deep-text-recognition-benchmark
PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR) (by roatienza)
Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace. (by NielsRogge)
deep-text-recognition-benchmark | Transformers-Tutorials | |
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1 | 7 | |
278 | 7,965 | |
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2.2 | 8.4 | |
about 1 month ago | 7 days ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deep-text-recognition-benchmark
Posts with mentions or reviews of deep-text-recognition-benchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-11.
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Building an Internet Scale Meme Search Engine
https://github.com/roatienza/deep-text-recognition-benchmark (available weights are for tasks that seem similar to OCR so there is a good chance you can use it out of the box). With a good gpu it should process hundreds to thousands image per seconds, so you likely can build your index in less than a day. (Maybe you can even port it to your iphone stack :) )
https://github.com/microsoft/GenerativeImage2Text (You'll probably have to train on your custom dataset that you have constituted)
There are tons of other freely available solutions that you can get with a search for things with keywords like "image to text ocr" "transformers" "visual transformers"...
Transformers-Tutorials
Posts with mentions or reviews of Transformers-Tutorials.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-16.
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
- FLaNK Stack Weekly for 07August2023
- How to annotate compound words to build NER models?
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[discussion] Anybody Working with VITMAE?
I'm pretraining on 850K grayscale spectrograms of birdsongs. I'm on epoch 400 out of 800 and the loss has declined from about 1.2 to 0.7. I don't really have a sense of what is "good enough" and I guess the only way I can judge is by looking at the reconstruction. I'm doing that using this notebook as a guide and right now it's doing quite badly.
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[D] NLP has HuggingFace, what does Computer Vision have?
More tutorials can be found at https://github.com/NielsRogge/Transformers-Tutorials.
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[Discussion] Information Extraction with LayoutLMv2
Ive been looking for an off the shelf encoder-decoder document understanding model for key information extraction. I found a great Huggingface implementation with concise notebook examples. However, the token classification model outputs a list of token labels corresponding bounding boxes for the token, but, not the text contained within the labeled bounding boxes themselves. Am I missing something? LayoutLMv2 describes itself as being capable of information extraction but without extracting the text I feel like it's fallen short of that ambition.
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[Project] Deepmind's Perceiver IO available through Hugging Face
Example Notebooks