GitHub - zhanghang1989/gluoncv-torch: PyTorch API for GluonCV Models
source link: https://github.com/zhanghang1989/gluoncv-torch
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README.md
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 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 86.2Results on ADE20K dataset:
Model Base Network PixAcc mIoU FCN ResNet101 80.6 41.6 PSPNet ResNet101 80.8 42.9 DeepLabV3 ResNet101 81.1 44.1Quick 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.
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