EfficientNets

[1] Mingxing Tan and Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019. Arxiv link: https://arxiv.org/abs/1905.11946.

Updates

• [Aug 2019] Released EfficientNet-EdgeTPU models: README and blog post.

• [Jul 2019] Released new EfficientNet checkpoints trained with AutoAugment (better accuracy than paper).

• [May 2019] Released EfficientNets code and weights: blog post

1. About EfficientNet Models

EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.

We develop EfficientNets based on AutoML and Compound Scaling. In particular, we first use AutoML MNAS Mobile framework to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to B7.

EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency:

• In high-accuracy regime, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS, being 8.4x smaller and 6.1x faster on CPU inference than previous best Gpipe.

• In middle-accuracy regime, our EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than ResNet-152, with similar ImageNet accuracy.

• Compared with the widely used ResNet-50, our EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint.

2. Using Pretrained EfficientNet Checkpoints

We have provided a list of EfficientNet checkpoints for EfficientNet checkpoints: both w/ and w/o AutoAugment.

• With standard ResNet preprocessing, we achieved similar accuracy as the original ICML paper.
• WIth AutoAugment preprocessing, we achieved higher accuracy than the original ICML paper.

	B0	B1	B2	B3	B4	B5	B6	B7
Standard preprocessing	76.8% (ckpt)	78.8% (ckpt)	79.8% (ckpt)	81.0% (ckpt)	82.6% (ckpt)	83.2% (ckpt)
AutoAugment	77.3% (ckpt)	79.2% (ckpt)	80.3% (ckpt)	81.7% (ckpt)	83.0% (ckpt)	83.7% (ckpt)	84.2% (ckpt)	84.5% (ckpt)

^{**To train EfficientNets with AutoAugment (code), simply add option "--autoaugment_name=v0". If you use these checkpoints, you can cite this paper.}

A quick way to use these checkpoints is to run:

$ export MODEL=efficientnet-b0
$ wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/${MODEL}.tar.gz
$ tar xf ${MODEL}.tar.gz
$ wget https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG -O panda.jpg
$ wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/eval_data/labels_map.txt
$ python eval_ckpt_main.py --model_name=$MODEL --ckpt_dir=$MODEL --example_img=panda.jpg --labels_map_file=labels_map.txt

Please refer to the following colab for more instructions on how to obtain and use those checkpoints.

• eval_ckpt_example.ipynb: A colab example to load EfficientNet pretrained checkpoints files and use the restored model to classify images.

3. Using EfficientNet as Feature Extractor

    import efficientnet_builder
    features, endpoints = efficientnet_builder.build_model_base(images, 'efficientnet-b0')

• Use features for classification finetuning.
• Use endpoints['reduction_i'] for detection/segmentation, as the last intermediate feature with reduction level i. For example, if input image has resolution 224x224, then:
  • endpoints['reduction_1'] has resolution 112x112
  • endpoints['reduction_2'] has resolution 56x56
  • endpoints['reduction_3'] has resolution 28x28
  • endpoints['reduction_4'] has resolution 14x14
  • endpoints['reduction_5'] has resolution 7x7

4. Training EfficientNets on TPUs.

To train this model on Cloud TPU, you will need:

• A GCE VM instance with an associated Cloud TPU resource
• A GCS bucket to store your training checkpoints (the "model directory")
• Install TensorFlow version >= 1.13 for both GCE VM and Cloud.

Then train the model:

$ export PYTHONPATH="$PYTHONPATH:/path/to/models"
$ python main.py --tpu=TPU_NAME --data_dir=DATA_DIR --model_dir=MODEL_DIR

# TPU_NAME is the name of the TPU node, the same name that appears when you run gcloud compute tpus list, or ctpu ls.
# MODEL_DIR is a GCS location (a URL starting with gs:// where both the GCE VM and the associated Cloud TPU have write access
# DATA_DIR is a GCS location to which both the GCE VM and associated Cloud TPU have read access.

For more instructions, please refer to our tutorial: https://cloud.google.com/tpu/docs/tutorials/efficientnet