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Posted to commits@mxnet.apache.org by zh...@apache.org on 2018/05/01 17:28:39 UTC
[incubator-mxnet] branch master updated: Improve resnet18 and
resnet34 pre-trained models (#10752)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 8585ef5 Improve resnet18 and resnet34 pre-trained models (#10752)
8585ef5 is described below
commit 8585ef5b940ceac9ace85e50004f4729c44d702e
Author: Tong He <he...@gmail.com>
AuthorDate: Tue May 1 10:28:33 2018 -0700
Improve resnet18 and resnet34 pre-trained models (#10752)
* fix #9865
* add unittest
* fix format
* fix format
* fix superfluous loop in metric
* fix lint
* update resnet18/34 in the gluon model zoo
* correct values
---
docs/api/python/gluon/model_zoo.md | 8 ++++----
python/mxnet/gluon/model_zoo/model_store.py | 8 ++++----
2 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/docs/api/python/gluon/model_zoo.md b/docs/api/python/gluon/model_zoo.md
index b4aa176..cb12fab 100644
--- a/docs/api/python/gluon/model_zoo.md
+++ b/docs/api/python/gluon/model_zoo.md
@@ -42,13 +42,13 @@ The following table summarizes the available models.
| mobilenet0.5 | [MobileNet 0.5](https://arxiv.org/abs/1704.04861) | 1,342,536 | 0.6307 | 0.8475 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| mobilenet0.75 | [MobileNet 0.75](https://arxiv.org/abs/1704.04861) | 2,601,976 | 0.6738 | 0.8782 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| mobilenet1.0 | [MobileNet 1.0](https://arxiv.org/abs/1704.04861) | 4,253,864 | 0.7105 | 0.9006 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
-| resnet18_v1 | [ResNet-18 V1](http://arxiv.org/abs/1512.03385) | 11,699,112 | 0.6803 | 0.8818 | Converted from pytorch vision |
-| resnet34_v1 | [ResNet-34 V1](http://arxiv.org/abs/1512.03385) | 21,814,696 | 0.7202 | 0.9066 | Converted from pytorch vision |
+| resnet18_v1 | [ResNet-18 V1](http://arxiv.org/abs/1512.03385) | 11,699,112 | 0.7039 | 0.8959 | Trained with [script](https://github.com/dmlc/gluon-cv/blob/15ed8a4c71d411b878f0d71d1c7afdce6710c913/scripts/classification/imagenet/train_imagenet.py) |
+| resnet34_v1 | [ResNet-34 V1](http://arxiv.org/abs/1512.03385) | 21,814,696 | 0.7411 | 0.9184 | Trained with [script](https://github.com/dmlc/gluon-cv/blob/15ed8a4c71d411b878f0d71d1c7afdce6710c913/scripts/classification/imagenet/train_imagenet.py) |
| resnet50_v1 | [ResNet-50 V1](http://arxiv.org/abs/1512.03385) | 25,629,032 | 0.7540 | 0.9266 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| resnet101_v1 | [ResNet-101 V1](http://arxiv.org/abs/1512.03385) | 44,695,144 | 0.7693 | 0.9334 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| resnet152_v1 | [ResNet-152 V1](http://arxiv.org/abs/1512.03385) | 60,404,072 | 0.7727 | 0.9353 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
-| resnet18_v2 | [ResNet-18 V2](https://arxiv.org/abs/1603.05027) | 11,695,796 | 0.6961 | 0.8901 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/4dcd96ae2f6820e01455079d00f49db1cd21eda9/example/gluon/image_classification.py) |
-| resnet34_v2 | [ResNet-34 V2](https://arxiv.org/abs/1603.05027) | 21,811,380 | 0.7324 | 0.9125 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/4dcd96ae2f6820e01455079d00f49db1cd21eda9/example/gluon/image_classification.py) |
+| resnet18_v2 | [ResNet-18 V2](https://arxiv.org/abs/1603.05027) | 11,695,796 | 0.7037 | 0.8962 | Trained with [script](https://github.com/dmlc/gluon-cv/blob/15ed8a4c71d411b878f0d71d1c7afdce6710c913/scripts/classification/imagenet/train_imagenet.py) |
+| resnet34_v2 | [ResNet-34 V2](https://arxiv.org/abs/1603.05027) | 21,811,380 | 0.7389 | 0.9178 | Trained with [script](https://github.com/dmlc/gluon-cv/blob/15ed8a4c71d411b878f0d71d1c7afdce6710c913/scripts/classification/imagenet/train_imagenet.py) |
| resnet50_v2 | [ResNet-50 V2](https://arxiv.org/abs/1603.05027) | 25,595,060 | 0.7622 | 0.9297 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| resnet101_v2 | [ResNet-101 V2](https://arxiv.org/abs/1603.05027) | 44,639,412 | 0.7747 | 0.9375 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
| resnet152_v2 | [ResNet-152 V2](https://arxiv.org/abs/1603.05027) | 60,329,140 | 0.7833 | 0.9409 | Trained with [script](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py) |
diff --git a/python/mxnet/gluon/model_zoo/model_store.py b/python/mxnet/gluon/model_zoo/model_store.py
index 208b1a1..6dc57ac 100644
--- a/python/mxnet/gluon/model_zoo/model_store.py
+++ b/python/mxnet/gluon/model_zoo/model_store.py
@@ -35,13 +35,13 @@ _model_sha1 = {name: checksum for checksum, name in [
('8e9d539cc66aa5efa71c4b6af983b936ab8701c3', 'mobilenet0.5'),
('529b2c7f4934e6cb851155b22c96c9ab0a7c4dc2', 'mobilenet0.75'),
('6b8c5106c730e8750bcd82ceb75220a3351157cd', 'mobilenet1.0'),
- ('38d6d423c22828718ec3397924b8e116a03e6ac0', 'resnet18_v1'),
- ('4dc2c2390a7c7990e0ca1e53aeebb1d1a08592d1', 'resnet34_v1'),
+ ('e54b379f50fa4b10bbd2506237e3bd74e6164778', 'resnet18_v1'),
+ ('c1dc0967a3d25ee9127e03bc1046a5d44d92e2ba', 'resnet34_v1'),
('c940b1a062b32e3a5762f397c9d1e178b5abd007', 'resnet50_v1'),
('d992389084bc5475c370e9b52c3561706e755799', 'resnet101_v1'),
('48ce7775d375987d019ec9aa96bc43b98165dfcb', 'resnet152_v1'),
- ('8aacf80ff4014c1efa2362a963ac5ec82cf92d5b', 'resnet18_v2'),
- ('0ed3cd06da41932c03dea1de7bc2506ef3fb97b3', 'resnet34_v2'),
+ ('84f666402577b5b79cd59eba5d3ba0bc1edf2152', 'resnet18_v2'),
+ ('5da34c2772893e9d680d5fa0bd6d432eba8689c9', 'resnet34_v2'),
('81a4e66af7859a5aa904e2b4051aa0d3bc472b2f', 'resnet50_v2'),
('7eb2b3cde097883c11941b927048a705ed334294', 'resnet101_v2'),
('64c75ac8c292f6ac54f873f9ef62e0531105878b', 'resnet152_v2'),
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