cvat
alibi-detect
cvat | alibi-detect | |
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26 | 9 | |
11,287 | 2,085 | |
- | 1.6% | |
9.8 | 7.6 | |
28 days ago | 9 days ago | |
TypeScript | Python | |
MIT License | GNU General Public License v3.0 or later |
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cvat
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Another powerful resource is CVAT, the Computer Vision Annotation Tool which supports both image and video annotations with advanced capabilities such as interpolation of shapes between frames, making it highly suitable for computer vision.
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Need help identifying a good open source data annotation tool
CVAT has an open source repo under MIT license: https://github.com/opencv/cvat I've not worked with it directly but it might be a good place to start.
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OKENYO - Eyes to the Sky
ref https://github.com/opencv/cvat/issues/6061
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Way to label yolov7 images fast
an open source annotation tool that integrates object detectors is CVAT https://github.com/opencv/cvat however, using your own detector might require some coding. there is an integration for yolov5, but without modification it only loads the pretrained models.
- [D] Choosing the image labeling tool
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Segment Anything Model is now available in the open-source CVAT
This integration is currently available in the open-source version of Computer Vision Annotation Tool (http://github.com/opencv/cvat) and coming soon to CVAT.ai cloud (http://cvat.ai/)! Please use it for your computer vision projects to segment images faster.
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How to build computer vision dataset labeling team in-house
You can download the CVAT docker from a github (Link) and install it yourself, keeping all data local. And here are two options - locally on your personal computer (or company server) or in your own cloud (there are instructions on how to do this with AWS).
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CVAT Release v2.3.0: Brush tool, WebHooks, and Social auth
In this release, CVAT introduced new features based on our vision and suggestions in the CVAT community, plus addressed more than 20+ reported bugs.
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CVAT Course. Lecture #3 - Integration
You can find more information here Waiting for your feedback here: Discord, LinkedIn, Gitter, GitHub
alibi-detect
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Numerous tools exist for detecting anomalies in time series data, but Alibi Detect stood out to me, particularly for its capabilities and its compatibility with both TensorFlow and PyTorch backends.
- Looking for recommendations to monitor / detect data drifts over time
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[D] Distributions to represent an Image Dataset
That is, to see whether a test image belongs in the distribution of the training images and to provide a routine for special cases. After a bit of reading Ive found that this is related to the field of drift detection in which I tried out alibi-detect . Whereby the training images are trained by an autoencoder and any subsequent drift will be flagged by the AE.
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[D] Which statistical test would you use to detect drift in a dataset of images?
Wasserstein distance is not very suitable for drift detection on most problems given that the sample complexity (and estimation error) scales with O(n^(-1/d)) with n the number of instances (100k-10m in your case) and d the feature dimension (192 in your case). More interesting will be to use for instance a detector based on the maximum mean discrepancy (MMD) with estimation error of O(n^(-1/2)). Notice the absence of the feature dimension here. You can find scalable implementations in Alibi Detect (disclosure: I am a contributor): MMD docs, image example. We just added the KeOps backend for the MMD detector to scale and speed up the drift detector further, so if you install from master, you can leverage this backend and easily scale the detector to 1mn instances on e.g. 1 RTX2080Ti GPU. Check this example for more info.
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- What Machine Learning model monitoring tools can you recommend?
- Ask HN: Who is hiring? (December 2021)
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[D] How do you deal with covariate shift and concept drift in production?
I work in this area and also contribute to outlier/drift detection library https://github.com/SeldonIO/alibi-detect. To tackle this type of problem, I would strongly encourage following a more principled, fundamentally (statistically) sound approach. So for instance measuring metrics such as the KL-divergence (or many other f-divergences) will not be that informative since it has a lot of undesirable properties for the problem at hand (in order to be informative requires already overlapping distributions P and Q, it is asymmetric, not a real distance metric, will not scale well with data dimensionality etc). So you should probably look at Integral Probability Metrics (IPMs) such as the Maximum Mean Discrepancy (MMD) instead which have much nicer behaviour to monitor drift. I highly recommend the Interpretable Comparison of Distributions and Models NeurIPS workshop talks for more in-depth background.
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[D] Is this a reasonable assumption in machine learning?
All of the above functionality and more can be easily used under a simple API in https://github.com/SeldonIO/alibi-detect.
What are some alternatives?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
coco-annotator - :pencil2: Web-based image segmentation tool for object detection, localization, and keypoints
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
django-rest-framework - Web APIs for Django. 🎸
river - 🌊 Online machine learning in Python
labelbox-custom-labeling-apps - Explore example custom labeling apps built with Labelbox SDK
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder