fixes

README.md

@@ -85,7 +85,7 @@ Peng Gao, Renrui Zhang, Chris Liu, Longtian Qiu, Siyuan Huang, Weifeng Lin, Shit
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 ### **BLIP: Bootstrapping Language-Image Pre-training**
-[![arXiv](https://img.shields.io/badge/arXiv-2201.12086-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2201.12086) [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/salesforce/BLIP)   
+[![arXiv](https://img.shields.io/badge/arXiv-2201.12086-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2201.12086) [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/salesforce/BLIP)    
 Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi
 <p align="center">
 <img src="https://github.com/gokayfem/Awesome-VLM-Architectures/assets/88277926/27db1037-2b48-4097-9891-019ba77fc536" />
@@ -214,7 +214,7 @@ Junyan Li, Delin Chen, Yining Hong, Zhenfang Chen, Peihao Chen, Yikang Shen, Chu
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 ### **GLaMM: Pixel Grounding Large Multimodal Model**
-[![arXiv](https://img.shields.io/badge/arXiv-2311.03356-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2311.03356) [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/mbzuai-oryx/groundingLMM)   
+[![arXiv](https://img.shields.io/badge/arXiv-2311.03356-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2311.03356) [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/mbzuai-oryx/groundingLMM)     
 Hanoona Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Erix Xing, Ming-Hsuan Yang, Fahad S. Khan
 <p align="center">
 <img src="https://github.com/gokayfem/Awesome-VLM-Architectures/assets/88277926/ccb22206-6a48-4b77-8cc1-094fe86d72fd" />
@@ -494,7 +494,7 @@ Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma
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 <summary>ℹ️ <i>More Information</i></summary>  
 
-**MetaCLIP**: Represents an innovative approach in the realm of data curation for machine learning, specifically targeting the **enhancement of training datasets** through metadata utilization derived from CLIP's concepts. This model is designed to sift through extensive raw data pools, such as the CommonCrawl dataset, to curate a high-quality, balanced subset that significantly betters the diversity and performance metrics of the data used for training machine learning models. The essence of MetaCLIP lies in its unique architecture that incorporates data curation algorithms, which are adept at leveraging metadata for the purpose of balancing and enriching the training dataset both in terms of quality and diversity. The architecture of MetaCLIP is structured around these **data curation algorithms**, which play a pivotal role in the framework by identifying and assembling a balanced and high-quality dataset from a vast collection of 400 million image-text pairs initially sourced from CommonCrawl. This process is instrumental in MetaCLIP's ability to demonstrate superior performance on various benchmarks, including zero-shot ImageNet classification, when compared to datasets curated using CLIP's original methodologies. The training methods employed by MetaCLIP, therefore, are not just about processing and learning from data but also about intelligently selecting the data that is most beneficial for the training process, ensuring that the model is trained on a dataset that is representative, diverse, and of high quality. The purpose behind employing datasets like CommonCrawl within the MetaCLIP framework is to address and overcome the limitations observed in CLIP’s original dataset. By curating a balanced and high-quality dataset of 400 million image-text pairs, MetaCLIP sets a new precedent in the field of machine learning data curation. This strategic selection and enhancement of the training dataset enable MetaCLIP to significantly improve performance on standard benchmarks compared to its predecessor, highlighting the importance of dataset quality and diversity in achieving high performance in machine learning tasks. Through its innovative approach to data curation, MetaCLIP offers a promising avenue for enhancing the capabilities of machine learning models, particularly in applications requiring robust image-text understanding and classification.
+**MetaCLIP**: Represents an innovative approach in the realm of data curation for machine learning, specifically targeting the **enhancement of training datasets** through metadata utilization derived from CLIP's concepts. This model is designed to sift through extensive raw data pools, such as the CommonCrawl dataset, to curate a high-quality, balanced subset that significantly betters the diversity and performance metrics of the data used for training machine learning models. The essence of MetaCLIP lies in its unique architecture that incorporates data curation algorithms, which are adept at leveraging metadata for the purpose of balancing and enriching the training dataset both in terms of quality and diversity. The architecture of MetaCLIP is structured around these **data curation algorithms**, which play a pivotal role in the framework by identifying and assembling a balanced and high-quality dataset from a vast collection of 400 million image-text pairs initially sourced from CommonCrawl. This process is instrumental in MetaCLIP's ability to demonstrate superior performance on various benchmarks, including zero-shot ImageNet classification, when compared to datasets curated using CLIP's original methodologies. The training methods employed by MetaCLIP, therefore, are not just about processing and learning from data but also about intelligently selecting the data that is most beneficial for the training process, ensuring that the model is trained on a dataset that is representative, diverse, and of high quality. The purpose behind employing datasets like CommonCrawl within the MetaCLIP framework is to address and overcome the limitations observed in CLIP's original dataset. By curating a balanced and high-quality dataset of 400 million image-text pairs, MetaCLIP sets a new precedent in the field of machine learning data curation. This strategic selection and enhancement of the training dataset enable MetaCLIP to significantly improve performance on standard benchmarks compared to its predecessor, highlighting the importance of dataset quality and diversity in achieving high performance in machine learning tasks. Through its innovative approach to data curation, MetaCLIP offers a promising avenue for enhancing the capabilities of machine learning models, particularly in applications requiring robust image-text understanding and classification.
 </details> 
 
 ### **Alpha-CLIP: A CLIP Model Focusing on Wherever You Want**