| lutzroeder/netron |
25,287 |
|
4 |
70 |
about 2 years ago |
610 |
December 09, 2023 |
27 |
mit |
JavaScript |
| Visualizer for neural network, deep learning and machine learning models |
| Tencent/ncnn |
18,693 |
|
0 |
1 |
about 2 years ago |
26 |
October 27, 2023 |
1,010 |
other |
C++ |
| ncnn is a high-performance neural network inference framework optimized for the mobile platform |
| tangyudi/Ai-Learn |
7,757 |
|
0 |
0 |
over 2 years ago |
0 |
|
20 |
|
|
| 人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域 |
| ufoym/deepo |
6,312 |
|
0 |
0 |
about 3 years ago |
0 |
|
1 |
mit |
Python |
| Setup and customize deep learning environment in seconds. |
| microsoft/MMdnn |
5,767 |
|
3 |
0 |
over 2 years ago |
10 |
July 24, 2020 |
333 |
mit |
Python |
| MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. |
| ahkarami/Deep-Learning-in-Production |
4,138 |
|
0 |
0 |
over 2 years ago |
0 |
|
9 |
|
|
| In this repository, I will share some useful notes and references about deploying deep learning-based models in production. |
| polyaxon/polyaxon |
3,438 |
|
4 |
12 |
about 2 years ago |
418 |
December 09, 2023 |
122 |
apache-2.0 |
|
| MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle |
| ysh329/deep-learning-model-convertor |
3,197 |
|
0 |
0 |
almost 3 years ago |
0 |
|
2 |
|
|
| The convertor/conversion of deep learning models for different deep learning frameworks/softwares. |
| PINTO0309/PINTO_model_zoo |
3,121 |
|
0 |
0 |
about 2 years ago |
0 |
|
11 |
mit |
Python |
| A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. |
| chenyuntc/simple-faster-rcnn-pytorch |
3,107 |
|
0 |
0 |
almost 5 years ago |
0 |
|
164 |
other |
Jupyter Notebook |
| A simplified implemention of Faster R-CNN that replicate performance from origin paper |
