mindcv3/configs/densenet

• DenseNet
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

DenseNet

Densely Connected Convolutional Networks

Introduction

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Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if
they contain shorter connections between layers close to the input and those close to the output. Dense Convolutional
Network (DenseNet) is introduced based on this observation, which connects each layer to every other layer in a
feed-forward fashion. Whereas traditional convolutional networks with $L$ layers have $L$ connections-one between each
layer and its subsequent layer, our network has $\frac{L(L+1)}{2}$ direct connections. For each layer, the feature-maps
of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers.
DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature
propagation, encourage feature reuse, and substantially reduce the number of parameters.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
DenseNet121	D910x8-G	75.64	92.84	8.06	238s/epoch	6.7ms/step	model	cfg	log
DenseNet161	D910x8-G	79.09	94.66	28.90	472s/epoch	8.7ms/step	model	cfg	log
DenseNet169	D910x8-G	77.26	93.71	14.30	313s/epoch	7.4ms/step	model	cfg	log
DenseNet201	D910x8-G	78.14	94.08	20.24	394s/epoch	7.9ms/step	model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml files in mindcv/configs/densenet folder. For example, to train with one of these configurations, you can run:

  # train densenet121 on 8 GPUs
  mpirun -n 8 python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/imagenet
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascends with the same batch size.

Detailed adjustable parameters and their default value can be seen in config.py.

Validation

• To validate the model, you can use validate.py. Here is an example for densenet121 to verify the accuracy of your
  training.

  python validate.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/densenet121.ckpt
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.