twilio-automation-plugin
fiftyone-plugins
| twilio-automation-plugin | fiftyone-plugins | |
|---|---|---|
| 3 | 6 | |
| 2 | 103 | |
| - | 1.0% | |
| 6.2 | 8.9 | |
| about 1 year ago | about 23 hours ago | |
| Python | Python | |
| - | Apache License 2.0 |
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.
twilio-automation-plugin
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Zero-Shot Prediction Plugin for FiftyOne
Week 1: šØ AI Art Gallery & Twilio Automation
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Plugin for Building and Managing Plugins!
Some of these plugins were simpler than others. On one end, the Twilio automation plugin consists of a single Python file without bells and whistles. On the opposite extreme, plugins like Active Learning, which required multiple operators, caching, and special handling for many different scenarios. Plugins like Reverse Image Search and Concept Space Traversal were challenging in a different way, mostly because I am new to JavaScript. But that is for another day.
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Build Custom Computer Vision Applications
In the AI Art Gallery and Twilio Automation plugins, I had used the ctx.trigger() method to perform operations like reloading samples (ctx.trigger(āreload_samplesā)), and reloading the dataset (ctx.trigger(āreload_datasetā)). I was even aware from VoxelGPT that you could use ctx.trigger() to set the sessionās view.
fiftyone-plugins
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Voxel51 Filtered Views Newsletter - August 23, 2024
Custom Dashboards: Create no-code dashboards to visualize dataset statistics and model performance.
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Aug 7 - Developing Data-Centric AI Applications Workshop
Join machine learning engineer Daniel Gural for a free 90 min workshop, where he'll cover how to install, use, and build your own data-centric AI applications using FiftyOne's open source plugin framework. For example:
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How I Built an In-Cabin Perception Dataset
Use our I/O plugin to āimport samplesā
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Finding Outliers in Your Vision Datasets
Outliers are found in almost every dataset. Finding them, especially across hundreds of thousands if not millions of samples can be a daunting task, but with FiftyOne, the workflow can be made simple with the Outlier Detection. If you are interested in finding more FiftyOne plugins, checkout our community repo to optimize your workflows with plugins or contribute one of your own! Plugins are highly flexible and always open source so that you can customize it exactly to your needs! Have fun exploring!
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Build Custom Computer Vision Applications
FiftyOne Plugins Repo
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Generating Videos from Images with Stable Video Diffusion and FiftyOne
As always, be sure to check out all of our FiftyOne Plugins for more GenAI plugins for computer vision plus much, much more! If you are interested in building your own plugins, hop into the community slack to join other developers and get access to tons of resources!
What are some alternatives?
zero-shot-prediction-plugin - Run zero-shot prediction models on your data
vqa-plugin - Perform visual question answering on your images
image-deduplication-plugin - Remove exact and approximate duplicates from your dataset in FiftyOne!
img_to_video_plugin - A FiftyOne Plugin that allows you to turn images to video!
pytesseract-ocr-plugin - Run optical character recognition with PyTesseract from the FiftyOne App!
AI-Scientist - The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery š§āš¬
image-quality-issues - FiftyOne Plugin for finding common image quality issues
ten-weeks-of-plugins - My journey during 10 weeks of building FiftyOne plugins
text-to-image - Use text-to-image models Stable Diffusion, DALL-E2, DALL-E3, SDXL, SSD-1B, Kandinsky-2.2, and LCM from UI. Add images directly to your dataset!
Phi-3CookBook - This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.
ai-art-gallery - Use text-to-image models Stable Diffusion, DALL-E2, DALL-E3, SDXL, SSD-1B, Kandinsky-2.2, and LCM from UI. Add images directly to your dataset! [Moved to: https://github.com/jacobmarks/text-to-image]