# EfficientNet-PyTorch-3D

**Repository Path**: khanx/EfficientNet-PyTorch-3D

## Basic Information

- **Project Name**: EfficientNet-PyTorch-3D
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-21
- **Last Updated**: 2025-08-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# EfficientNet PyTorch

3D Version is based on top of [EfficientNet-Pytorch](https://github.com/lukemelas/EfficientNet-PyTorch).

For similar usage, you may install like:
```bash
pip install git+https://github.com/shijianjian/EfficientNet-PyTorch-3D
```

```python
from efficientnet_pytorch_3d import EfficientNet3D
import torch

model = EfficientNet3D.from_name("efficientnet-b0", override_params={'num_classes': 2}, in_channels=1)

from torchsummary import summary
summary(model, input_size=(1, 200, 200, 200))

model = model.cuda()
inputs = torch.randn((1, 1, 200, 200, 200)).cuda()
labels = torch.tensor([0]).cuda()
# test forward
num_classes = 2

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

model.train()
for epoch in range(2):
    # zero the parameter gradients
    optimizer.zero_grad()

    # forward + backward + optimize
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    # print statistics
    print('[%d] loss: %.3f' % (epoch + 1, loss.item()))

print('Finished Training')
```

3D EfficientNet has a high GPU cost. Here, the ```block_args``` for the first block is altered from ```'r1_k3_s111_e1_i32_o16_se0.25'``` to ```'r1_k3_s222_e1_i32_o16_se0.25'``` to save GPU memories.

Take an example from EfficientNet-b0 with an input size of (1, 200, 200, 200):
- Stide 1 for the first block will cost 8703.64 MB GPU Memories.
- Strde 2 for the first block will cost 2023.29 MB GPU Memories.

Take an example from EfficientNet-b0 with an input size of (1, 200, 1024, 200):
- Stide 1 for the first block will cost 44387.63 MB GPU Memories, which will not be loaded into any single GPUs currently.
- Strde 2 for the first block will cost 10283.67 MB GPU Memories, which is relatively acceptable.

Mostly, we can save 4 times GPU memories by reducing the stride from the first block from 1 to 2.

Below is the README from the original repo:

_IMPORTANT NOTE_: In the latest update, I switched hosting providers for the pretrained models, as the previous models were becoming extremely expensive to host. This _will_ break old versions of the library. I apologize, but I cannot afford to keep serving the models on the old provider. Everything should work properly if you update the library: 
```
pip install --upgrade efficientnet-pytorch
```

### Update (January 23, 2020)

This update adds a new category of pre-trained model based on adversarial training, called _advprop_. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. As a result, by default, advprop models are not used. To load a model with advprop, use:
```
model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True)
```
There is also a new, large `efficientnet-b8` pretrained model that is only available in advprop form. When using these models, replace ImageNet preprocessing code as follows:
```
if advprop:  # for models using advprop pretrained weights
    normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
else:
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                     std=[0.229, 0.224, 0.225])

```
This update also addresses multiple other issues ([#115](https://github.com/lukemelas/EfficientNet-PyTorch/issues/115), [#128](https://github.com/lukemelas/EfficientNet-PyTorch/issues/128)). 

### Update (October 15, 2019)

This update allows you to choose whether to use a memory-efficient Swish activation. The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. For this purpose, we have also included a standard (export-friendly) swish activation function. To switch to the export-friendly version, simply call `model.set_swish(memory_efficient=False)` after loading your desired model. This update addresses issues [#88](https://github.com/lukemelas/EfficientNet-PyTorch/pull/88) and [#89](https://github.com/lukemelas/EfficientNet-PyTorch/pull/89).

### Update (October 12, 2019)

This update makes the Swish activation function more memory-efficient. It also addresses pull requests [#72](https://github.com/lukemelas/EfficientNet-PyTorch/pull/72), [#73](https://github.com/lukemelas/EfficientNet-PyTorch/pull/73), [#85](https://github.com/lukemelas/EfficientNet-PyTorch/pull/85), and [#86](https://github.com/lukemelas/EfficientNet-PyTorch/pull/86). Thanks to the authors of all the pull requests! 

### Update (July 31, 2019)

_Upgrade the pip package with_ `pip install --upgrade efficientnet-pytorch`

The B6 and B7 models are now available. Additionally, _all_ pretrained models have been updated to use AutoAugment preprocessing, which translates to better performance across the board. Usage is the same as before: 
```python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b7') 
```

### Update (June 29, 2019)

This update adds easy model exporting ([#20](https://github.com/lukemelas/EfficientNet-PyTorch/issues/20)) and feature extraction ([#38](https://github.com/lukemelas/EfficientNet-PyTorch/issues/38)). 

 * [Example: Export to ONNX](#example-export)
 * [Example: Extract features](#example-feature-extraction)
 * Also: fixed a CUDA/CPU bug ([#32](https://github.com/lukemelas/EfficientNet-PyTorch/issues/32))

It is also now incredibly simple to load a pretrained model with a new number of classes for transfer learning:
```python
model = EfficientNet.from_pretrained('efficientnet-b1', num_classes=23)
``` 


### Update (June 23, 2019)

The B4 and B5 models are now available. Their usage is identical to the other models: 
```python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b4') 
```

### Overview
This repository contains an op-for-op PyTorch reimplementation of [EfficientNet](https://arxiv.org/abs/1905.11946), along with pre-trained models and examples. 

The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented. 

At the moment, you can easily:  
 * Load pretrained EfficientNet models 
 * Use EfficientNet models for classification or feature extraction 
 * Evaluate EfficientNet models on ImageNet or your own images

_Upcoming features_: In the next few days, you will be able to:
 * Train new models from scratch on ImageNet with a simple command 
 * Quickly finetune an EfficientNet on your own dataset
 * Export EfficientNet models for production

### Table of contents
1. [About EfficientNet](#about-efficientnet)
2. [About EfficientNet-PyTorch](#about-efficientnet-pytorch)
3. [Installation](#installation)
4. [Usage](#usage)
    * [Load pretrained models](#loading-pretrained-models)
    * [Example: Classify](#example-classification)
    * [Example: Extract features](#example-feature-extraction)
    * [Example: Export to ONNX](#example-export)
6. [Contributing](#contributing) 

### About EfficientNet

If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation: 

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 Mobile framework](https://ai.googleblog.com/2018/08/mnasnet-towards-automating-design-of.html) 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 B7.

<table border="0">
<tr>
    <td>
    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" width="100%" />
    </td>
    <td>
    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png", width="90%" />
    </td>
</tr>
</table>

EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency:


* In high-accuracy regime, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS, being 8.4x smaller and 6.1x faster on CPU inference than previous best [Gpipe](https://arxiv.org/abs/1811.06965).

* In middle-accuracy regime, our EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than [ResNet-152](https://arxiv.org/abs/1512.03385), with similar ImageNet accuracy.

* Compared with the widely used [ResNet-50](https://arxiv.org/abs/1512.03385), our EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraint.

### About EfficientNet PyTorch

EfficientNet PyTorch is a PyTorch re-implementation of EfficientNet. It is consistent with the [original TensorFlow implementation](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet), such that it is easy to load weights from a TensorFlow checkpoint. At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.

If you have any feature requests or questions, feel free to leave them as GitHub issues!

### Installation

Install via pip:
```bash
pip install efficientnet_pytorch
```

Or install from source:
```bash
git clone https://github.com/lukemelas/EfficientNet-PyTorch
cd EfficientNet-Pytorch
pip install -e .
``` 

### Usage

#### Loading pretrained models

Load an EfficientNet:  
```python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_name('efficientnet-b0')
```

Load a pretrained EfficientNet: 
```python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
```

Note that pretrained models have only been released for `N=0,1,2,3,4,5` at the current time, so `.from_pretrained` only supports `'efficientnet-b{N}'` for `N=0,1,2,3,4,5`. 

Details about the models are below: 

|    *Name*         |*# Params*|*Top-1 Acc.*|*Pretrained?*|
|:-----------------:|:--------:|:----------:|:-----------:|
| `efficientnet-b0` |   5.3M   |    76.3    |      ✓      |
| `efficientnet-b1` |   7.8M   |    78.8    |      ✓      |
| `efficientnet-b2` |   9.2M   |    79.8    |      ✓      |
| `efficientnet-b3` |    12M   |    81.1    |      ✓      |
| `efficientnet-b4` |    19M   |    82.6    |      ✓      |
| `efficientnet-b5` |    30M   |    83.3    |      ✓      |
| `efficientnet-b6` |    43M   |    84.0    |      ✓      |
| `efficientnet-b7` |    66M   |    84.4    |      ✓      |


#### Example: Classification

Below is a simple, complete example. It may also be found as a jupyter notebook in `examples/simple` or as a [Colab Notebook](https://colab.research.google.com/drive/1Jw28xZ1NJq4Cja4jLe6tJ6_F5lCzElb4).

We assume that in your current directory, there is a `img.jpg` file and a `labels_map.txt` file (ImageNet class names). These are both included in `examples/simple`. 

```python
import json
from PIL import Image
import torch
from torchvision import transforms

from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')

# Preprocess image
tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])
img = tfms(Image.open('img.jpg')).unsqueeze(0)
print(img.shape) # torch.Size([1, 3, 224, 224])

# Load ImageNet class names
labels_map = json.load(open('labels_map.txt'))
labels_map = [labels_map[str(i)] for i in range(1000)]

# Classify
model.eval()
with torch.no_grad():
    outputs = model(img)

# Print predictions
print('-----')
for idx in torch.topk(outputs, k=5).indices.squeeze(0).tolist():
    prob = torch.softmax(outputs, dim=1)[0, idx].item()
    print('{label:<75} ({p:.2f}%)'.format(label=labels_map[idx], p=prob*100))
```

#### Example: Feature Extraction 

You can easily extract features with `model.extract_features`:
```python
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')

# ... image preprocessing as in the classification example ...
print(img.shape) # torch.Size([1, 3, 224, 224])

features = model.extract_features(img)
print(features.shape) # torch.Size([1, 1280, 7, 7])
```

#### Example: Export to ONNX  

Exporting to ONNX for deploying to production is now simple: 
```python
import torch 
from efficientnet_pytorch import EfficientNet

model = EfficientNet.from_pretrained('efficientnet-b1')
dummy_input = torch.randn(10, 3, 240, 240)

torch.onnx.export(model, dummy_input, "test-b1.onnx", verbose=True)
``` 

[Here](https://colab.research.google.com/drive/1rOAEXeXHaA8uo3aG2YcFDHItlRJMV0VP) is a Colab example. 


#### ImageNet

See `examples/imagenet` for details about evaluating on ImageNet.

### Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.   

I look forward to seeing what the community does with these models!