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Tiny Yolo v2 quantized

Use case : Object detection

Model description

Tiny Yolo v2 is a real-time object detection model targeted for real-time processing implemented in Tensorflow.

The model is quantized in int8 format using tensorflow lite converter.

Network information

Network information Value
Framework TensorFlow Lite
Quantization int8
Provenance https://github.com/AlexeyAB/darknet
Paper https://pjreddie.com/media/files/papers/YOLO9000.pdf

The models are quantized using tensorflow lite converter.

Network inputs / outputs

For an image resolution of NxM and NC classes

Input Shape Description
(1, W, H, 3) Single NxM RGB image with UINT8 values between 0 and 255
Output Shape Description
(1, WxH, NAx(5+NC)) FLOAT values Where WXH is the resolution of the output grid cell, NA is the number of anchors and NC is the number of classes

Recommended Platforms

Platform Supported Recommended
STM32L0 [] []
STM32L4 [] []
STM32U5 [] []
STM32H7 [x] [x]
STM32MP1 [x] [x]
STM32MP2 [x] []

Performances

Training

To train a tiny_yolo_v2 model with transfer learning or from scratch on your own dataset, you need to configure the user_config.yaml file following the tutorial under the training section. As an example, tiny_yolo_v2_224_config.yaml file is used to train this model on the person dataset, you can copy its content in the user_config.yaml file provided under the training section to reproduce the results presented below

Deployment

To deploy your trained model, you need to configure the same user_config.yaml file following the tutorial.

Metrics

Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.

Reference MCU memory footprint based on COCO Person dataset (see Accuracy for details on dataset)

Model Format Resolution Series Activation RAM Runtime RAM Weights Flash Code Flash Total RAM Total Flash STM32Cube.AI version
tiny_yolo_v2 Int8 224x224x3 STM32H7 249.35 KiB 7.98 KiB 10776 KiB 55.8 KiB 257.33 KiB 10831.8 KiB 9.1.0
tiny_yolo_v2 Int8 416x416x3 STM32H7 979.35 KiB 8.03 KiB 10776 KiB 55.86 KiB 987.38 KiB 10831.8 KiB 9.1.0

Reference MCU inference time based on COCO Person dataset (see Accuracy for details on dataset)

Model Format Resolution Board Execution Engine Frequency Inference time (ms) STM32Cube.AI version
tiny_yolo_v2 Int8 224x224x3 STM32H747I-DISCO 1 CPU 400 MHz 2742 ms 9.1.0
tiny_yolo_v2 Int8 416x416x3 STM32H747I-DISCO 1 CPU 400 MHz 10472.9 ms 9.1.0

Reference MPU inference time based on COCO Person dataset (see Accuracy for details on dataset)

Model Format Resolution Quantization Board Execution Engine Frequency Inference time (ms) %NPU %GPU %CPU X-LINUX-AI version Framework
tiny_yolo_v2 Int8 224x224x3 per-channel** STM32MP257F-DK2 NPU/GPU 800 MHz 120.8 ms 3.45 96.55 0 v5.1.0 OpenVX
tiny_yolo_v2 Int8 416x416x3 per-channel** STM32MP257F-DK2 NPU/GPU 800 MHz 425.6 ms 2.74 97.26 0 v5.1.0 OpenVX
tiny_yolo_v2 Int8 224x224x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz 410.50 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
tiny_yolo_v2 Int8 416x416x3 per-channel STM32MP157F-DK2 2 CPU 800 MHz 1347 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
tiny_yolo_v2 Int8 224x224x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz 619.70 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0
tiny_yolo_v2 Int8 416x416x3 per-channel STM32MP135F-DK2 1 CPU 1000 MHz 2105 ms NA NA 100 v5.1.0 TensorFlowLite 2.11.0

** To get the most out of MP25 NPU hardware acceleration, please use per-tensor quantization

AP on COCO Person dataset

Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 80, Number of images: 118,287

Model Format Resolution AP
tiny_yolo_v2 Int8 224x224x3 30.91 %
tiny_yolo_v2 Int8 416x416x3 41.44 %

* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001

Retraining and code generation

Please refer to the yaml explanations: here

Demos

Integration in a simple example

Please refer to the generic guideline here

References

[1] “Microsoft COCO: Common Objects in Context”. [Online]. Available: https://cocodataset.org/#download. @article{DBLP:journals/corr/LinMBHPRDZ14, author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick}, title = {Microsoft {COCO:} Common Objects in Context}, journal = {CoRR}, volume = {abs/1405.0312}, year = {2014}, url = {http://arxiv.org/abs/1405.0312}, archivePrefix = {arXiv}, eprint = {1405.0312}, timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, bibsource = {dblp computer science bibliography, https://dblp.org} }