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MXNet Gluon CV Toolkit

Project description

Load GluonCV Models in PyTorch. Simply import gluoncvth to getting better pretrained model than torchvision:

import gluoncvth as gcv
model = gcv.models.resnet50(pretrained=True)


pip install gluoncv-torch

Available Models


ImageNet models single-crop error rates, comparing to the torchvision models:

  torchvision   gluoncvth  
Model Top-1 error Top-5 error Top-1 error Top-5 error
ResNet18 30.24 10.92 29.06 10.17
ResNet34 26.70 8.58 25.35 7.92
ResNet50 23.85 7.13 22.33 6.18
ResNet101 22.63 6.44 20.80 5.39
ResNet-152 21.69 5.94 20.56 5.39
Inception v3 22.55 6.44 21.33 5.61

More models available at GluonCV Image Classification ModelZoo

Semantic Segmentation

Results on Pascal VOC dataset:

Model Base Network mIoU
FCN ResNet101 83.6
PSPNet ResNet101 85.1
DeepLabV3 ResNet101 86.2

Results on ADE20K dataset:

Model Base Network PixAcc mIoU
FCN ResNet101 80.6 41.6
PSPNet ResNet101 80.8 42.9
DeepLabV3 ResNet101 81.1 44.1

Quick Demo

import torch
import gluoncvth

# Get the model
model = gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)

# Prepare the image
url = '' + \
filename = 'example.jpg'
img = gluoncvth.utils.load_image(, filename)).unsqueeze(0)

# Make prediction
output = model.evaluate(img)
predict = torch.max(output, 1)[1].cpu().numpy() + 1

# Get color pallete for visualization
mask = gluoncvth.utils.get_mask_pallete(predict, 'ade20k')'output.png')

More models available at GluonCV Semantic Segmentation ModelZoo

API Reference


  • gluoncvth.models.resnet18(pretrained=True)
  • gluoncvth.models.resnet34(pretrained=True)
  • gluoncvth.models.resnet50(pretrained=True)
  • gluoncvth.models.resnet101(pretrained=True)
  • gluoncvth.models.resnet152(pretrained=True)


  • gluoncvth.models.get_fcn_resnet101_voc(pretrained=True)
  • gluoncvth.models.get_fcn_resnet101_ade(pretrained=True)


  • gluoncvth.models.get_psp_resnet101_voc(pretrained=True)
  • gluoncvth.models.get_psp_resnet101_ade(pretrained=True)


  • gluoncvth.models.get_deeplab_resnet101_voc(pretrained=True)
  • gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)

Why GluonCV?

1. State-of-the-art Implementations

2. Pretrained Models and Tutorials

3. Community Support

We expect this PyTorch inference API for GluonCV models will be beneficial to the entire computer vision comunity.

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
gluoncv-torch-0.0.2.tar.gz (15.9 kB) Copy SHA256 hash SHA256 Source None Oct 12, 2018

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