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        "author": "Lyken from TuSimple",
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        "description": "# MXbox: Simple, efficient and flexible vision toolbox for mxnet framework.\n\nMXbox 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.\n\n\n## Installation\n```bash\npip install mxbox\n```\n\n## Features\n1. Define **preprocess** as a flow\n\n```python\ntransform = transforms.Compose([\n    transforms.RandomSizedCrop(224),\n    transforms.RandomHorizontalFlip(),\n    transforms.mx.ToNdArray(),\n    transforms.mx.Normalize(mean = [ 0.485, 0.456, 0.406 ],\n                            std  = [ 0.229, 0.224, 0.225 ]),\n])\n```\n\nPS: By default, mxbox uses `PIL` to read and transform images. But it also supports other backends like `accimage` and `skimage`.\n\nMore examples can be found in XXX.\n\n2) Build **DataLoader** in several lines\n\n```python\nfeedin_shapes = {\n    'batch_size': 8,\n    'data': [mx.io.DataDesc(name='data', shape=(8, 3, 32, 32), layout='NCHW')],\n    'label': [mx.io.DataDesc(name='softmax_label', shape=(8, 1), layout='N')]\n}\n\ndst = Dataset(root='../../data', transform=img_transform, label_transform=label_transform)\nloader = DataLoader(dst, feedin_shapes, threads=8, shuffle=True)\n```\n\nAlso, common datasets such as `cifar10`, `cifar100`, `SVHN`, `MNIST` are out-of-the-box. You can simply load them from `mxbox.datasets`.  \n\n3) Load popular model with pretrained weights\n\n```python\nvgg = mxbox.models.vgg(num_classes=10, pretrained=True)\nresnet = mxbox.models.resnet152(num_classes=10, pretrained=True)\n```\n\n\n\n##  Documentation\n\nUnder construction, coming soon.\n\n\n## TODO list\n\n1) Efficient multi-thread reading (Prefetch wanted\n\n2) Common Models preparation.\n\n3) More friendly error logging.\n\n",
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