https://github.com/ibebbs/u2net-cpu-http
https://github.com/ibebbs/u2net-cpu-http
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/ibebbs/u2net-cpu-http
- Owner: ibebbs
- License: mit
- Created: 2020-08-17T13:49:43.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-21T16:16:53.000Z (almost 6 years ago)
- Last Synced: 2025-05-25T08:41:38.355Z (about 1 year ago)
- Language: Python
- Size: 5.86 KB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**Forked from [ideo](https://github.com/ideo/bg-removal-with-u2net) and changed to perform inference on CPU only**
# U^2-Net HTTP
This is an HTTP service wrapper of the model: [U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://github.com/NathanUA/U-2-Net) (Qin et al, Pattern Recognition 2020)
The deploy folder contains configuration files for deployment as serverless container with Knative.
# Usage:
```bash
docker run --rm -p 8080:80 ibebbs/u2net-http
```
# Test:
```bash
curl -F "data=@test.jpg" http://localhost:8080 -o result.png
```
# Development
- Clone this repository: `git clone https://github.com/cyrildiagne/u2net-http.git`
- Go into the cloned directory: `cd u2net-http`
- Clone the official [U^2-Net repository](https://github.com/NathanUA/U-2-Net)
- Download the pretrained model [u2net.pth](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view)
- Put the file inside the `U-2-Net/saved_models/u2net/` folder.
# Build from source:
### Option 1 - Locally with virtualenv
```bash
virtualenv venv
venv/bin/activate
```
```bash
pip install torch==0.4.1
pip install -r requirements.txt
```
```
python main.py
```
### Option 2 - Using Docker
After you've retrieved the U^2-Net model.
Download Resnet checkpoint
```
curl https://download.pytorch.org/models/resnet34-333f7ec4.pth -o resnet34-333f7ec4.pth
```
```
docker build -t u2net .
docker run --rm -p 8080:8080 u2net
```