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 |
TO DO |
| Paper |
TO DO |
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 |
| TO DO |
|
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 |
324 |
0.0 |
1028.08 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
256x256x3 |
STM32N6 |
624 |
0.0 |
1028.08 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
256x256x3 |
STM32N6 |
971.62 |
0.0 |
2547.17 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
320x320x3 |
STM32N6 |
968.5 |
0.0 |
1028.08 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
416x416x3 |
STM32N6 |
2640.62 |
0.0 |
1027.89 |
10.0.0 |
2.0.0 |
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 |
5.99 |
166.94 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
256x256x3 |
STM32N6570-DK |
NPU/MCU |
8.5 |
117.65 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
256x256x3 |
STM32N6570-DK |
NPU/MCU |
21.12 |
47.35 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
320x320x3 |
STM32N6570-DK |
NPU/MCU |
11.59 |
86.29 |
10.0.0 |
2.0.0 |
| st_yolo_x_nano |
COCO-Person |
Int8 |
416x416x3 |
|
|
|
|
|
|
| STM32N6570-DK |
NPU/MCU |
17.99 |
55.59 |
10.0.0 |
2.0.0 |
|
|
|
|
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 |
166.19 |
226.47 |
1057.37 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
256x256x3 |
STM32H7 |
284.92 |
64.05 |
891.18 |
166.21 |
348.97 |
1057.39 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
256x256x3 |
STM32H7 |
463.9 |
83.8 |
2435.76 |
228.22 |
547.7 |
2663.98 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
320x320x3 |
STM32H7 |
442.42 |
64.05 |
891.18 |
166.25 |
506.47 |
1057.43 |
10.0.0 |
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 |
352.4 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
256x256x3 |
STM32H747I-DISCO |
1 CPU |
400 MHz |
619.92 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
256x256x3 |
STM32H747I-DISCO |
1 CPU |
400 MHz |
1696.59 |
10.0.0 |
| st_yolo_x_nano |
Int8 |
320x320x3 |
STM32H747I-DISCO |
1 CPU |
400 MHz |
988.86 |
10.0.0 |
AP on COCO Person dataset
Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 80, Number of images: 118,287
* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
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}
}
