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Posted to commits@singa.apache.org by jo...@apache.org on 2021/01/11 07:52:53 UTC
[singa-doc] branch master updated: Update onnx.md
This is an automated email from the ASF dual-hosted git repository.
joddiyzhang pushed a commit to branch master
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The following commit(s) were added to refs/heads/master by this push:
new e52afda Update onnx.md
e52afda is described below
commit e52afdaf890e42842152b03fc8e219d9e85fa516
Author: Joddiy Zhang <jo...@qq.com>
AuthorDate: Mon Jan 11 15:52:46 2021 +0800
Update onnx.md
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1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/docs-site/docs/onnx.md b/docs-site/docs/onnx.md
index 877bf9a..25ffe0b 100644
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@@ -199,7 +199,7 @@ pencil, and many animals.
| <b>[MobileNet](https://github.com/onnx/models/tree/master/vision/classification/mobilenet)</b> | [Sandler et al.](https://arxiv.org/abs/1801.04381) | Light-weight deep neural network best suited for mobile and embedded vision applications. <br>Top-5 error from paper - ~10% | [](https://colab.res [...]
| <b>[ResNet18](https://github.com/onnx/models/tree/master/vision/classification/resnet)</b> | [He et al.](https://arxiv.org/abs/1512.03385) | A CNN model (up to 152 layers). Uses shortcut connections to achieve higher accuracy when classifying images. <br> Top-5 error from paper - ~3.6% | [](https://colab.res [...]
| <b>[VGG16](https://github.com/onnx/models/tree/master/vision/classification/vgg)</b> | [Simonyan et al.](https://arxiv.org/abs/1409.1556) | Deep CNN model(up to 19 layers). Similar to AlexNet but uses multiple smaller kernel-sized filters that provides more accuracy when classifying images. <br>Top-5 error from paper - ~8% | [](https://colab.res [...]
-| <b>[ShuffleNet_V2](https://github.com/onnx/models/tree/master/vision/classification/shufflenet)</b> | [Simonyan et al.](https://arxiv.org/pdf/1707.01083.pdf) | Extremely computation efficient CNN model that is designed specifically for mobile devices. This network architecture design considers direct metric such as speed, instead of indirect metric like FLOP. Top-1 error from paper - ~30.6% | [</b> | [Simonyan et al.](https://arxiv.org/pdf/1707.01083.pdf) | Extremely computation efficient CNN model that is designed specifically for mobile devices. This network architecture design considers direct metric such as speed, instead of indirect metric like FLOP. Top-1 error from paper - ~30.6% | [](https://colab.res [...]
We also give some re-training examples by using VGG and ResNet, please check
`examples/onnx/training`.
@@ -232,7 +232,7 @@ given context paragraph.
| ----------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- [...]
| <b>[BERT-Squad](https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad)</b> | [Devlin et al.](https://arxiv.org/pdf/1810.04805.pdf) | This model answers questions based on the context of the given input paragraph. | [](https://colab.research.google.com/drive/1kud-lUPjS_u-TkDAzi [...]
| <b>[RoBERTa](https://github.com/onnx/models/tree/master/text/machine_comprehension/roberta)</b> | [Devlin et al.](https://arxiv.org/pdf/1907.11692.pdf) | A large transformer-based model that predicts sentiment based on given input text. | [](https://colab.research.google.com/drive/1F-c4LJSx3Cb2jW6tP7 [...]
-| <b>[GPT-2](https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2)</b> | [Devlin et al.](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | A large transformer-based language model that given a sequence of words within some text, predicts the next word. | [ [...]
+| <b>[GPT-2](https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2)</b> | [Devlin et al.](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | A large transformer-based language model that given a sequence of words within some text, predicts the next word. | [](https://colab.research.google.com/drive/1ZlXLSIMppPch6HgzKR [...]
## Supported operators