semvec/pypi-packages huggingface dataset:
django-oss2-storage 1.0.4
Support for aliyun oss2 storage backends in Django
License: BSD

===================
django-oss2-storage
===================

Installation
============
Installing from PyPI is as easy as doing::

  pip install django-oss2-storage

Once that is done add ``storages`` to your ``INSTALLED_APPS`` and set ``DEFAULT_FILE_STORAGE`` to the
backend of your choice. If, for example, you want to use the boto3 backend you would set::

  DEFAULT_FILE_STORAGE = 'oss2_storage.backends.Oss2MediaStorage'
mxbox 0.0.22
Image and video datasets and models for mxnet deep learning
License: BSD

# 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.