ST Yolo LC v1 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 | Value |
---|---|
Framework | TensorFlow Lite |
Quantization | int8 |
Paper | https://pjreddie.com/media/files/papers/YOLO9000.pdf |
The models are quantized using tensorflow lite converter.
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 |
Platform | Supported | Recommended |
---|---|---|
STM32L0 | [] | [] |
STM32L4 | [] | [] |
STM32U5 | [] | [] |
STM32H7 | [x] | [x] |
STM32MP1 | [x] | [x] |
STM32MP2 | [x] | [] |
STM32N6 | [x] | [] |
Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
---|---|---|---|---|---|---|---|---|---|
st_yolo_lc_v1 | COCO-Person | Int8 | 192x192x3 | STM32N6 | 252 | 0.0 | 328.19 | 10.0.0 | 2.0.0 |
st_yolo_lc_v1 | COCO-Person | Int8 | 256x256x3 | STM32N6 | 343 | 0.0 | 328.19 | 10.0.0 | 2.0.0 |
st_yolo_lc_v1 | COCO-Person | Int8 | 256x256x3 | STM32N6 | 576 | 0.0 | 328.19 | 10.0.0 | 2.0.0 |
Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
---|---|---|---|---|---|---|---|---|---|
st_yolo_lc_v1 | COCO-Person | Int8 | 192x192x3 | STM32N6570-DK | NPU/MCU | 1.96 | 510.20 | 10.0.0 | 2.0.0 |
st_yolo_lc_v1 | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 2.35 | 425.53 | 10.0.0 | 2.0.0 |
st_yolo_lc_v1 | COCO-Person | Int8 256x256x3 | STM32N6570-DK | NPU/MCU | 3.01 | 332.23 | 10.0.0 | 2.0.0 |
Model | Format | Resolution | Series | Activation RAM | Runtime RAM | Weights Flash | Code Flash | Total RAM | Total Flash | STM32Cube.AI version |
---|---|---|---|---|---|---|---|---|---|---|
st_yolo_lc_v1 | Int8 | 192x192x3 | STM32H7 | 166.29 KiB | 8.09 KiB | 276.73 KiB | 53.48 KiB | 174.38 KiB | 330.21 KiB | 10.0.0 |
st_yolo_lc_v1 | Int8 | 224x224x3 | STM32H7 | 217.29 KiB | 8.09 KiB | 276.73 KiB | 53.48 KiB | 225.38 KiB | 330.21 KiB | 10.0.0 |
st_yolo_lc_v1 | Int8 | 256x256x3 | STM32H7 | 278.29 KiB | 8.09 KiB | 276.73 KiB | 53.48 KiB | 286.38 KiB | 330.21 KiB | 10.0.0 |
Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
---|---|---|---|---|---|---|---|
st_yolo_lc_v1 | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 179.01 | 10.0.0 |
st_yolo_lc_v1 | Int8 | 224x224x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 244.7 | 10.0.0 |
st_yolo_lc_v1 | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 321.38 | 10.0.0 |
Model | Format | Resolution | Quantization | Board | Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version | Framework |
---|---|---|---|---|---|---|---|---|---|---|---|---|
st_yolo_lc_v1 | Int8 | 192x192x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 12.00 ms | 2.62 | 97.38 | 0 | v5.1.0 | OpenVX |
st_yolo_lc_v1 | Int8 | 224x224x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 17.92 ms | 2.43 | 97.57 | 0 | v5.1.0 | OpenVX |
st_yolo_lc_v1 | Int8 | 256x256x3 | per-channel** | STM32MP257F-DK2 | NPU/GPU | 800 MHz | 14.43 ms | 3.20 | 96.80 | 0 | v5.1.0 | OpenVX |
st_yolo_lc_v1 | Int8 | 192x192x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 32.84 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
st_yolo_lc_v1 | Int8 | 224x224x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 45.13 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
st_yolo_lc_v1 | Int8 | 256x256x3 | per-channel | STM32MP157F-DK2 | 2 CPU | 800 MHz | 59.38 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
st_yolo_lc_v1 | Int8 | 192x192x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 52.64 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
st_yolo_lc_v1 | Int8 | 224x224x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 71.26 ms | NA | NA | 100 | v5.1.0 | TensorFlowLite 2.11.0 |
st_yolo_lc_v1 | Int8 | 256x256x3 | per-channel | STM32MP135F-DK2 | 1 CPU | 1000 MHz | 93.50 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
Dataset details: link , License CC BY 4.0 , Quotation[1] , Number of classes: 80, Number of images: 118,287
Model | Format | Resolution | AP |
---|---|---|---|
st_yolo_lc_v1 | Int8 | 192x192x3 | 39.0 % |
st_yolo_lc_v1 | Float | 192x192x3 | 39.2 % |
st_yolo_lc_v1 | Int8 | 224x224x3 | 42.94 % |
st_yolo_lc_v1 | Float | 224x224x3 | 41.7 % |
st_yolo_lc_v1 | Int8 | 256x256x3 | 43.8 % |
st_yolo_lc_v1 | Float | 256x256x3 | 44.7 % |
* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
Please refer to the stm32ai-modelzoo-services GitHub here
[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} }