|
|
--- |
|
|
library_name: keras-hub |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- image-classification |
|
|
pipeline_tag: image-classification |
|
|
--- |
|
|
### Model Overview |
|
|
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 MNAS 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 EfficientNet-B7. |
|
|
|
|
|
This class encapsulates the architectures for both EfficientNetV1 and EfficientNetV2. EfficientNetV2 uses Fused-MBConv Blocks and Neural Architecture Search (NAS) to make models sizes much smaller while still improving overall model quality. |
|
|
|
|
|
This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub. |
|
|
|
|
|
## Links |
|
|
|
|
|
* [EfficientNet Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/efficientnet-quickstart-kerashub) |
|
|
* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)(ICML 2019) |
|
|
* [Based on the original keras.applications EfficientNet](https://github.com/keras-team/keras/blob/master/keras/applications/efficientnet.py) |
|
|
* [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298) (ICML 2021) |
|
|
* [EfficientNet API Documentation](https://keras.io/keras_hub/api/models/efficientnet/) |
|
|
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/) |
|
|
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/) |
|
|
|
|
|
|
|
|
## Installation |
|
|
|
|
|
Keras and KerasHub can be installed with: |
|
|
|
|
|
``` |
|
|
pip install -U -q keras-hub |
|
|
pip install -U -q keras |
|
|
``` |
|
|
|
|
|
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page. |
|
|
|
|
|
## Presets |
|
|
|
|
|
The following model checkpoints are provided by the Keras team. Full code examples for each are available below. |
|
|
|
|
|
| Preset name | Parameters | Description | |
|
|
|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| efficientnet_b0_ra_imagenet | 5.3M | EfficientNet B0 model pre-trained on the ImageNet 1k dataset with RandAugment recipe. | |
|
|
| efficientnet_b0_ra4_e3600_r224_imagenet | 5.3M | EfficientNet B0 model pre-trained on the ImageNet 1k dataset by Ross Wightman. Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from timm and 'ResNet Strikes Back'. | |
|
|
| efficientnet_b1_ft_imagenet | 7.8M | EfficientNet B1 model fine-tuned on the ImageNet 1k dataset. | |
|
|
| efficientnet_b1_ra4_e3600_r240_imagenet | 7.8M | EfficientNet B1 model pre-trained on the ImageNet 1k dataset by Ross Wightman. Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from timm and 'ResNet Strikes Back'. | |
|
|
| efficientnet_b2_ra_imagenet | 9.1M | EfficientNet B2 model pre-trained on the ImageNet 1k dataset with RandAugment recipe. | |
|
|
| efficientnet_b3_ra2_imagenet | 12.2M | EfficientNet B3 model pre-trained on the ImageNet 1k dataset with RandAugment2 recipe. | |
|
|
| efficientnet_b4_ra2_imagenet | 19.3M | EfficientNet B4 model pre-trained on the ImageNet 1k dataset with RandAugment2 recipe. | |
|
|
| efficientnet_b5_sw_imagenet | 30.4M | EfficientNet B5 model pre-trained on the ImageNet 12k dataset by Ross Wightman. Based on Swin Transformer train / pretrain recipe with modifications (related to both DeiT and ConvNeXt recipes). | |
|
|
| efficientnet_b5_sw_ft_imagenet | 30.4M | EfficientNet B5 model pre-trained on the ImageNet 12k dataset and fine-tuned on ImageNet-1k by Ross Wightman. Based on Swin Transformer train / pretrain recipe with modifications (related to both DeiT and ConvNeXt recipes). | |
|
|
| efficientnet_el_ra_imagenet | 10.6M | EfficientNet-EdgeTPU Large model trained on the ImageNet 1k dataset with RandAugment recipe. | |
|
|
| efficientnet_em_ra2_imagenet | 6.9M | EfficientNet-EdgeTPU Medium model trained on the ImageNet 1k dataset with RandAugment2 recipe. | |
|
|
| efficientnet_es_ra_imagenet | 5.4M | EfficientNet-EdgeTPU Small model trained on the ImageNet 1k dataset with RandAugment recipe. | |
|
|
| efficientnet2_rw_m_agc_imagenet | 53.2M | EfficientNet-v2 Medium model trained on the ImageNet 1k dataset with adaptive gradient clipping. | |
|
|
| efficientnet2_rw_s_ra2_imagenet | 23.9M | EfficientNet-v2 Small model trained on the ImageNet 1k dataset with RandAugment2 recipe. | |
|
|
| efficientnet2_rw_t_ra2_imagenet | 13.6M | EfficientNet-v2 Tiny model trained on the ImageNet 1k dataset with RandAugment2 recipe. | |
|
|
| efficientnet_lite0_ra_imagenet | 4.7M | EfficientNet-Lite model fine-trained on the ImageNet 1k dataset with RandAugment recipe. | |
|
|
|
|
|
## Model card |
|
|
https://arxiv.org/abs/1905.11946 |
|
|
|
|
|
## Example Usage |
|
|
Load |
|
|
```python |
|
|
classifier = keras_hub.models.EfficientNetImageClassifier.from_preset( |
|
|
"efficientnet_b0_ra_imagenet", |
|
|
) |
|
|
``` |
|
|
Predict |
|
|
```python |
|
|
batch_size = 1 |
|
|
images = keras.random.normal(shape=(batch_size, 96, 96, 3)) |
|
|
classifier.predict(images) |
|
|
``` |
|
|
Train, specify `num_classes` to load randomly initialized classifier head. |
|
|
```python |
|
|
num_classes = 2 |
|
|
labels = keras.random.randint(shape=(batch_size,), minval=0, maxval=num_classes) |
|
|
classifier = keras_hub.models.EfficientNetImageClassifier.from_preset( |
|
|
"efficientnet_b0_ra_imagenet", |
|
|
num_classes=num_classes, |
|
|
) |
|
|
classifier.preprocessor.image_size = (96, 96) |
|
|
classifier.fit(images, labels, epochs=3) |
|
|
``` |
|
|
|
|
|
## Example Usage with Hugging Face URI |
|
|
|
|
|
Load |
|
|
```python |
|
|
classifier = keras_hub.models.EfficientNetImageClassifier.from_preset( |
|
|
"efficientnet_b0_ra_imagenet", |
|
|
) |
|
|
``` |
|
|
Predict |
|
|
```python |
|
|
batch_size = 1 |
|
|
images = keras.random.normal(shape=(batch_size, 96, 96, 3)) |
|
|
classifier.predict(images) |
|
|
``` |
|
|
Train, specify `num_classes` to load randomly initialized classifier head. |
|
|
```python |
|
|
num_classes = 2 |
|
|
labels = keras.random.randint(shape=(batch_size,), minval=0, maxval=num_classes) |
|
|
classifier = keras_hub.models.EfficientNetImageClassifier.from_preset( |
|
|
"efficientnet_b0_ra_imagenet", |
|
|
num_classes=num_classes, |
|
|
) |
|
|
classifier.preprocessor.image_size = (96, 96) |
|
|
classifier.fit(images, labels, epochs=3) |
|
|
``` |
|
|
|
