albumentations
ttach
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| albumentations | ttach | |
|---|---|---|
| 7 | 1 | |
| 10,178 | 723 | |
| 2.7% | - | |
| 7.4 | 1.5 | |
| 16 days ago | 4 months ago | |
| Python | Python | |
| MIT License | MIT License |
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albumentations
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How to further improve validation accuracy in multiclass semantic segmentation
I recommend looking into using Albumentations. It'll really help with your data augmentation.
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Data augmentation strategies for object detection? Could you point me to good resources or best practices you know of?
You can definitely look at Albumentation - we had a ton of success working with this library https://github.com/albumentations-team/albumentations
- Albumentations 1.1.0 Was Released
- Optimization for semantic segmentation!
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[P] Albumentations 1.0 is released (a Python library for image augmentation)
Full release notes: https://github.com/albumentations-team/albumentations/releases/tag/1.0.0
If you want to know what changed in the latest versions, please refer to the [Release Notes](https://github.com/albumentations-team/albumentations/releases) page.
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[Urgent Help] CNN model not working desirably
Image augmentation could help amplify your dataset without needing additional training data. Check out albumentations. It’s super easy.
ttach
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Setting up Google Colab for Deep Learning
While Colab usually comes pre-installed with most of the basic dependencies like Tensorflow, PyTorch, scikit-learn, pandas and many more, there are chances that you have to install external packages at times. You can do that using the !pip install command. For example we can install the ttach library which is used for augmentation of images during test phase. This can be done using:
What are some alternatives?
imgaug - Image augmentation for machine learning experiments.
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch - Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)
image-statistics-matching - Methods for alignment of global image statistics aimed at unsupervised Domain Adaptation and Data Augmentation
SickZil-Machine - Manga/Comics Translation Helper Tool
cvlib - A simple, high level, easy to use, open source Computer Vision library for Python.
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition