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EfficientNet
EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster. Trained with mixed precision using Tensor Cores.
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Model Description

EfficientNet is an image classification model family. It was first described in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. This notebook allows you to load and test the EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0 and, EfficientNet-WideSE-B4 models.

EfficientNet-WideSE models use Squeeze-and-Excitation layers wider than original EfficientNet models, the width of SE module is proportional to the width of Depthwise Separable Convolutions instead of block width.

WideSE models are slightly more accurate than original models.

This model is trained with mixed precision using Tensor Cores on Volta and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

We use NHWC data layout when training using Mixed Precision.

Example

In the example below we will use the pretrained EfficientNet model to perform inference on image and present the result.

To run the example you need some extra python packages installed. These are needed for preprocessing images and visualization.

!pip install validators matplotlib
import torch
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import json
import requests
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
print(f'Using {device} for inference')

Load the model pretrained on ImageNet dataset.

You can choose among the following models:

TorchHub entrypoint Description
nvidia_efficientnet_b0 baseline EfficientNet
nvidia_efficientnet_b4 scaled EfficientNet
nvidia_efficientnet_widese_b0 model with Squeeze-and-Excitation layers wider than baseline EfficientNet model
nvidia_efficientnet_widese_b4 model with Squeeze-and-Excitation layers wider than scaled EfficientNet model

There are also quantized version of the models, but they require nvidia container. See quantized models

efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)
utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')

efficientnet.eval().to(device)

Prepare sample input data.

uris = [
    'http://images.cocodataset.org/test-stuff2017/000000024309.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000028117.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000006149.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000004954.jpg',
]

batch = torch.cat(
    [utils.prepare_input_from_uri(uri) for uri in uris]
).to(device)

Run inference. Use pick_n_best(predictions=output, n=topN) helper function to pick N most probable hypotheses according to the model.

with torch.no_grad():
    output = torch.nn.functional.softmax(efficientnet(batch), dim=1)
    
results = utils.pick_n_best(predictions=output, n=5)

Display the result.

for uri, result in zip(uris, results):
    img = Image.open(requests.get(uri, stream=True).raw)
    img.thumbnail((256,256), Image.ANTIALIAS)
    plt.imshow(img)
    plt.show()
    print(result)

Details

For detailed information on model input and output, training recipies, inference and performance visit: github and/or NGC

References