https://github.com/jahongir7174/efficientnetv2

EfficientNetV2 implementation using PyTorch
https://github.com/jahongir7174/efficientnetv2

efficientnetv2 imagenet pytorch training

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EfficientNetV2 implementation using PyTorch

• Host: GitHub
• URL: https://github.com/jahongir7174/efficientnetv2
• Owner: jahongir7174
• License: apache-2.0
• Created: 2021-04-12T12:16:37.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-07-12T05:21:41.000Z (over 2 years ago)
• Last Synced: 2023-03-09T08:11:02.821Z (over 1 year ago)
• Topics: efficientnetv2, imagenet, pytorch, training
• Language: Python
• Homepage: https://arxiv.org/abs/2104.00298
• Size: 74.8 MB
• Stars: 91
• Watchers: 2
• Forks: 23
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[EfficientNetV2](https://arxiv.org/abs/2104.00298) implementation using PyTorch

### Steps

* `imagenet` path by changing `data_dir` in `main.py`

* `bash ./main.sh $ --train` for training model, `$` is number of GPUs

* `EfficientNet` class in `nets/nn.py` for different versions

### Note

* the default training configuration is for `EfficientNetV2-S`

### Parameters and FLOPS

* `python main.py --benchmark`

```

Number of parameters: 21458488

Time per operator type:

        1504.95 ms.    80.5982%. Conv

        225.509 ms.    12.0772%. Sigmoid

        115.112 ms.     6.1649%. Mul

        12.7341 ms.   0.681982%. Add

        7.50523 ms.   0.401946%. AveragePool

        1.40185 ms.  0.0750768%. FC

      0.0112697 ms. 0.000603555%. Flatten

        1867.22 ms in Total

FLOP per operator type:

        16.7287 GFLOP.     99.708%. Conv

      0.0412707 GFLOP.   0.245986%. Mul

     0.00516096 GFLOP.  0.0307609%. Add

       0.002561 GFLOP.  0.0152643%. FC

        16.7777 GFLOP in Total

Feature Memory Read per operator type:

        291.409 MB.    51.8224%. Mul

        224.497 MB.    39.9231%. Conv

        41.2877 MB.    7.34234%. Add

        5.12912 MB.   0.912131%. FC

        562.323 MB in Total

Feature Memory Written per operator type:

        165.083 MB.    50.2087%. Mul

        143.062 MB.    43.5114%. Conv

        20.6438 MB.    6.27867%. Add

          0.004 MB. 0.00121657%. FC

        328.793 MB in Total

Parameter Memory per operator type:

        79.9537 MB.    93.9773%. Conv

          5.124 MB.    6.02273%. FC

              0 MB.          0%. Add

              0 MB.          0%. Mul

        85.0777 MB in Total

```

### Results

* `python main.py --test` for trained model testing

|       name       | resolution | acc@1 | acc@5 | #params |  FLOPS  | resample | training loss |

|:----------------:|:----------:|:-----:|:-----:|:-------:|:-------:|---------:|--------------:|

| EfficientNetV2-S |  384x384   | 83.9  | 96.7  |  21.46  | 16.7777 | BILINEAR |  CrossEntropy |

| EfficientNetV2-S |  384x384   |   -   |   -   |  21.46  | 16.7777 | BILINEAR |      PolyLoss |

| EfficientNetV2-M |     -      |   -   |   -   |    -    |    -    |        - |             - |

| EfficientNetV2-L |     -      |   -   |   -   |    -    |    -    |        - |             - |