https://github.com/wrannaman/tensorflow-age-gender
Age and Gender in tensorflow on a cpu or gpu!
https://github.com/wrannaman/tensorflow-age-gender
age ai back-end computer-vision cpu machine-learning tensorflow
Last synced: 7 months ago
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Age and Gender in tensorflow on a cpu or gpu!
- Host: GitHub
- URL: https://github.com/wrannaman/tensorflow-age-gender
- Owner: wrannaman
- Created: 2019-08-06T15:48:05.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-02T05:17:22.000Z (over 2 years ago)
- Last Synced: 2025-02-11T17:11:43.593Z (8 months ago)
- Topics: age, ai, back-end, computer-vision, cpu, machine-learning, tensorflow
- Language: Jupyter Notebook
- Size: 434 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
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README
# Age Gender Estimation (Tensorflow)
*Tensorflow based Age Gender Model (CPU + GPU)*
## Speed
| Hardware | Inference Time (Milliseconds)
|---------- |-------------------------------
| CPU | 750 (intel i-7)
| GPU | 430 (NVIDIA 2080)
This model's inference time increases sublinearly with the number of people.
## Accuracy
| Detection | Accuracy
|--------------- |-------------------------------
| Face - Accurate | ~95% (frontal faces) (accurate model)
| Face - Fast | ~90% (frontal faces) (accurate model)
| Age | +- 5 years
There are two detectors built into this container. You can toggle between them in the post parameters
## Face Detector
| Model | Description
|---------- |-------------------------------
| Fast | dlib
| Accurate | tensorflow based cnn face detector
- The size of the face is 64x64
- For the GPU version, this model needs a minimum of 200MB.
## Run
```sh
#cpu
docker run -ti \\
-p 9090:9090 \\
sugarkubes/tensorflow-age-gender:cpu
#gpu
nvidia-docker run -ti \\
-p 9090:9090 \\
sugarkubes/tensorflow-age-gender:gpu
```
## Routes
`GET /`
`GET /health`
`GET /healthz`
- Responds with a 200 for healthcheck
`POST /predict`
- Example:
```sh
curl -X POST \\
http://0.0.0.0:9090/predict \\
-H 'Content-Type: application/json' \\
-d '{ "url": "https://s3.us-west-1.wasabisys.com/public.sugarkubes/repos/sugar-cv/object-detection/friends.jpg" }'
```
- Post parameters
```json
{
"face_detector": "fast", # One of ["accurate", "fast"]
"return_image": true, # use false for production/faster results
"url": 'https://your-image.jpg', # use url or b64 image
"b64": "", # base 64 encoded image
}
```
## ENV Variables
| Variable | Default
|------------ |-------------------------------
| PORT | 8080
| HOST | 0.0.0.0
| GPU | "" (true for GPU version)
| GPU_FRACTION | 0.25 (25% of the gpu will be allocated to this model)
| BASIC_AUTH_USERNAME | ""
| BASIC_AUTH_PASSWORD | ""
## Google Cloud Run Enabled
- This container can immediately be deployed using Google's new Cloud Run serverless service (cpu inference only)
- Its a cheap and quick way to get the model online
## Response
```json
{
// x1 y1 x2 y2 w h conf age gender
"faces": [[451, 0, 914, 452, 463, 514, -1, 37, "M"]],
"image_size": [1920, 1080],
"inference_time": 431.462,
}
```
## Authentication
- The container comes with the ability to support basic auth.
- basic auth is disabled by default.
- to turn it off, set `BASIC_AUTH_USERNAME=""` and `BASIC_AUTH_PASSWORD=""`
- to turn it on, set `BASIC_AUTH_USERNAME="root"` and `BASIC_AUTH_PASSWORD="your password"`
## About the container
- python 3.5.2 (cpu)
- python 3.5.1 (gpu)
- tensorflow 1.13.1
## Validated Hosts
- intel x86 machines running ubuntu server 18.04 19.04 20.04
- cuda 10.1
- cuda 10.2
- mac