https://github.com/murufeng/epsanet
https://github.com/murufeng/epsanet
attention-mechanism convolutional-neural-network multi-scale-feature-learning
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/murufeng/epsanet
- Owner: murufeng
- License: mit
- Created: 2021-04-29T13:49:06.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-11-15T02:30:58.000Z (almost 4 years ago)
- Last Synced: 2025-05-20T12:07:23.979Z (5 months ago)
- Topics: attention-mechanism, convolutional-neural-network, multi-scale-feature-learning
- Language: Python
- Homepage:
- Size: 52.7 KB
- Stars: 302
- Watchers: 4
- Forks: 47
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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[](https://github.com/murufeng/EPSANet)
[](https://github.com/murufeng/EPSANet)
This repo contains the official Pytorch implementaion code.
## Installation
### Requirements
- Python 3.6+
- PyTorch 1.0+
### Our environments
- OS: Ubuntu 18.04
- CUDA: 10.0
- Toolkit: PyTorch 1.0
- GPU: Titan RTX
## Data preparation
Download and extract ImageNet train and val images from [http://image-net.org/](http://image-net.org/).
The directory structure is the standard layout for the torchvision [`datasets.ImageFolder`](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder), and the training and validation data is expected to be in the `train/` folder and `val` folder respectively:
```
/path/to/imagenet/
train/
class1/
img1.jpeg
class2/
img2.jpeg
val/
class1/
img3.jpeg
class/2
img4.jpeg
```
# Usage
First, clone the repository locally:
```
git clone https://github.com/murufeng/EPSANet.git
cd EPSANet
```
- Create a conda virtual environment and activate it:
```bash
conda create -n epsanet python=3.6
conda activate epsanet
```
- Install `CUDA==10.0` with `cudnn7` following
the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- Install `PyTorch==1.0.1` and `torchvision==0.2.0` with `CUDA==10.0`:
```
conda install -c pytorch pytorch torchvision
```
## Training
To train models on ImageNet with 8 gpus run:
```
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py -a epsanet50 --data /path/to/imagenet
```
## Model Zoo
Models are trained with 8 GPUs on both ImageNet and MS-COCO 2017 dataset.
### Image Classification on ImageNet
| Model | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
|:---------------------:|:---------:|:--------:|:---------:|:---------:|
| EPSANet-50(Small) | 22.56 | 3.62 | 77.49 | 93.54 |
| EPSANet-50(Large) | 27.90 | 4.72 | 78.64 | 94.18 |
| EPSANet-101(Small) | 38.90 | 6.82 | 78.43 | 94.11 |
| EPSANet-101(Large) | 49.59 | 8.97 | 79.38 | 94.58 |
### Object Detection on MS-COCO 2017
#### Faster R-CNN
| model | Style | Lr schd | Params(M) | FLOPs(G) | box AP | AP_50 | AP_75|
| :-------------:| :-----: | :-----: |:---------:|:--------:| :----: | :--------: | :----: |
| EPSANet-50(small) | pytorch | 1x | 38.56 | 197.07 | 39.2 | 60.3 | 42.3 |
| EPSANet-50(large) | pytorch | 1x | 43.85 | 219.64 | 40.9 | 62.1 | 44.6 |
#### Mask R-CNN
| model | Style | Lr schd | Params(M) | FLOPs(G) | box AP | AP_50 | AP_75 |
| :-------------:| :-----: | :-----: |:---------:|:--------:| :----: | :------: | :----: |
| EPSANet-50(small) | pytorch | 1x | 41.20 | 248.53 | 40.0 | 60.9 | 43.3 |
| EPSANet-50(large) | pytorch | 1x | 46.50 | 271.10 | 41.4 | 62.3 | 45.3 |
#### RetinaNet
| model | Style | Lr schd | Params(M) | FLOPs(G) | box AP | AP_50 | AP_75 |
| :-------------:| :-----: | :-----: |:---------:|:--------:| :----: | :------: | :----: |
| EPSANet-50(small) | pytorch | 1x | 34.78 | 229.32 | 38.2 | 58.1 | 40.6 |
| EPSANet-50(large) | pytorch | 1x | 40.07 | 251.89 | 39.6 | 59.4 | 42.3 |
### Instance segmentation with Mask R-CNN on MS-COCO 2017
|model |Params(M) | FLOPs(G) | AP | AP_50 | AP_75 |
| :----:| :-----: | :-----: |:---------:|:---------:|:---------:|
|EPSANet-50(small) | 41.20 | 248.53 | 35.9 | 57.7 | 38.1 |
|EPSANet-50(Large) | 46.50 | 271.10 | 37.1 | 59.0 | 39.5 |