a316b11aaa
* added HDDL VPU support * changed to return True in one line if any device connected * dnn: use releaseHDDLPlugin() * dnn(hddl): fix conditions |
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|---|---|---|
| .. | ||
| face_detector | ||
| action_recognition.py | ||
| classification.cpp | ||
| classification.py | ||
| CMakeLists.txt | ||
| colorization.cpp | ||
| colorization.py | ||
| common.hpp | ||
| common.py | ||
| custom_layers.hpp | ||
| dasiamrpn_tracker.py | ||
| edge_detection.py | ||
| fast_neural_style.py | ||
| human_parsing.cpp | ||
| human_parsing.py | ||
| js_face_recognition.html | ||
| mask_rcnn.py | ||
| mobilenet_ssd_accuracy.py | ||
| models.yml | ||
| object_detection.cpp | ||
| object_detection.py | ||
| openpose.cpp | ||
| openpose.py | ||
| optical_flow.py | ||
| README.md | ||
| segmentation.cpp | ||
| segmentation.py | ||
| shrink_tf_graph_weights.py | ||
| siamrpnpp.py | ||
| text_detection.cpp | ||
| text_detection.py | ||
| tf_text_graph_common.py | ||
| tf_text_graph_efficientdet.py | ||
| tf_text_graph_faster_rcnn.py | ||
| tf_text_graph_mask_rcnn.py | ||
| tf_text_graph_ssd.py | ||
| virtual_try_on.py | ||
OpenCV deep learning module samples
Model Zoo
Check a wiki for a list of tested models.
If OpenCV is built with Intel's Inference Engine support you can use Intel's pre-trained models.
There are different preprocessing parameters such mean subtraction or scale factors for different models. You may check the most popular models and their parameters at models.yml configuration file. It might be also used for aliasing samples parameters. In example,
python object_detection.py opencv_fd --model /path/to/caffemodel --config /path/to/prototxt
Check -h option to know which values are used by default:
python object_detection.py opencv_fd -h
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) |