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Efficient Net
Scaled neural network.
Efficient nets are the family of neural networks with the baseline model constructed with Neural Architecture Search.
Neural Architecture Search is a technique for automating the design of artificial neural networks.
The type of artificial neural network that can be designed depends on the search space .
With the help of a neural architecture search that optimizes both accuracy and FLOP (Floating point Operation), we firstly create the baseline model called EfficientNET-B0.
Starting with this baseline model, we perform compound scaling on it and create family of Efficient net models from EfficientNetB1 to B7.
Compound Scaling was the major topic of the paper where Efficient Net was introduced which dealt with scaling the neural network in depth, width and resolution to increase its accuracy.
The neural networks proved to work with less parameters and with more accuracy compared to other state of the art neural networks.
Comparison of Efficient Net with different neural nets on Imagenet. Source:https://arxiv.org/abs/1905.11946
We can see that the number of parameters in the Efficient net family is significantly low compared to other models.
Parameters
EfficientNet sub type
The hasty tool lets you choose from these different Efficient net.
Note that there is a trade off between the number of parameters and accuracy going from EfficientNetB1 to B7.
Weight
It is the weight that is used for model initialization. Here, we use the the weights of the EfficientNetB0 found on Image Net dataset.
Code Implementation
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import numpy as np
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import pandas as pd
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import os
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import matplotlib.image as mpimg
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchvision
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from torch.utils.data import DataLoader, Dataset
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import torch.utils.data as utils
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from torchvision import transforms
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import matplotlib.pyplot as plt
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%matplotlib inline
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import warnings
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warnings.filterwarnings("ignore")
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data_dir = '../input'
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train_dir = data_dir + '/train/train/'
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test_dir = data_dir + '/test/test/'
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labels = pd.read_csv("../input/train.csv")
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labels.head()
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class ImageData(Dataset):
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def __init__(self, df, data_dir, transform):
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super().__init__()
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self.df = df
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self.data_dir = data_dir
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self.transform = transform
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def __len__(self):
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return len(self.df)
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def __getitem__(self, index):
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img_name = self.df.id[index]
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label = self.df.has_cactus[index]
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img_path = os.path.join(self.data_dir, img_name)
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image = mpimg.imread(img_path)
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image = self.transform(image)
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return image, label
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data_transf = transforms.Compose([transforms.ToPILImage(), transforms.ToTensor()])
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train_data = ImageData(df = labels, data_dir = train_dir, transform = data_transf)
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train_loader = DataLoader(dataset = train_data, batch_size = 64)
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from efficientnet_pytorch import EfficientNet
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model = EfficientNet.from_name('efficientnet-b1')
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# Unfreeze model weights
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for param in model.parameters():
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param.requires_grad = True
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num_ftrs = model._fc.in_features
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model._fc = nn.Linear(num_ftrs, 1)
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model = model.to('cuda')
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optimizer = optim.Adam(model.parameters())
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loss_func = nn.BCELoss()
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# Train model
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loss_log = []
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for epoch in range(5):
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model.train()
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for ii, (data, target) in enumerate(train_loader):
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data, target = data.cuda(), target.cuda()
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target = target.float()
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optimizer.zero_grad()
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output = model(data)
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m = nn.Sigmoid()
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loss = loss_func(m(output), target)
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loss.backward()
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optimizer.step()
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if ii % 1000 == 0:
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loss_log.append(loss.item())
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print('Epoch: {} - Loss: {:.6f}'.format(epoch + 1, loss.item()))
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Last modified 9mo ago
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