moondream/synthetic-gauges-v6
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vikhyatk
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moondream/synthetic-analog-clocks-v3
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moondream/synthetic-gauges-v5
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moondream/synthetic-gauges-v4
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moondream/1M-synthetic-gauges-v4
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moondream/synthetic-gauges-v3
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moondream/synthetic-gauges-v2
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moondream/analog-clock-benchmark
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moondream/synthetic-analog-clocks-v2
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moondream/1M-synthetic-gauges
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moondream/1M-synthetic-analog-clocks
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moondream/100k-synthetic-clocks
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moondream/megalith-qa-resized
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Post
4543
🚨 New VQA + captioning dataset!
moondream/megalith-mdqa
Images from Megalith, captioned using Moondream, then transformed to short-form QA.
9M+ images, 6-10 QA pairs per image.
Images from Megalith, captioned using Moondream, then transformed to short-form QA.
9M+ images, 6-10 QA pairs per image.
Post
6053
Just released a dataset with 7000+ hours of synthetically generated lo-fi music.
vikhyatk/lofi
Post
6733
Pushed a new update to
vikhyatk/moondream2 today. TextVQA up from 60.2 to 65.2, DocVQA up from 61.9 to 70.5.
Space has been updated to the new model if you want to try it out! vikhyatk/moondream2
Space has been updated to the new model if you want to try it out! vikhyatk/moondream2
Post
3342
🚀 Exciting news! We've just launched "Thundermoon" - the latest version of Moondream, our open-source vision language model! 🌙
Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
* DocVQA: 61.9 (↑103%)
* TextVQA: 60.2 (↑5.2%)
* GQA: 64.9 (↑2.9%)
What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.
Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!
Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.
Curious to try it out? Try out the live demo here! https://moondream.ai/playground
Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
* DocVQA: 61.9 (↑103%)
* TextVQA: 60.2 (↑5.2%)
* GQA: 64.9 (↑2.9%)
What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.
Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!
Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.
Curious to try it out? Try out the live demo here! https://moondream.ai/playground
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- 4 replies
Post
3764
Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?
Try out the vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇️
https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb
Try out the vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇️
https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb
Post
3117
Just released a new version of
vikhyatk/moondream2 - now supporting higher resolution images (up to 756x756)!
TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.
TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.
Post
1800
Cool new dataset from
@isidentical
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isidentical/moondream2-coyo-5M-captions
The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.
The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.
- 1 reply