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[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220 ### Pull Request Readiness Checklist This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206) Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
85 lines
5.0 KiB
Markdown
85 lines
5.0 KiB
Markdown
# OpenCV deep learning module samples
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## Model Zoo
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Check [a wiki](https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV) for a list of tested models.
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If OpenCV is built with [Intel's Inference Engine support](https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend) you can use [Intel's pre-trained](https://github.com/opencv/open_model_zoo) models.
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There are different preprocessing parameters such mean subtraction or scale factors for different models.
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You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/5.x/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
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```bash
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python object_detection.py opencv_fd --model /path/to/model.onnx
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```
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Check `-h` option to know which values are used by default:
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```bash
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python object_detection.py opencv_fd -h
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```
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### Sample models
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You can download sample models using ```download_models.py```. For example, the following command will download network weights for OpenCV Face Detector model and store them in FaceDetector folder:
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```bash
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python download_models.py --save_dir FaceDetector opencv_fd
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```
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You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/5.x/testdata/dnn).
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You also can use the script to download necessary files from your code. Assume you have the following code inside ```your_script.py```:
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```python
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from download_models import downloadFile
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filepath1 = downloadFile("https://huggingface.co/onnxmodelzoo/ssd_mobilenet_v1_12/resolve/main/ssd_mobilenet_v1_12.onnx", None, filename="ssd_mobilenet_v1_12.onnx", save_dir="save_dir_1")
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filepath2 = downloadFile("https://huggingface.co/onnxmodelzoo/ssd_mobilenet_v1_12/resolve/main/ssd_mobilenet_v1_12.onnx", "83536889adce1eda154175f8e3b156dd20443631", filename="ssd_mobilenet_v1_12.onnx")
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print(filepath1)
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print(filepath2)
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# Your code
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```
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By running the following commands, you will get **ssd_mobilenet_v1_12.onnx** file:
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```bash
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export OPENCV_DOWNLOAD_DATA_PATH=download_folder
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python your_script.py
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```
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**Note** that you can provide a directory using **save_dir** parameter or via **OPENCV_SAVE_DIR** environment variable.
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#### Face detection
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[An origin model](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detector)
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with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
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To achieve the best accuracy run the model on BGR images resized to `300x300` applying mean subtraction
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of values `(104, 177, 123)` for each blue, green and red channels correspondingly.
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The following are accuracy metrics obtained using [COCO object detection evaluation
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tool](http://cocodataset.org/#detections-eval) on [FDDB dataset](http://vis-www.cs.umass.edu/fddb/)
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(see [script](https://github.com/opencv/opencv/blob/5.x/modules/dnn/misc/face_detector_accuracy.py))
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applying resize to `300x300` and keeping an origin images' sizes.
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```
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AP - Average Precision | FP32/FP16 | UINT8 | FP32/FP16 | UINT8 |
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AR - Average Recall | 300x300 | 300x300 | any size | any size |
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--------------------------------------------------|-----------|----------------|-----------|----------------|
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AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.408 | 0.408 | 0.378 | 0.328 (-0.050) |
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AP @[ IoU=0.50 | area= all | maxDets=100 ] | 0.849 | 0.849 | 0.797 | 0.790 (-0.007) |
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AP @[ IoU=0.75 | area= all | maxDets=100 ] | 0.251 | 0.251 | 0.208 | 0.140 (-0.068) |
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AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050 | 0.051 (+0.001) | 0.107 | 0.070 (-0.037) |
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AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381 | 0.379 (-0.002) | 0.380 | 0.368 (-0.012) |
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AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455 | 0.455 | 0.412 | 0.337 (-0.075) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.299 | 0.299 | 0.279 | 0.246 (-0.033) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.482 | 0.482 | 0.476 | 0.436 (-0.040) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.496 | 0.496 | 0.491 | 0.451 (-0.040) |
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AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189 | 0.193 (+0.004) | 0.284 | 0.232 (-0.052) |
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AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) | 0.470 | 0.458 (-0.012) |
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AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) |
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```
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## References
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* [Models downloading script](https://github.com/opencv/opencv/blob/5.x/samples/dnn/download_models.py)
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* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/5.x/testdata/dnn)
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* [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
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* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/5.x/samples/data/dnn)
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