| NVIDIA/DALI |
4,770 |
|
0 |
4 |
about 2 years ago |
19 |
December 06, 2023 |
209 |
apache-2.0 |
C++ |
| A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications. |
| zhixuhao/unet |
4,218 |
|
0 |
0 |
almost 3 years ago |
0 |
|
203 |
mit |
Jupyter Notebook |
| unet for image segmentation |
| ZhaoJ9014/face.evoLVe |
3,074 |
|
0 |
0 |
over 3 years ago |
0 |
|
84 |
mit |
Python |
| 🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥 |
| fepegar/torchio |
1,904 |
|
0 |
29 |
about 2 years ago |
245 |
November 24, 2023 |
44 |
apache-2.0 |
Python |
| Medical imaging toolkit for deep learning |
| ncullen93/torchsample |
1,829 |
|
0 |
0 |
over 2 years ago |
0 |
|
59 |
other |
Python |
| High-Level Training, Data Augmentation, and Utilities for Pytorch |
| webdataset/webdataset |
1,737 |
|
0 |
51 |
about 2 years ago |
71 |
December 07, 2023 |
93 |
bsd-3-clause |
Python |
| A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch. |
| iver56/audiomentations |
1,605 |
|
0 |
5 |
about 2 years ago |
37 |
November 24, 2023 |
46 |
mit |
Python |
| A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning. |
| visual-layer/fastdup |
1,313 |
|
0 |
0 |
about 2 years ago |
284 |
December 01, 2023 |
23 |
other |
Python |
| fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. |
| LirongWu/awesome-graph-self-supervised-learning |
1,194 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
|
|
| Awesome Graph Self-Supervised Learning |
| asteroid-team/torch-audiomentations |
823 |
|
0 |
3 |
over 2 years ago |
14 |
June 29, 2022 |
49 |
mit |
Python |
| Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning. |
