The LWTNet is using EfficientNet [15] as the convolutional base with the 3 dense layers as the classifier. This is a +0.5% absolute improvement over the Noisy student EfficientNet-B0 trained with 300M unlabeled images. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. For EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. … This quick sanity check ensures the training pipeline is working as expected. Object detection algorithms get the predicted box through the prior box technology, and then adjust parameters of prior boxes to obtain the result of the predicted box. This aids in overfitting . EfficientNet-L1 is now used as a teacher and EfficientNet-L2, which is the largest model, is used as a student. It has been rising in … Reduce the network size The simplest way to prevent overfitting is to reduce the size of the model: the number of learnable parameters in the model (which is determined by the number of layers and the number of units per layer). 2. Cross-Validation 4. Adversarial examples are commonly viewed as a threat to ConvNets. In this section, we present another solution to this problem, a “knowledge transfer” framework which is a global, fast and light-weight framework that combines the transfer learning technique with an SVM classifier. For our model structure, we chose the number of layers and neurons in each dense layer. This paper provides an extensive analysis of the performance of the EfficientNet image classifiers with several recent training procedures, in particular one that corrects the discrepancy between train and test images. The base of this model is extremely similar to fastai's TabularModel, minus a few distinctions:. Data augmentation is routinely employed at training time to improve model generalization and reduce overfitting. 24. Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. QuadFPN experiments (efficientdet_q0/q1/q2) and CSPResDeXt + PAN (csp… features, and it can result in overfitting of the training data. In recent years, deep learning has vastly improved the identification and diagnosis of various diseases in plants. For this run I used some improved augmentations, still experimenting so not ready for release, should work well without them but will likely start overfitting a bit sooner and possibly end up a in the .385-.39 range. Issue 82. When we previously demonstrated the idea of fine-tuning in earlier episodes, we used the cat and dog data set. ... epochs: more epochs could achieve better accuracy until it converges but training for too many epochs may lead to overfitting. In this note, we focus on the EfficientNet architecture [tan2019efficientnet] due to its good accuracy/cost trad-off and its popularity. Thanks for your answer! Convolutional neural networks (CNNs) are commonly developed at a fixed resource cost, and then scaled up in order to achieve better accuracy when more resources are made available. VGG-16 VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The EfficientNet is 8.4x ... overfitting and reduces computation cost. 논문 : Self-training with Noisy Student improves ImageNet classification 분류 : classification (Detection) 저자 : Qizhe Xie, Minh-Thang Luong, Eduard Hovy 느낀점 목차 Paper Review Noise 기법 정리 Self-training with Noisy Student 1. The dataset was randomly split into 70% training, 20% validation, and 10% testing. Pick your tracks or playlist from Spotify or iTunes and let our AI DJ help you create a masterpiece. With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. The above Augmentation techniques help in generalizing the model by preventing the overfitting and in turn increases the accuracy of the model. Bạn có thể tìm sổ ghi chép cho bài viết này tại đây. Compared to the widely used ResNet (He et al., 2016), our EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% with similar FLOPS. These models can be used for prediction, feature extraction, and fine-tuning. official. Adversarial examples are commonly viewed as a threat to ConvNets. We tested the network performances on different configurations of input data. Use skimage.transform.resize with anti_aliasing=True. It can also reduce overfitting and improve the generalization of our models because it increases the diversity of our training set. an initially simple convolutional neural network (CNN), the precision andefficiency of a model can usually be further increased step by step byarbitrarily scaling the This result even surpasses the best model in [20] which is trained with 3.5B Instagram images ( 3000X more than ImageNet) and 9.4X more parameters. Phần 4: Giới thiệu chung kết Trong bài viết này, chúng tôi sẽ viết một EfficientNet chung có tính đến các yếu tố quy mô chiều rộng và chiều sâu và chia tỷ lệ EfficientNet-B0 cho phù hợp. The one-stage framework includes SSD, YOLO and CenterNet and EfficientNet series, that are relatively fast but less accurate. Overfitting will manifest itself as a trend of quadratic decrease in the performance gain. (if your gain graph is becoming a horiontal line before iterations end it is a clear signal for overfitting) The result shows that the system can clasiffy the 5 level of Diabetic Retinopathy with accuracy 79.8%. Test and validation datasets inherently also help with this process. However, training EfficientNet on smaller datasets, especially those with lower resolution like CIFAR-100, faces the significant challenge of: overfitting. Efficientnet training Pacemaker for iPhone, iPad and Apple Watch. Key to our method is the usage of a … We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. View Nusrat Jahan Pritha’s profile on LinkedIn, the world’s largest professional community. Finally in 2019 EfficientNet , provided a new scaling up method that uniformly increases the dimensions of depth, width, and resolution, achieving state-of-the-art accuracy on ImageNet . An EfficientNet-L2 pre-trained with weak supervision on 300M unlabeled images and further optimized with FixRes achieves 88.5% top-1 accuracy (top-5: 98.7%), which establishes the new state of the art for ImageNet with a single crop. To import EfficientNet, first you have to decide which depth to go with. TF-EfficientDet-D7 Discover essential techniques that ensure your classification models perform well in production, starting with the best way to train-test split datasets and create validation subsets to avoid overfitting.. Additionally, we reduce the number of stacked Operation-wise Attention (OwA) layers from 10 to 4, in order to avoid overfitting during training. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. Afterwards, a certain number of layers were unfrozen so that the network could be fine-tuned. It means that our model is overfitting. ... How to Prevent Overfitting in Machine Learning Models. Pick your tracks or playlist from Spotify or iTunes and let our AI DJ help you create a masterpiece. Data augmentation is routinely employed at training time to improve model generalization and reduce overfitting. For each variant of the architecture (b0 to b7) the training and validation loss decrease up until +/- 100 epochs. Other training parameters : Data augmentation : (package used : albumentations) ShiftScaleRotate + RandomCrop + MultiplicativeNoise + HorizontalFlip; Architecture & encoder : DeepLabV3+ & efficientnet-b3 (package used : segmentation_models_pytorch) DeepLabV3+ outperformed Unet. Also, in our adaptation, each operation-wise attention layer consists of one operation layer, in contrast with the original implementation that stacks 4 consecutive operation layers. ️These CNNs not only provide better accuracy but also improve the efficiency of the models by reducing the number of parameters as compared to the other state-of-the-art models. It achieves 77.3% accuracy on ImageNet with only 5.3M parameters and 0.39B FLOPS. The accuracy of automatic classification of benthic images depends on many factors including imaging conditions (e.g, resolution, lighting, turbidity, motion blur, camera quality), the particular taxa of interest, and number of training annotations. Finally, in the case of ResNet, the accuracy is 33.45% in epoch 1, but the accuracy is 59.82% in the last epoch 50. See the complete profile on LinkedIn and discover Nusrat’s connections and jobs at similar companies. EfficientNet could serve as a new foundation for future computer vision tasks. Then VGG Net and EfficientNet showed an accuracy of around 30% in all epochs, and it is judged that Vanishing gradient occurred. To this end, the authors use Neural Architecture Search to build an efficient network architecture, EfficientNet-B0. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Afterwards, a certain number of layers were unfrozen so that the network could be fine-tuned. Dropout also helps reduce overfitting, by preventing a layer from seeing twice the exact same pattern, thus acting in a way analoguous to data augmentation (you could say that both dropout and data augmentation tend to disrupt random correlations occuring in your data). Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. EfficientNet-B0 model achieved 99.95% accuracy on our dataset at the 8 th epoch. For EfficientNet, its main building block is mobile inverted bottleneck MBConv, which was first introduced in MobileNetV2. The EfficientNet is a model created for image recognition, and your task demands the correct localization of 7 characters in one image, recognition of each one of them, and also demand to keep the order of the characters. They are stored at ~/.keras/models/. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. There is no general rule on how much to remove or how big your network should be. Usually, when a model performs great on training data but poorly on validation data, we call this condition overfitting. To build our model, we used EfficientNet-B0, EfficientNet-B4, and EfficientNet-B7. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images (~3000X more than ImageNet) and ~9.4X more parameters AutoAugment is a paper published … But, if your network is overfitting, try making it smaller. dropout_rate: The rate for dropout, avoid overfitting. EfficientNet-B0 is the baseline network developed by AutoML MNAS, while Efficient-B1 to B7 are obtained by scaling up the baseline network. Adversarial examples are commonly viewed as a threat to ConvNets. Small datasets lead to a discriminator overfitting to the training samples. I was trying to train the EfficientNet model with the official implementation in tf 2.3 and the model was greatly overfitting. In particular, our EfficientNet-B7 achieves 84.4% top1 / 97.1% top-5 accuracy with 66M parameters and 37B FLOPS, being more accurate but 8.4x smaller than the previous best GPipe (Huang et al., 2018). Let’s try to improve our previous model by: lowering the learning rate from 0.001 to 0.0001; adding high-level (256 filters) convolution layer; adding fully connected layer; And check the performance after 20 epochs. Key to our method is the … GrowNet. Introduction. Overfitting on a single batch is a simple and effective way to spot problems early, since they may come from unexpected places not necessarily reflected in stack traces or loss anomalies. Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. Tan, Mingxing and Le, Quoc in cs.LG, cs.CV and stat.ML, latest revision 11/23/2019 1905.11946v3: Abstract – Full Paper (pdf) The simplest way to avoid over-fitting is to make sure that the number of independent parameters in your fit is much smaller than the number of data … To avoid overfitting, I limited the training to 20 epochs. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A dropout layer randomly drops some of the connections between layers. you must resize the input img to dimensions [1,224,224,3]. Install EfficientNet #pip command install EfficientNet model by using!pip install efficientnet Imported libraries and modules #Imported libraries and modules import efficientnet.keras as efn from sklearn.metrics import classification_report,accuracy_score,f1_score,confusion_matrix import numpy as np from keras.preprocessing.image import load_img, img_to_array import matplotlib.pyplot … The EfficientNet-B4 architecture is a relatively newly developed architecture and, to the authors’ knowledge, has had very little research done in evaluating its performance in this task, including any comparative evaluations on it against other transfer learning architectures. Big speed gains for CPU bound training. To control overfitting, we adjusted the weight for L2 regularization (which penalizes larger weights and biases) and the dropout rate (which controls the probability of keeping the output of each node in a hidden layer). Find Waldo With YOLOv2. The EfficientNet has B0 to B7 model. It brings me great pleasure as I begin writing about EfficientNetsfor two reasons: 1. reduce the size of the model: the number of learnable parameters in the model can extract manipulation features, their performance is unsatisfactory due to extremely high model overfitting. Dear friends, Engineers need strong technical skills to be successful. The mCE was improved from the baseline 75.55% to 48.89% (43.27% with ResNet-152, 59.4% for EfficientNet B7), while the throughput only decreased from 536 to 312 images/sec (143 with ResNet-152, just 16 for EfficientNet B7). You can find the full codebase on Google Colab with this link: https://colab.research.google.com/drive/1jSpUEJIAz2N6A0rY_rGQYtLXNgSUP8sK Loading pictures Pictures AlexNet, proposed by Alex Krizhevsky, uses ReLu(Rectified Linear Unit) for the non-linear part, instead of a Tanh or Sigmoid function which was the earlier standard for traditional neural networks.ReLu is given by f(x) = max(0,x) The advantage of the ReLu over sigmoid is that it trains much faster than the latter because the derivative of sigmoid becomes very small in the saturating region … In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. 2: Adding Dropout Layers. In training, the learning rate is initiated at 1e-3 and decreased 10 times after 5 non-improved epochs. VGG-16 VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. Computer-aided diagnosis systems have been designed and implemented to combat these issues. This might make it sounds easy to simply train EfficientNet on any: dataset wanted from scratch. Nusrat’s education is listed on their profile. Typically this is a number between 0.01 and 0.0001. A ResNet-RS model with 1.8x more FLOPs than EfficientNet-B6 is 2.7x faster on a TPUv3. Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. Meanwhile, LWTNet uses the residual connection, pointwise convolution, and depthwise separable convolution [15] in … Early stopping with a patience of 20 epochs, and a checkpoint callback monitoring minimum validation loss were the additional techniques implemented to avoid overfitting. We used B0 to B4 noisy-student pre-trained model weight and not included top, used the average pooling. To see how we can prevent overfitting, we first need to create a base model to compare the improved models to. The base model is a simple keras model with two hidden layers with 128 and 64 neurons. You can check it out here: With this model we c an achieve a training accuracy of over 97%, but a validation accuracy of only about 60%. Here you can observe overfitting in the validation losses (objectness in particular) across the 4 models. , 2018) that dynamically synthesizes training data and GPipe (Huang et al. Epoch 10, batch size 16, adam optimizer [9] 0.0001, L2 regular-izer [10] 0.0001, dropout [11] size depends on EfficientNet model and use masked loss function. Add weights for alternate FPN layouts. tl;dr: GrowNet applies gradient boosting to shallow neural networks. EfficientNet is a much lighter and more accurate network than the existing network proposed in May 2019. The resulting network, called FixEfficientNet, significantly outperforms the initial architecture with the same number of parameters. Weights are downloaded automatically when instantiating a model. To solve this problem, we usually try to get new data, and if new data isn’t available, data augmentation comes to the rescue. March 10, 2021. EfficientDet is the object detection version of EfficientNet, building on the success EfficientNet has seen in image classification tasks. expensive and prone to overfitting on smaller datasets. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a 0.945 score on the task. Deepfake detection is a more constrained problem than general object detection, but these types of fine-grained visual classification seem to provide an edge when figuring out exactly which parts of a face to drop. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. The default value works just fine, but you may want to tweak this if you notice overfitting. It has been rising in … Realtime augmentation is applied to prevent overfitting. The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. It helped to reduce the chances of overtraining or overfitting the model by halting the training epochs as soon as the validation accuracy stops improving. scale model depth if overfitting is possible, scale model width otherwise; increase image resolution slower than recommended in previous papers; Based on these ideas, the new architecture ResNet-RS was developed. Dropout Layers can be an easy and effective way to prevent overfitting in your models. When training for 350 epochs on ImageNet, use depth scaling, whereas scaling the width is preferable when training for few epochs (e.g. For EfficientNet, its main building block is mobile , ... Keras’s ImageDataGenerator, which adds various data augmentation options during the training to reduce the chance of overfitting. EfficientNet Performance Results on ImageNet (Russakovsky et al., 2015). However, when applied to GAN training, standard dataset augmentations tend to ‘leak’ into generated images (e.g., noisy augmentation leads to … Hybrid-TSR is a novel hybrid model designed to accurately perform the task of traffic sign recognition. 79 with model m3. Discover essential techniques that ensure your classification models perform well in production, starting with the best way to train-test split datasets and create validation subsets to avoid overfitting.. EfficientNets come from a family of models that achieve a high performance on benchmark tasks while controlling for a number of efficiency parameters, such as model size and FLOPS. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions … When trying to reduce overfitting it's not clear to me when I should prefer early stopping or when I should prefer weight decay. EfficientNet uses a compound coefficient ϕ to uniformly scales network width, depth, and resolution in a principled way. The integration of the two models indirectly achieves great results. However, training EfficientNet on smaller datasets, especially those with lower resolution like CIFAR-100, faces the significant challenge of overfitting. the model returns logits which must be transformed into probabilities with a softmax function: p i = e L i ∑ j e L j, where p i is the probability of class i and L i is the logit assigned to that class. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. available on Colab). The progression of greater overfitting for larger models is observed, as expected. NOTE: I've only tried submitting D7 to dev server for sanity check so far. For example here is YOLOv5l v2.0 vs v3.0. The pre-trained weight gets untouched; they are frozen. With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. These techniques can be applicable only for the Computer Vision problems with image datasets. My final model was a combination of Fast Resnest embedded with visual attention combined with Efficientnet-B4. Specifically, EfficientNet-B0’s pre-trained parameters will be migrated and fine-tuned using the GTSRB dataset. You might want to bookmark it. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. An EfficientNet-L2 pre-trained with weak supervision on 300M unlabeled images and further optimized with FixRes achieves 88.5% top-1 accuracy (top-5: 98.7%), which establishes the new state of the art for ImageNet with a single crop. So let’s cut to the chase: How can we perform Data Augmentation? How to improve the performance of your tf.keras model by reducing overfitting and accelerating training. 1. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. EfficientNet model is used to trained the dataset which have been pre-prossed. If there is a large discrepancy between training and validation accuracy (called overfitting), try to introduce dropout and/or batch normalization blocks to improve generalization. To reduce the time needed for training, the proposed model architecture is initialized using the transfer learning technique. We use the images cut in endoscopic videos before the release frames for an augmentation strategy in our method. EfficientNet model achieved state-of-the-art performance on ImageNet challenge and also transferred well to several datasets, including those involved in medical tasks, such as for COVID-19 diagnosis based on X-ray images 23 and CT scans. Data augmentation achieves slightly better time series classification rates but still the CNN is prone to overfitting. In this article, we will write a generic EfficientNet that takes in I've been running the EfficientNet code from Google on my own image datasets and have run into the following problem. To reduce the chance of overfitting, we have used the Keras’s ImageDataGenerator method for … We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. None by default. EfficientNet with Arcface; Freeze-Unfreeze Technique; Cutmix and Mixup (Does not improve score but helps prevent overfitting) My Final Model. EfficientNet-B4 and the high quality of recall is taken from the U-Net based on ResNet-34. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. At the time of writing, Efficientnet training Pacemaker for iPhone, iPad and Apple Watch. While coins themselves are relatively simple objects, many coins look very similar and it is surprisingly challenging to build a system that can reliably identify a particular coin. Andrej Karpathy gives this and other advice in his recipe on training neural networks. Hence training from scratch requires very careful choice of hyperparameters and is Applications. This aids in overfitting [20]. This repository contains Keras reimplementation of EfficientNet, the new convolutional neural network architecture from EfficientNet (TensorFlow implementation). Formally, an EfficientNet is defined by three dimensions: (i) depth; (ii) width; and (iii) resolutions as illustrated in Figure 4. EfficientNet has 8 depth levels, starting from B0 (the baseline), and finishing up with the deepest B7. 6x fewer parameters However, training EfficientNet on smaller datasets, especially those with lower resolution like CIFAR-100, faces the significant challenge of overfitting. Fine-tuning MobileNet on a custom data set with TensorFlow's Keras API In this episode, we'll be building on what we've learned about MobileNet combined with the techniques we've used for fine-tuning to fine-tune MobileNet for a custom image data set.. https://stackoverflow.com/questions/62930951/overfitting-huge-difference-between-training-and-validation-accuracy/ EfficientNet includes models from B0 to B7, and each one has different parameters from 5.3M to 66M. [IPP](images/logo_ipp.jpeg) ! This is a very easy test and the model is expected to perform perfectly. In particular, our EfficientNet-B7 surpasses the best existing GPipe accuracy (Huang et al., 2018), but using 8.4x fewer parameters and running 6.1x faster on inference. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. In this note, we focus on the EfficientNet architecture [tan2019efficientnet] due to its good accuracy/cost trad-off and its popularity. However, deeper networks are more prone to overfitting and harder to regularise during training, in addition to requiring a ... (SE) blocks, that adaptively recalibrate channel-wise feature responses. The rationale behind the EfficientNet family is to to start from high quality yet compact baseline model and uniformly scale each of its dimensions systematically with a fixed set of scaling coefficients. (Resnet-50 provides 76% accuracy with 26M parameters and 4.1B FLOPS). EfficientNet is a family of CNN's built by Google. The code snippet below is our first model, a simple stack of 3 convolution layers with a ReLU activation and followed by … This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 … Besides ImageNet, EfficientNets also transfer well and achieve state-of-the-art … I think the image below says it all. A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. This encoder–decoder architecture is built on the top of the baseline EfficientNet model for encoder and U-Net for decoder. tl;dr. For the release of CoralNet 1.0, we have created a new deep learning engine that is more accurate and faster. NOTE: I've only tried submitting D7 to dev server for sanity check so far. Efficientnet B1* Yes. tl;dr: GrowNet applies gradient boosting to shallow neural networks. The contradictory trend is because the basic EfficientNet overfits the training set, while the boosted-EfficientNet-B3 mitigates overfitting problems since RCC enables the algorithm to crop images randomly, thus improving the diversity of training images. model_5. Augmentations help to fight overfitting and improve the performance of deep neural networks for computer vision tasks such as classification, segmentation, and object detection. The behavior is tested on both GPU and TPU. Overfitting is the problem when a model is trained and it works so well on training data that it negatively impacts the performance of the model on new data. The coin recognition system is a great showcase of the power of modern deep learning image processing models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan & Le, 2019). In this article, we will implement the main layers used in EfficientNet and This model takes an image and then either emphasizes or drops discriminative face parts (eyes, mouth, forehead, etc.) 48.22%. EfficientNet models use many orders of magnitude lesser parameters and FLOPS than other ConvNets with similar accuracy.
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