awesome-data-labeling
SSL4MIS
awesome-data-labeling | SSL4MIS | |
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7 | 2 | |
3,480 | 2,001 | |
1.6% | 2.0% | |
0.0 | 6.2 | |
7 months ago | 9 months ago | |
Python | ||
- | MIT License |
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awesome-data-labeling
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CVAT alternatives for video frame annotation
GitHub - heartexlabs/awesome-data-labeling: A curated list of awesome data labeling tools
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[P] Can anyone suggest free Image annotation tool for multi labelling?
Checkout this curated list on heartexlabs github. I used the list to find server-like annotation tools.
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Segmentation Maps
A Google search throws plenty of results for labelling tools: - https://github.com/heartexlabs/awesome-data-labeling - https://www.folio3.ai/blog/labelling-images-annotation-tool/ - https://neptune.ai/blog/data-labeling-software/amp
- How would you structure a dataset for both image counting and classification? And what would be the best approach for this task?
- Awesome-Data-Labeling
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How to get image dataset annotated? Any idea?
If you're looking for a tool or something, there are plenty out there. Of course, even with these tools, labeling 50k images is likely not feasible for an individual.
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[D] Suggestions for sentiment analysis tools
You can have a look at aws ground truth for this, or have a look at this https://github.com/heartexlabs/awesome-data-labeling
SSL4MIS
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Researchers at Oxford University Propose a Machine Learning Framework Called ‘TriSegNet’ Based on Triple-View Feature Learning for Medical Image Segmentation
Continue reading | Check out the paper and github link
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How to get image dataset annotated? Any idea?
Otherwise, you may be able to look into semi-supervised learning. Basically, you label a subset of your data, and use semi-supervised techniques to extrapolate and label the rest. This, of course, is a challenge in itself, but luckily this particular challenge has been researched a lot, so you may find something to get started with.
What are some alternatives?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
diffgram - The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.
uda - Unsupervised Data Augmentation (UDA)
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]
alibi-detect - Algorithms for outlier, adversarial and drift detection
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/opencv/cvat]
cleanlab - The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models. [Moved to: https://github.com/cleanlab/cleanlab]
labelbee-client - Out-of-the-box Annotation Toolbox
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
ScrivanoForLinux - Scrivano is a notetaking application for handwritten notes.
datalabel - datalabel is a UI-based data editing tool that makes it easy to create labeled text data in a dataframe. With datalabel, you can quickly and effortlessly edit your data without having to write any code. Its intuitive interface makes it ideal for both experienced data professionals and those new to data editing.