MXNet Gluon CV Toolkit
Project description
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file gluoncv-torch-0.0.4b6042020.tar.gz
.
File metadata
- Download URL: gluoncv-torch-0.0.4b6042020.tar.gz
- Upload date:
- Size: 15.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.8.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5caa00ded7a80cdbb81d3ac458f0af24098d2e53a58a3de4cc80fe81b5c499f7 |
|
MD5 | e68efcdb10f98a8aaa4ca0c20e3aa542 |
|
BLAKE2b-256 | b2df8e16995b0549e99f86422b08e55b71011983161fcf56801f0681494f35da |
File details
Details for the file gluoncv_torch-0.0.4b6042020-py3-none-any.whl
.
File metadata
- Download URL: gluoncv_torch-0.0.4b6042020-py3-none-any.whl
- Upload date:
- Size: 21.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.8.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | eab8cace5af1ac93cfad69a0e7ea31dfb43855f8ae11dab64d3bd5627e9d5a89 |
|
MD5 | 31ce6d1615a0ecd27acb4d38ded37bae |
|
BLAKE2b-256 | 268ac47700cb366584f95f234bcdbcc3079025bc1d5e9f6884d3a1b967be0ad8 |