gluoncv-torch 0.0.5b20200628

MXNet Gluon CV Toolkit

PyTorch-Encoding

GluonCV-Torch

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)

Installation:

pip install gluoncv-torch

Available Models

ImageNet

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
ResNet152	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)
model.eval()

# Prepare the image
url = 'https://github.com/zhanghang1989/image-data/blob/master/encoding/' + \
    'segmentation/ade20k/ADE_val_00001142.jpg?raw=true'
filename = 'example.jpg'
img = gluoncvth.utils.load_image(
    gluoncvth.utils.download(url, 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')
mask.save('output.png')

More models available at GluonCV Semantic Segmentation ModelZoo

API Reference

ResNet

• 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)

FCN

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

PSPNet

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

DeepLabV3

• 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.