nnUNet
albumentations
| nnUNet | albumentations | |
|---|---|---|
| 11 | 28 | |
| 5,045 | 13,425 | |
| 3.6% | 0.9% | |
| 9.2 | 8.9 | |
| 4 days ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
nnUNet
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Where to even begin to extract yellow spots from these pictures?
Now just run your images and masks in nnUNet to see if it works. https://github.com/MIC-DKFZ/nnUNet
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[D] Improvements/alternatives to U-net for medical images segmentation?
I would also try the nnU-Net which should give state-of-the-art performance, and so will give you a good idea of what's possible with the dataset that you have: https://github.com/MIC-DKFZ/nnUNet
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Seeking ideas for Road Damage Detection
I used the nnUnet repo and their guide on using it on your own data
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Model conversion from Pytorch to Tf using Onnx.
Hi all, Does anyone have experience with converting a nnUNet pytorch models into Tf using Onnx?
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[D] Best practices for training medical imaging models
If you want my honest opinion, you should probably always use nnUnet as a baseline for any 3d segmentation tasks - https://github.com/MIC-DKFZ/nnUNet.
- Image segmentation: huggingface segformers vs detectronv2
- How to get started with 3D computer vision?
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3D rcnn
Not a "detection" but a "segmenting" CNN: https://github.com/MIC-DKFZ/nnunet
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Underwhelming performance of Tesla V100 and A100 in vast.ai, why?
I ran some model fitting performance benchmarks on a 3D (medical) image segmentation UNET on various cloud providers and I am flabbergasted by the very low performance of V100 and A100 machines on vast.ai.
- [D] Which approach is recommended for Brain 🧠Tumor Segmentation on MRI scans (DICOM Files)?
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?
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
imgaug - Image augmentation for machine learning experiments.
3d-multi-resolution-rcnn - Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN."
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
s2ds
BlenderProc - A procedural Blender pipeline for photorealistic training image generation