Florence-2 : Advancing a Unified Representation for a Variety of Vision Tasks | Paper Explained
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- Опубліковано 22 чер 2024
- Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks.
GitHub: github.com/AarohiSingla/Flore...
Try out the Florence-2 model here: huggingface.co/spaces/gokaygo...
Paper: arxiv.org/pdf/2311.06242
Florence-2 is pre-trained on our FLD-5B dataset encompassing a total of 5.4B comprehensive annotations across 126M images.
#computervision #largelanguagemodels #languagemodels #microsoft #ai #artificialintelligence
Very excited to play with this architecture. There are already a few tutorials out there showing how to fine-tune on custom data, too. Thanks for the overview!
Glad to help!
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Glad you liked it!
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More to come!
I was waiting for this video! Thank you!
Hope you enjoyed it!
Hats off to your commendable efforts
Thanks a lot
Nicely explained video
Thanks a lot
amazing explanation as usual
Thank you!
great explanation!
Thanks!
Hell Thanks for your all videos and efforts. I am following your channel, but I request you please upload one detail video on how to finetune Yolov5 model for custome images classification.
Noted
Mam where can i see cv related research paper. Im currently final year student looking for cv project. Can you share any link. Which will be so helpful for me and my batch mates
ArXiv.org , CVPR, IEEE Xplore
Whis is netter Yolov-9 or Florence-2
Yolov9 is an object detection and segmentation model whereas Florence 2 is a vision language model. It can handle various tasks which yolov9 can't perform like Image captioning, text extracting etc.
AI renewed so soon!
Absolutely! Technology moves fast these days.
🎯 Key points for quick navigation:
00:05 *📚 Florence-2 is a lightweight vision language model that can handle various tasks based on simple instructions.*
00:26 *💡 The key innovation of Florence-2 is its ability to handle tasks like object detection, captioning, and detailed image analysis using a unified approach.*
04:13 *🔍 In computer vision, models need to understand both global concepts and finer details to be effective across different tasks.*
04:54 *📍 Spatial hierarchy refers to the understanding of visual information at different scales or levels of detail within an image.*
06:03 *🔎 Semantic granularity refers to how much detail we can understand from visual information, ranging from general ideas to specific details.*
09:11 *🤝 Multitask learning involves teaching a model to do multiple related tasks at the same time to improve its overall understanding and performance.*
10:08 *💪 Universal representation learning means training a single model that can understand different types of information without processing has several phases for ensuring correct and complete annotations.*
20:39 *👀 The detailed annotation process ensures that the FLD 5B data set is properly labeled across different levels of granularity, enhancing its utility for advanced AI applications.*
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