diffgram
albumentations
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| diffgram | albumentations | |
|---|---|---|
| 9 | 28 | |
| 1,795 | 13,395 | |
| 1.3% | 1.9% | |
| 9.1 | 8.3 | |
| 7 days ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
diffgram
- Open source tool scrapes git commit email addresses to send spam to.
- Open Source Training Data – Diffgram
- Open Source Data Annotation - Diffgram
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[D] Labelbox threatens to sue small open-source startup Diffgram
As a few examples of how much depth we have considered, here's a detailed comparison with sagemaker. Part of an integration with scale. Part of code for labelbox integration, datasaur (scroll to trusted by for our logo) etc. To the best of my knowledge I am trying to track every firm that is in this direct space.
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[P] 😃🎉 Open source AI/ML data annotation platform for free [Product Hunt]
(Direct repo link https://github.com/diffgram/diffgram)
- Open Source AI Data Annotation 2.0 (Source Code)
- Open Source AI Data Annotation 2.0
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[P] Diffgram - Open Annotation Platform
We list some benefits here but if I had to pick one thing, it's that it's a complete system. You can be up and running in 2 minutes on docker. And scale to "big tech co" level on multiple k8s clusters.
- Show HN: Open Core Diffgram (GitHub)
albumentations
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Augment specific classes?
You can use albumentations if you are comfortable with using open source libraries https://github.com/albumentations-team/albumentations
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Ask HN: What side projects landed you a job?
One of the members of the core team of our open-source library https://albumentations.ai/
It was not the only reason he was hired; it was a solid addition to his already good performance at the interviews.
Or at least that is what the hiring manager later said.
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The Lack of Compensation in Open Source Software Is Unsustainable
I am one of the creators and maintainers of https://albumentations.ai/.
- 12800+ stars
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Burn Deep Learning Framework Release 0.7.0: Revamped (de)serialization, optimizer & module overhaul, initial ONNX support and tons of new features.
Is something planned to support data augmentations? Something like https://albumentations.ai/
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How to label augmented images for training YOLO algorithm?
Here you go: https://albumentations.ai/
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Unstable Diffusion bounces back with $19,000 raised in one day, by using Stripe
I think they should use some data augmentation techniques like I am using for Infinity AI if you wanna see more here. Note that most of these do not work for image generation.
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Tokyo Drift : detecting drift in images with NannyML and Whylogs
Our second approach was a more automated one. Here the idea was to try out an image augmentation library, Albumentations, and use it for adversarial attacks. This time, instead of one-shot images, we applied the transformations at random time ranges. We chose for these transformations also to be more subtle than then one-shot images, such as vertical flips, grayscaling, downscaling, …
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[D] Improve machine learning with same number of images
Check out albumentations. If your use case is segmentation, check out the offline augmentation of this project
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What are the best programs/scripts for image augmentation of YOLO5 training dataset. Something like roboflow but free)
I think this is the most popular open source project: https://github.com/albumentations-team/albumentations
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To get dataset for face image restoration.
You can also curate your own dataset by using open source images (https://universe.roboflow.com/search?q=faces%20images%3E1000) and open source augmentations (https://github.com/albumentations-team/albumentations). Or you can do use the augmentation UI (https://docs.roboflow.com/image-transformations/image-augmentation) to apply noise, blurring, shear, crop, etc.
What are some alternatives?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
imgaug - Image augmentation for machine learning experiments.
awesome-data-labeling - A curated list of awesome data labeling tools
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
dataqa - Labelling platform for text using weak supervision.
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
hover - :speedboat: Label data at scale. Fun and precision included.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
auto_annotate - Labeling is boring. Use this tool to speed up your next object detection project!
BlenderProc - A procedural Blender pipeline for photorealistic training image generation