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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Sandbox for training deep learning networks
| Date | Stars |
|---|---|
| 2026-07-24 | 3015 |
| 2026-07-25 | 3015 |
| 2026-07-28 | 3015 |
| 2026-07-30 | 3015 |
| 2026-07-31 | 3016 |
| 2026-08-06 | 3016 |
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# Deep learning networks [](https://travis-ci.org/osmr/imgclsmob) [](https://opensource.org/licenses/MIT) [](https://github.com/osmr/imgclsmob) This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (re)implementations of various classification, segmentation, detection, and pose estimation models and scripts for training/evaluating/converting. The following frameworks are used: - MXNet/Gluon ([info](https://mxnet.apache.org)), - PyTorch ([info](https://pytorch.org)), - Chainer ([info](https://chainer.org)), - Keras ([info](https://keras.io)), - TensorFlow 1.x/2.x ([info](https://www.tensorflow.org)). For each supported framework, there is a PIP-package containing pure models without auxiliary scripts. List of packages: - [gluoncv2](https://pypi.org/project/gluoncv2) for Gluon, - [pytorchcv](https://pypi.org/project/pytorchcv) for PyTorch, - [chainercv2](https://pypi.org/project/chainercv2) for Chainer, - [kerascv](https://pypi.org/project/kerascv) for Keras, - [tensorflowcv](https://pypi.org/project/tensorflowcv) for TensorFlow 1.x, - [tf2cv](https://pypi.org/project/tf2cv) for TensorFlow 2.x. Currently, models are mostly implemented on Gluon and then ported to other frameworks. Some models are pretrained on [ImageNet-1K](http://www.image-net.org), [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html), [SVHN](http://ufldl.stanford.edu/housenumbers), [CUB-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html), [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012), [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K), [Cityscapes](https://www.cityscapes-dataset.com), and [COCO](http://cocodataset.org) datasets. All pretrained weights are loaded automatically during use. See examples of such automatic loading of weights in the corresponding sections of the documentation dedicated to a particular package: - [Gluon models](gluon/README.md), - [PyTorch models](https://github.com/osmr/pytorchcv/blob/master/README.md), - [Chainer models](chainer_/README.md), - [Keras models](keras_/README.md), - [TensorFlow 1.x models](tensorflow_/README.md), - [TensorFlow 2.x models](tensorflow2/README.md). ## Installation To use training/evaluating scripts as well as all models, you need to clone the repository and install dependencies: ``` git clone [email protected]:osmr/imgclsmob.git pip install -r requirements.txt ``` ## Table of implemented classification models Some remarks: - `Repo` is an author repository, if it exists. - `a`, `b`, `c`, `d`, and `e` means the implementation of a model for ImageNet-1K, CIFAR-10, CIFAR-100, SVHN, and CUB-200-2011, respectively. - `A`, `B`, `C`, `D`, and `E` means having a pre-trained model for corresponding datasets. | Model | [Gluon](gluon/README.md) | [PyTorch](https://github.com/osmr/pytorchcv/blob/master/README.md) | [Chainer](chainer_/README.md) | [Keras](keras_/README.md) | [TF](tensorflow_/README.md) | [TF2](tensorflow2/README.md) | Paper | Repo | Year | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | AlexNet | A | A | A | A | A | A | [link](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) | [link](https://code.google.com/archive/p/cuda-convnet2) | 2012 | | ZFNet | A | A | A | A | A | A | [link](https://arxiv.org/abs/1311.2901) | - | 2013 | | VGG | A | A | A | A | A | A | [link](https://arxiv.org/abs/1409.1556) | - | 2014 | | BN-VGG | A | A | A | A | A | A | [link](https://arxiv.org/abs/1409.1556) | - | 2015 | | BN-Inception | A | A | A | - | - | A | [link](https://arxiv.org/abs/1502.03167) | - | 2015 | | ResNet | ABCDE | ABCDE | ABCDE | A | A | ABCDE | [link](https://arxiv.org/abs/1512.03385) | [link](https://gi
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:be7545a2df98bce6, topic:deep-learning, topic:neural-network, topic:pytorch
