OpenCV deep learning module samples
Model Zoo
Object detection
Face detection
An origin model
with single precision floating point weights has been quantized using TensorFlow framework.
To achieve the best accuracy run the model on BGR images resized to 300x300 applying mean subtraction
of values (104, 177, 123) for each blue, green and red channels correspondingly.
The following are accuracy metrics obtained using COCO object detection evaluation
tool on FDDB dataset
(see script)
applying resize to 300x300 and keeping an origin images' sizes.
AP - Average Precision | FP32/FP16 | UINT8 | FP32/FP16 | UINT8 |
AR - Average Recall | 300x300 | 300x300 | any size | any size |
--------------------------------------------------|-----------|----------------|-----------|----------------|
AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.408 | 0.408 | 0.378 | 0.328 (-0.050) |
AP @[ IoU=0.50 | area= all | maxDets=100 ] | 0.849 | 0.849 | 0.797 | 0.790 (-0.007) |
AP @[ IoU=0.75 | area= all | maxDets=100 ] | 0.251 | 0.251 | 0.208 | 0.140 (-0.068) |
AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050 | 0.051 (+0.001) | 0.107 | 0.070 (-0.037) |
AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381 | 0.379 (-0.002) | 0.380 | 0.368 (-0.012) |
AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455 | 0.455 | 0.412 | 0.337 (-0.075) |
AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.299 | 0.299 | 0.279 | 0.246 (-0.033) |
AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.482 | 0.482 | 0.476 | 0.436 (-0.040) |
AR @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.496 | 0.496 | 0.491 | 0.451 (-0.040) |
AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189 | 0.193 (+0.004) | 0.284 | 0.232 (-0.052) |
AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) | 0.470 | 0.458 (-0.012) |
AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) |
Classification
| Model |
Scale |
Size WxH |
Mean subtraction |
Channels order |
| GoogLeNet |
1.0 |
224x224 |
104 117 123 |
BGR |
| SqueezeNet |
1.0 |
227x227 |
0 0 0 |
BGR |
Semantic segmentation
| Model |
Scale |
Size WxH |
Mean subtraction |
Channels order |
| ENet |
0.00392 (1/255) |
1024x512 |
0 0 0 |
RGB |
| FCN8s |
1.0 |
500x500 |
0 0 0 |
BGR |
References
