Object Detection

ST Yolo X quantized

Use case : Object detection

Model description

ST Yolo X is a real-time object detection model targeted for real-time processing implemented in Tensorflow. This is an optimized ST version of the well known yolo x, quantized in int8 format using tensorflow lite converter.

Network information

Network information Value
Framework TensorFlow Lite
Quantization int8
Provenance
Paper

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

Recommended Platforms

Platform Supported Recommended
STM32L0 [] []
STM32L4 [] []
STM32U5 [] []
STM32H7 [x] []
STM32MP1 [x] []
STM32MP2 [x] [x]
STM32N6 [x] [x]

Performances

Metrics

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

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

Model Dataset Format Resolution Series Internal RAM (KiB) External RAM (KiB) Weights Flash (KiB) STM32Cube.AI version STEdgeAI Core version
st_yolo_x_nano COCO-Person Int8 192x192x3 STM32N6 297 0 980.38 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 256x256x3 STM32N6 560 0 980.31 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 256x256x3 STM32N6 971.62 0 2452.39 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 320x320x3 STM32N6 847.5 0 980.31 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 416x416x3 STM32N6 2682.88 0 980.31 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 480x480x3 STM32N6 2418.75 0 1383.56 10.2.0 2.2.0

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

Model Dataset Format Resolution Board Execution Engine Inference time (ms) Inf / sec STM32Cube.AI version STEdgeAI Core version
st_yolo_x_nano COCO-Person Int8 192x192x3 STM32N6570-DK NPU/MCU 6.01 166.39 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 256x256x3 STM32N6570-DK NPU/MCU 8.59 116.41 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 256x256x3 STM32N6570-DK NPU/MCU 21.27 47.01 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 320x320x3 STM32N6570-DK NPU/MCU 11.89 84.1 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 416x416x3 STM32N6570-DK NPU/MCU 17.69 56.53 10.2.0 2.2.0
st_yolo_x_nano COCO-Person Int8 480x480x3 STM32N6570-DK NPU/MCU 32.4 30.8 10.2.0 2.2.0

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

Model Format Resolution Series Activation RAM (KiB) Runtime RAM (KiB) Weights Flash (KiB) Code Flash (KiB) Total RAM Total Flash STM32Cube.AI version
st_yolo_x_nano Int8 192x192x3 STM32H7 162.42 64.05 891.18 165.3 226.47 1056.48 10.2.0
st_yolo_x_nano Int8 256x256x3 STM32H7 284.92 64.05 891.18 165.31 348.97 1056.49 10.2.0
st_yolo_x_nano Int8 256x256x3 STM32H7 463.9 83.8 2435.76 227.33 547.7 2663.09 10.2.0
st_yolo_x_nano Int8 320x320x3 STM32H7 442.42 64.05 891.18 165.36 506.47 1056.54 10.2.0

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
st_yolo_x_nano Int8 192x192x3 STM32H747I-DISCO 1 CPU 400 MHz 335.19 10.2.0
st_yolo_x_nano Int8 256x256x3 STM32H747I-DISCO 1 CPU 400 MHz 603.06 10.2.0
st_yolo_x_nano Int8 256x256x3 STM32H747I-DISCO 1 CPU 400 MHz 1708.16 10.2.0
st_yolo_x_nano Int8 320x320x3 STM32H747I-DISCO 1 CPU 400 MHz 967.8 10.2.0

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 Depth Multiplier Width Multiplier Anchors AP
st_yolo_x_nano Int8 192x192x3 0.33 0.25 1 36.1 %
st_yolo_x_nano Float 192x192x3 0.33 0.25 1 36.1 %
st_yolo_x_nano Int8 256x256x3 0.33 0.25 1 44.2 %
st_yolo_x_nano Float 256x256x3 0.33 0.25 1 44.1 %
st_yolo_x_nano Int8 256x256x3 0.5 0.4 1 50.1 %
st_yolo_x_nano Float 256x256x3 0.5 0.4 1 50.0 %
st_yolo_x_nano Int8 320x320x3 0.33 0.25 1 48.8 %
st_yolo_x_nano Float 320x320x3 0.33 0.25 1 48.5 %
st_yolo_x_nano Int8 416x416x3 0.33 0.25 1 54.0 %
st_yolo_x_nano Float 416x416x3 0.33 0.25 1 54.5 %
st_yolo_x_nano Int8 480x480x3 1.0 0.25 3 61.4 %
st_yolo_x_nano Float 480x480x3 1.0 0.25 3 62.1 %

* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100

Retraining and Integration in a simple example:

Please refer to the stm32ai-modelzoo-services GitHub 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} }

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