The code of InsightFace Python Library is released under the MIT License. There is no limitation for both academic and commercial usage.
The pretrained models we provided with this library are available for non-commercial research purposes only, including both auto-downloading models and manual-downloading models.
For insightface<=0.1.5, we use MXNet as inference backend.
Starting from insightface>=0.2, we use onnxruntime as inference backend.
You have to install onnxruntime-gpu manually to enable GPU inference, or install onnxruntime to use CPU only inference.
- Change model downloading provider to cloudfront.
- Add face swapping model and example.
- Set default ORT provider to CUDA and CPU.
- Add pose estimation in face-analysis app.
- Change model automated downloading url, to ucloud.
import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
img = ins_get_image('t1')
faces = app.get(img)
rimg = app.draw_on(img, faces)
cv2.imwrite("./t1_output.jpg", rimg)
This quick example will detect faces from the t1.jpg image and draw detection results on it.
In the latest version of insightface library, we provide following model packs:
Name in bold is the default model pack. Auto means we can download the model pack through the python library directly.
Once you manually downloaded the zip model pack, unzip it under ~/.insightface/models/ first before you call the program.
| Name | Detection Model | Recognition Model | Alignment | Attributes | Model-Size | Link | Auto |
|---|---|---|---|---|---|---|---|
| antelopev2 | SCRFD-10GF | ResNet100@Glint360K | 2d106 & 3d68 | Gender&Age | 407MB | link | N |
| buffalo_l | SCRFD-10GF | ResNet50@WebFace600K | 2d106 & 3d68 | Gender&Age | 326MB | link | Y |
| buffalo_m | SCRFD-2.5GF | ResNet50@WebFace600K | 2d106 & 3d68 | Gender&Age | 313MB | link | N |
| buffalo_s | SCRFD-500MF | MBF@WebFace600K | 2d106 & 3d68 | Gender&Age | 159MB | link | N |
| buffalo_sc | SCRFD-500MF | MBF@WebFace600K | - | - | 16MB | link | N |
Recognition Accuracy:
| Name | MR-ALL | African | Caucasian | South Asian | East Asian | LFW | CFP-FP | AgeDB-30 | IJB-C(E4) |
|---|---|---|---|---|---|---|---|---|---|
| buffalo_l | 91.25 | 90.29 | 94.70 | 93.16 | 74.96 | 99.83 | 99.33 | 98.23 | 97.25 |
| buffalo_s | 71.87 | 69.45 | 80.45 | 73.39 | 51.03 | 99.70 | 98.00 | 96.58 | 95.02 |
buffalo_m has the same accuracy with buffalo_l.
buffalo_sc has the same accuracy with buffalo_s.
Note that these models are available for non-commercial research purposes only.
For insightface>=0.3.3, models will be downloaded automatically once we init app = FaceAnalysis() instance.
For insightface==0.3.2, you must first download the model package by command:
insightface-cli model.download buffalo_l
You can simply create a new model directory under ~/.insightface/models/ and replace the pretrained models we provide with your own models. And then call app = FaceAnalysis(name='your_model_zoo') to load these models.
The latest insightface libary only supports onnx models. Once you have trained detection or recognition models by PyTorch, MXNet or any other frameworks, you can convert it to the onnx format and then they can be called with insightface library.
import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
# Method-1, use FaceAnalysis
app = FaceAnalysis(allowed_modules=['detection']) # enable detection model only
app.prepare(ctx_id=0, det_size=(640, 640))
# Method-2, load model directly
detector = insightface.model_zoo.get_model('your_detection_model.onnx')
detector.prepare(ctx_id=0, input_size=(640, 640))
import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
handler = insightface.model_zoo.get_model('your_recognition_model.onnx')
handler.prepare(ctx_id=0)