deequ
rembg
deequ | rembg | |
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17 | 52 | |
3,126 | 14,536 | |
0.6% | - | |
7.4 | 7.9 | |
14 days ago | 10 days ago | |
Scala | Python | |
Apache License 2.0 | MIT License |
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deequ
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[Data Quality] Deequ Feedback request
There's no straightforward way to drop and rerun a metric collection. For example, say you detect a problem in your data. You fix it, rerun the pipeline, and replace the bad data with the good. You'd want your metrics history to reflect the true state of your data. But the "bad run" cannot be dropped. Issue
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Thoughts on a business rules engine
I had similar requirements for QA reporting on large and diverse data sets. I implemented data check pipelines, with rules in AWS Deequ (https://github.com/awslabs/deequ) running on an Apache Spark cluster. The Deequ worked well for me, but there were a few cases where I opted to write the rule checks in the data store to improve throughput (i.e. SQL checks on critical data elements on the database).
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Building a data quality solution for devs and business people
Hey all! At the companies where I've worked as a developer, I've found that business stakeholders typically want a concrete way to check and assure the quality of data that pipelines are producing, before other downstream systems and users get impacted. I've tested solutions like Deequ, but I found that it made building compliance and data rules a bit more complicated and put a greater emphasis on developers to get the rules right that business was expecting. I also experienced issues with running checks in parallel and getting row level details about the failures.
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deequ VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
- November 15-19, 2022 FLiP Stack Weekly
- What are your favourite GitHub repos that shows how data engineering should be done?
- Well designed scala/spark project
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because itβs easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
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Congrats on hitting the v1 milestone, whylabs! You're r/MLOps OSS tool of the month!
I wonder how this compares with tools like DeeQu (https://github.com/awslabs/python-deequ - requires Spark) or Pandas Profiling? One plus side I can see is that it doesn't require Apache Spark to run profiling (though a quick look at the code indicates that they are working on Spark support) and can work with real time systems.
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What companies/startups are using Scala (open source projects on github)?
There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
rembg
- Rembg: Tool to Remove Images Background
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π Background Removal in Python with PyTorch and Rembg! π¨π
A bit conflicted as the linked video is also linked from the actual rembg repo but it seems way faster and more detailed to just read the readme at that repo first, and maybe use a video if something doesnβt make sense.
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Ask HN: How does MS-Teams, Meets and Zoom virtual background works?
There are open source tools like rembg (https://github.com/danielgatis/rembg) which call into pre-trained models.
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Lora Training - how to not train background?
You can use Rembg extension to remove background automatically: https://github.com/danielgatis/rembg
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[Question] where to deploy remove background using u2net ml app ? (ec2, lambda or else?)
Hi guys π· I am new to ml deployment. Can anyone help about production deployment ? I made fastapi docker app which removes background from image (https://github.com/danielgatis/rembg). It uses u2net segmentation. I tried aws ec2, lambda, googld cloudrun and so far ec2(t2-large) is the fastest but still too slow. Also, it costs way more than I expected. Are there any other solution that I can deploy ml app with as low as possible? Where do you guys mostly deploy ml app?
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lineart_coarse + openpose, batch img2img
I am currently using rembg https://github.com/danielgatis/rembg
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Newgen / regen face revamp project (AI powered) - once and for all!
rembg
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The new Controlnet lineart is great for sprite sheets/2D animations when combined with Canny. The top left was input and the other three were just Controlnet with no inpainting or upscaling.
A python script with rembg could work
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Useful utilities that will help when trying to make stuff
4.) rembg -- backgrounds be gone. If there isn't an extension in Automatic1111 for this yet, there should be (I haven't checked recently). Same deal as midas, you can point it at a folder and zap the backgrounds off of all your images. Useful in combo with midas and imagemagick if, for instance, you want images of an object on a white background (Stable Diffusion training via LoRAs/Dreambooth may not benefit, but other things like GANs prefer that sort of training image). Useful if you want to "compose" a scene and you have images of dispirate objects/people you want in that scene.
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Just a reminder that there is a new 'remove background' extension for a1111
Ran into another issue with " LoadLibrary failed with error 126 Here's the solution: https://github.com/danielgatis/rembg/issues/312
What are some alternatives?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
azure-kusto-spark - Apache Spark Connector for Azure Kusto
gmic - GREYC's Magic for Image Computing: A Full-Featured Open-Source Framework for Image Processing
dbt-data-reliability - dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
ai-background-remove - Cut out objects and remove backgrounds from pictures with artificial intelligence
Quill - Compile-time Language Integrated Queries for Scala
resynthesizer - Suite of gimp plugins for texture synthesis
BigDL - Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, etc.
stable-diffusion-webui-rembg - Removes backgrounds from pictures. Extension for webui.
re_data - re_data - fix data issues before your users & CEO would discover them π
Pixelitor - A desktop image editor