github.com/mlfoundations/datacomp/blob/main/download_upstream.py).
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 ### Example of Loading and Processing the Data
 You can simply access the data by: