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] | [] |
STM32MP1 | [x] | [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 |
---|---|---|---|---|---|---|---|---|---|
tiny_yolo_v2 | COCO-Person | Int8 | 224x224x3 | STM32N6 | 392 | 0.0 | 10804.81 | 10.0.0 | 2.0.0 |
tiny_yolo_v2 | ST-Person | Int8 | 224x224x3 | STM32N6 | 392 | 0.0 | 10804.81 | 10.0.0 | 2.0.0 |
tiny_yolo_v2 | COCO-Person | Int8 | 416x416x3 | STM32N6 | 1880.12 | 0.0 | 10829 | 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 |
---|---|---|---|---|---|---|---|---|---|
tiny_yolo_v2 | COCO-Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 30.67 | 32.61 | 10.0.0 | 2.0.0 |
tiny_yolo_v2 | ST-Person | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 30.67 | 32.61 | 10.0.0 | 2.0.0 |
tiny_yolo_v2 | COCO-Person | Int8 | 416x416x3 | STM32N6570-DK | NPU/MCU | 50.91 | 19.64 | 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 | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
---|---|---|---|---|---|---|---|---|---|---|
tiny_yolo_v2 | Int8 | 192x192x3 | STM32H7 | 220.6 KiB | 7.98 KiB | 10775.98 KiB | 55.85 KiB | 228.58 KiB | 10831.83 KiB | 10.0.0 |
tiny_yolo_v2 | Int8 | 224x224x3 | STM32H7 | 249.35 KiB | 7.98 KiB | 10775.98 KiB | 55.8 KiB | 257.33 KiB | 10831.78 KiB | 10.0.0 |
tiny_yolo_v2 | Int8 | 416x416x3 | STM32H7 | 1263.07 KiB | 8.03 KiB | 10775.98 KiB | 55.85 KiB | 1271.1 KiB | 10831.83 KiB | 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 |
---|---|---|---|---|---|---|---|
tiny_yolo_v2 | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 3006.3 ms | 10.0.0 |
tiny_yolo_v2 | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 2742.3 ms | 10.0.0 |
tiny_yolo_v2 | Int8 | 416x416x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 10468.2 ms | 10.0.0 |
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 | 192x192x3 | 33.7 % |
tiny_yolo_v2 | Float | 192x192x3 | 34.5 % |
tiny_yolo_v2 | Int8 | 224x224x3 | 37.3 % |
tiny_yolo_v2 | Float | 224x224x3 | 38.4 % |
tiny_yolo_v2 | Int8 | 416x416x3 | 50.7 % |
tiny_yolo_v2 | Float | 416x416x3 | 51.5 % |
* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
AP on ST Person dataset
Model | Format | Resolution | AP |
---|---|---|---|
tiny_yolo_v2 | Int8 | 224x224x3 | 34.0 % |
* 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} }