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-torchAvailable 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.