# (Tensorflow) EfficientNet Lite **EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way. 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. The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing [ReLU6](https://paperswithcode.com/method/relu6) activation functions and removing [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation). The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu). ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('tf_efficientnet_lite0', pretrained=True) model.eval() ``` To load and preprocess the image: ```python import urllib from PIL import Image from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform config = resolve_data_config({}, model=model) transform = create_transform(**config) url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") urllib.request.urlretrieve(url, filename) img = Image.open(filename).convert('RGB') tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```python import torch with torch.no_grad(): out = model(tensor) probabilities = torch.nn.functional.softmax(out[0], dim=0) print(probabilities.shape) # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```python # Get imagenet class mappings url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") urllib.request.urlretrieve(url, filename) with open("imagenet_classes.txt", "r") as f: categories = [s.strip() for s in f.readlines()] # Print top categories per image top5_prob, top5_catid = torch.topk(probabilities, 5) for i in range(top5_prob.size(0)): print(categories[top5_catid[i]], top5_prob[i].item()) # prints class names and probabilities like: # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `tf_efficientnet_lite0`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```python model = timm.create_model('tf_efficientnet_lite0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc{tan2020efficientnet, title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, author={Mingxing Tan and Quoc V. Le}, year={2020}, eprint={1905.11946}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- Type: model-index Collections: - Name: TF EfficientNet Lite Paper: Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for Models: - Name: tf_efficientnet_lite0 In Collection: TF EfficientNet Lite Metadata: FLOPs: 488052032 Parameters: 4650000 File Size: 18820223 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - RELU6 Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_lite0 Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1596 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 74.83% Top 5 Accuracy: 92.17% - Name: tf_efficientnet_lite1 In Collection: TF EfficientNet Lite Metadata: FLOPs: 773639520 Parameters: 5420000 File Size: 21939331 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - RELU6 Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_lite1 Crop Pct: '0.882' Image Size: '240' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1607 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.67% Top 5 Accuracy: 93.24% - Name: tf_efficientnet_lite2 In Collection: TF EfficientNet Lite Metadata: FLOPs: 1068494432 Parameters: 6090000 File Size: 24658687 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - RELU6 Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_lite2 Crop Pct: '0.89' Image Size: '260' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1618 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.48% Top 5 Accuracy: 93.75% - Name: tf_efficientnet_lite3 In Collection: TF EfficientNet Lite Metadata: FLOPs: 2011534304 Parameters: 8199999 File Size: 33161413 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - RELU6 Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_lite3 Crop Pct: '0.904' Image Size: '300' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1629 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.83% Top 5 Accuracy: 94.91% - Name: tf_efficientnet_lite4 In Collection: TF EfficientNet Lite Metadata: FLOPs: 5164802912 Parameters: 13010000 File Size: 52558819 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - RELU6 Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_lite4 Crop Pct: '0.92' Image Size: '380' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1640 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 81.54% Top 5 Accuracy: 95.66% -->