labelme2coco
albumentations
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| labelme2coco | albumentations | |
|---|---|---|
| 1 | 28 | |
| 246 | 13,395 | |
| - | 1.9% | |
| 3.8 | 8.3 | |
| 3 days ago | 4 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
labelme2coco
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What's A Simple Custom Segmentation Pipeline?
I would also suggest labelme, it's pretty easy to use. Just type "labelme" in the shell after pip installing and you will see the GUI. There are tools to convert to coco format (like https://github.com/fcakyon/labelme2coco) if needed, for instance for Detectron2.
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?
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
imgaug - Image augmentation for machine learning experiments.
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
ttach - Image Test Time Augmentation with PyTorch!