Image and video datasets and models for mxnet deep learning
Project description
# MXbox: Simple, efficient and flexible vision toolbox for mxnet framework.
MXbox is a toolbox aiming to provide a general and simple interface for vision tasks. This project is greatly inspired by [PyTorch](https://github.com/pytorch/pytorch) and [torchvision](https://github.com/pytorch/vision). Detailed copyright files are on the way. Improvements and suggestions are welcome.
## Installation
```bash
pip install mxbox
```
## Features
1. Define **preprocess** as a flow
```python
transform = transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.mx.ToNdArray(),
transforms.mx.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
])
```
PS: By default, mxbox uses `PIL` to read and transform images. But it also supports other backends like `accimage` and `skimage`.
More examples can be found in XXX.
2) Build **DataLoader** in several lines
```python
feedin_shapes = {
'batch_size': 8,
'data': [mx.io.DataDesc(name='data', shape=(8, 3, 32, 32), layout='NCHW')],
'label': [mx.io.DataDesc(name='softmax_label', shape=(8, 1), layout='N')]
}
dst = Dataset(root='../../data', transform=img_transform, label_transform=label_transform)
loader = DataLoader(dst, feedin_shapes, threads=8, shuffle=True)
```
Also, common datasets such as `cifar10`, `cifar100`, `SVHN`, `MNIST` are out-of-the-box. You can simply load them from `mxbox.datasets`.
3) Load popular model with pretrained weights
```python
vgg = mxbox.models.vgg(num_classes=10, pretrained=True)
resnet = mxbox.models.resnet152(num_classes=10, pretrained=True)
```
## Documentation
Under construction, coming soon.
## TODO list
1) Efficient multi-thread reading (Prefetch wanted
2) Common Models preparation.
3) More friendly error logging.
MXbox is a toolbox aiming to provide a general and simple interface for vision tasks. This project is greatly inspired by [PyTorch](https://github.com/pytorch/pytorch) and [torchvision](https://github.com/pytorch/vision). Detailed copyright files are on the way. Improvements and suggestions are welcome.
## Installation
```bash
pip install mxbox
```
## Features
1. Define **preprocess** as a flow
```python
transform = transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.mx.ToNdArray(),
transforms.mx.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
])
```
PS: By default, mxbox uses `PIL` to read and transform images. But it also supports other backends like `accimage` and `skimage`.
More examples can be found in XXX.
2) Build **DataLoader** in several lines
```python
feedin_shapes = {
'batch_size': 8,
'data': [mx.io.DataDesc(name='data', shape=(8, 3, 32, 32), layout='NCHW')],
'label': [mx.io.DataDesc(name='softmax_label', shape=(8, 1), layout='N')]
}
dst = Dataset(root='../../data', transform=img_transform, label_transform=label_transform)
loader = DataLoader(dst, feedin_shapes, threads=8, shuffle=True)
```
Also, common datasets such as `cifar10`, `cifar100`, `SVHN`, `MNIST` are out-of-the-box. You can simply load them from `mxbox.datasets`.
3) Load popular model with pretrained weights
```python
vgg = mxbox.models.vgg(num_classes=10, pretrained=True)
resnet = mxbox.models.resnet152(num_classes=10, pretrained=True)
```
## Documentation
Under construction, coming soon.
## TODO list
1) Efficient multi-thread reading (Prefetch wanted
2) Common Models preparation.
3) More friendly error logging.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
mxbox-0.0.22-py2.py3-none-any.whl
(33.2 kB
view hashes)
Close
Hashes for mxbox-0.0.22-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d75aad70c4c00b17b750f3e063b1b4ac4e85f2a86ba961dfadfc63ab135ce299 |
|
MD5 | 5ab2ce456be92a8fd1e75a3d3bd5e512 |
|
BLAKE2b-256 | 598516e5858f761c2d876d0af1df38dd8b96d0715c503cc77ebc4570729b8465 |