soeaver / mxnet-model

Mxnet models (includimg classification, detection and segmentation) and deploy files for famouse networks

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mxnet-model

MXNet models (only classification for now) and deploy prototxt for resnet, resnext, inception_v3, inception_v4, inception_resnet, wider_resnet, densenet, DPNs and other networks.

We recommend using these MXNet models with official MXNet repository

Please install MXNet for evaluating and finetuning.

Disclaimer

Most of the pre-train models are converted from other projects, the main contribution belongs to the original authors.

Project links:

pytorch-classificationmxnet-model-gallerytensorflow slimcraftGBDResNeXtkeras deep-learning-modelsDPNSSenet

CLS (Classification, more details are in cls)

Performance on imagenet validation.

Top-1/5 error of pre-train models in this repository (Pre-train models download urls).

Network 224/299
(single-crop)
320/395
(single-crop)
Download
resnet101_v2 21.89/6.01 20.44/5.22 170.3MB(BaiduCloud)
resnet152_v2 20.71/5.42 19.65/4.75 230.2MB(BaiduCloud)
resnet269_v2 19.73/4.99 18.64/4.33 390.4MB(BaiduCloud)
resnext50_32x4d 22.38/6.31 21.10/5.52 95.8MB(BaiduCloud)
resnext101_32x4d 21.33/5.80 19.92/4.97 169.1MB(BaiduCloud)
resnext101_64x4d 20.60/5.41 19.26/4.63 391.2MB(BaiduCloud)
inception_v3 21.70/5.75 .. 91.1MB(BaiduCloud)
inception_v4 20.03/5.09 .. 163.1MB(BaiduCloud)
inception_resnet_v2 19.86/4.83 .. 213.3MB(BaiduCloud)
xception 20.89/5.48 .. 87.4MB(BaiduCloud)
air101 21.32/5.77 .. 246.2MB(BaiduCloud)
dpn-92-extra 19.98/5.06 19.00/4.37 145MB(GoogleDrive)
dpn107-extra 19.75/4.94 18.34/4.19 333MB(GoogleDrive)
se-resnet50 22.39/6.37 20.50/5.23 107.4MB(BaiduCloud)
se-resnext50-32x4d 20.96/5/54 19.35/4.66 105.4MB(BaiduCloud)
se-inception-v2 23.64/7.05 21.61/5.88 45.6MB(BaiduCloud)
  • 224x224(base_size=256) and 320x320(base_size=320) crop size for resnet-v2/resnext, 299x299(base_size=320) and 395x395(base_size=395) crop size for inception.

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Mxnet models (includimg classification, detection and segmentation) and deploy files for famouse networks


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