course/training_example/CNN/MobileNet

• MobileNetV2
• • Performance
  • • Precision comparison line chart
  • pretrained models
  • Training
  • • Parameter description
  • Examples
  • • Train
    • Eval
    • Infer

MobileNetV2

---

The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer.

The architectural definition of each network refers to the following papers:

[1] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv preprint arXiv: 1801.04381.

Performance

---

Precision comparison line chart

The following line chart shows the accuracy comparison of MobileNetV2 network in Top1 and Top5 of MindSpore and TensorFlow.

pretrained models

---

The following table lists all MobilenetV2 imagenet checkpoints. Each model verifies the accuracy
of Top-1 and Top-5, and compares it with that of TensorFlow.

	MindSpore	MindSpore	TensorFlow	TensorFlow
Model	Top-1 (%)	Top-5 (%)	Top-1 (%)	Top-5 (%)	Download	Config
mobilenet_v2_1.4_224	75.43	92.5	75.0	92.5	model	config
mobilenet_v2_1.3_224	74.7	92.3	74.4	92.1	model	config
mobilenet_v2_1.0_224	72.4	90.8	71.8	91.0	model	config
mobilenet_v2_1.0_192	71.3	90.2	70.7	90.1	model	config
mobilenet_v2_1.0_160	69.7	89.1	68.8	89.0	model	config
mobilenet_v2_1.0_128	67.1	86.9	65.3	86.9	model	config
mobilenet_v2_1.0_96	61.9	83.6	60.3	83.2	model	config
mobilenet_v2_0.75_224	70.3	89.4	69.8	89.6	model	config
mobilenet_v2_0.75_192	69.2	88.7	68.7	88.9	model	config
mobilenet_v2_0.75_160	67.3	87.4	66.4	87.3	model	config
mobilenet_v2_0.75_128	64.5	85.4	63.2	85.3	model	config
mobilenet_v2_0.75_96	59.4	81.6	58.8	81.6	model	config
mobilenet_v2_0.5_224	66.1	86.3	65.4	86.4	model	config
mobilenet_v2_0.5_192	64.7	85.2	63.9	85.4	model	config
mobilenet_v2_0.5_160	61.9	83.4	61.0	83.2	model	config
mobilenet_v2_0.5_128	58.7	80.9	57.7	80.8	model	config
mobilenet_v2_0.5_96	52.9	76.3	51.2	75.8	model	config
mobilenet_v2_0.35_224	61.9	83.2	60.3	82.9	model	config
mobilenet_v2_0.35_192	59.5	81.6	58.2	81.2	model	config
mobilenet_v2_0.35_160	57.6	79.8	55.7	79.1	model	config
mobilenet_v2_0.35_128	52.8	76.3	50.8	75.0	model	config
mobilenet_v2_0.35_96	47.5	71.3	45.5	70.4	model	config

Training

---

Parameter description

Parameter	Default	Description
device_target	GPU	Hardware device
data_url		Path to training dataset
pretrained	False	Path to pretrained model
run_distribute	True	Distributed parallel training
num_parallel_workers	8	Number of parallel workers
dataset_sink_mode	True	Data sinking mode
num_classes	1001	Number of dataset classifications
batch_size	64	Number of batch size
repeat_num	1	Number of data repetitions
momentum	0.9	Momentum parameter
epoch_size	200	Number of epoch
keep_checkpoint_max	10	Maximum number of checkpoints saved
ckpt_save_dir	'./mobilenet_v2'	Save path of checkpoint
lr_decay_mode	cosine_decay_lr	Learning rate decay mode
decay_epoch	200	Number of decay epoch
smooth_factor	0.1	Label smoothing factor
max_lr	0.1	maximum learning rate
min_lr	0.0	minimum learning rate
milestone		A list of milestone
learning_rates		A list of learning rates
alpha	1.0	Magnification factor
resize	224	Resize the height and weight of picture

Examples

---

Train

• The following configuration uses 8 GPUs for training. The magnification factor is set to 1.0 and the image input size is set to 224.

  mpirun -n 8 python mobilenet_v2_imagenet_train.py --alpha 1.0 --resize 224 --data_url ./dataset/imagenet
  

  output:

  Epoch:[0/200], step:[2502/2502], loss:[4.676/4.676], time:872084.093, lr:0.10000
  Epoch time:883614.453, per step time:353.163, avg loss:4.676
  Epoch:[1/200], step:[2502/2502], loss:[4.452/4.452], time:693370.244, lr:0.09998
  Epoch time:693374.709, per step time:277.128, avg loss:4.452
  Epoch:[2/200], step:[2502/2502], loss:[3.885/3.885], time:685880.388, lr:0.09990
  Epoch time:685884.401, per step time:274.134, avg loss:3.885
  Epoch:[3/200], step:[2502/2502], loss:[3.550/3.550], time:689409.851, lr:0.09978
  Epoch time:689413.237, per step time:275.545, avg loss:3.550
  Epoch:[4/200], step:[2502/2502], loss:[3.371/3.371], time:692162.583, lr:0.09961
  Epoch time:692166.163, per step time:276.645, avg loss:3.371
  
  ...
  

• The following configuration uses yaml file for training.

  mpirun -n 8 python examples/classification/tools/train_config.py -c mindvision/classification/config/mobilenet_v2/mobilenet_v2_1.0_224_imagenet2012.yaml
  

  output:

  Epoch:[0/200], step:[2502/2502], loss:[5.350/5.350], time:860471.865, lr:0.10000
  Epoch time:867121.671, per step time:346.571, avg loss:5.350
  Epoch:[1/200], step:[2502/2502], loss:[4.737/4.737], time:838193.601, lr:0.09998
  Epoch time:838236.939, per step time:335.027, avg loss:4.737
  Epoch:[2/200], step:[2502/2502], loss:[4.430/4.430], time:841103.726, lr:0.09990
  Epoch time:841167.664, per step time:336.198, avg loss:4.430
  Epoch:[3/200], step:[2502/2502], loss:[3.845/3.845], time:844526.365, lr:0.09978
  Epoch time:844609.499, per step time:337.574, avg loss:3.845
  Epoch:[4/200], step:[2502/2502], loss:[3.604/3.604], time:840851.149, lr:0.09961
  Epoch time:840912.141, per step time:336.096, avg loss:3.604
  
  ...
  

Eval

• The following configuration for eval. The magnification factor is set to 0.75 and the image input size is set to 192.

  python mobilenet_v2_imagenet_eval.py --alpha 0.75 --resize 192 --pretrained True --data_url ./dataset/imagenet
  

  output:

  {'Top_1_Accuracy': 0.6922876602564103, 'Top_5_Accuracy': 0.8871594551282052}
  

• The following configuration uses yaml file for eval.

  python examples/classification/tools/eval_config.py -c mindvision/classification/config/mobilenet_v2/mobilenet_v2_0.75_192_imagenet2012.yaml
  

  output:

  {'Top_1_Accuracy': 0.692441581306018, 'Top_5_Accuracy': 0.8872439180537772}
  

Infer

• The following configuration for infer. The magnification factor is set to 1.0 and the image input size is set to 224.

  python mobilenet_v2_imagenet_infer.py --alpha 1.0 --resize 224 --pretrained True --data_url ./infer
  

  output:

  {283: 'Persian cat'}