zcdliuwei / efficientnet

EfficientNets snapshot

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[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.

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 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-B0, EfficientNet-B1, EfficientNet-B2, and EfficientNet-B3. A quick way to use these checkpoints is to run:

$ export MODEL=efficientnet-b0
$ wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/${MODEL}.tar.gz
$ tar zxf ${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. 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

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EfficientNets snapshot


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