--- license: other license_name: sla0044 license_link: >- https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/LICENSE.md pipeline_tag: 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 | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6 | 324 | 0.0 | 1028.08 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6 | 624 | 0.0 | 1028.08 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6 | 971.62 | 0.0 | 2547.17 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 320x320x3 | STM32N6 | 968.5 | 0.0 | 1028.08 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_416/st_yolo_x_nano_416_0.33_0.25_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 192x192x3 | STM32N6570-DK | NPU/MCU | 5.99 | 166.94 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 8.5 | 117.65 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4_int8.tflite) | COCO-Person | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 21.12 | 47.35 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25_int8.tflite) | COCO-Person | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 11.59 | 86.29 | 10.0.0 | 2.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_416/st_yolo_x_nano_416_0.33_0.25_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25_int8.tflite) | Int8 | 192x192x3 | STM32H7 | 162.42 | 64.05 | 891.18 | 166.19 | 226.47 | 1057.37 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25_int8.tflite) | Int8 | 256x256x3 | STM32H7 | 284.92 | 64.05 | 891.18 | 166.21 | 348.97 | 1057.39 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4_int8.tflite) | Int8 | 256x256x3 | STM32H7 | 463.9 | 83.8 | 2435.76 | 228.22| 547.7 |2663.98 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25_int8.tflite) | Int8 | 192x192x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 352.4 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25_int8.tflite) | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 619.92 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4_int8.tflite) | Int8 | 256x256x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 1696.59 | 10.0.0 | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25_int8.tflite) | Int8 | 320x320x3 | STM32H747I-DISCO | 1 CPU | 400 MHz | 988.86 | 10.0.0 | ### AP on COCO Person dataset Dataset details: [link](https://cocodataset.org/#download) , License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) , Quotation[[1]](#1) , Number of classes: 80, Number of images: 118,287 | Model | Format | Resolution | AP | |-------|--------|------------|----------------| | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25_int8.tflite) | Int8 | 192x192x3 | 45.1 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_192/st_yolo_x_nano_192_0.33_0.25.h5) | Float | 192x192x3 | 45.2 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25_int8.tflite) | Int8 | 256x256x3 | 53.6 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.33_0.25.h5) | Float | 256x256x3 | 53.3 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4_int8.tflite) | Int8 | 256x256x3 | 58.6 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_256/st_yolo_x_nano_256_0.5_0.4.h5) | Float | 256x256x3 | 58.7 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25_int8.tflite) | Int8 | 320x320x3 | 57.1 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_320/st_yolo_x_nano_320_0.33_0.25.h5) | Float | 320x320x3 | 57.1 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_416/st_yolo_x_nano_416_0.33_0.25_int8.tflite) | Int8 | 416x416x3 | 62.2 % | | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/coco_2017_person/st_yolo_x_nano_416/st_yolo_x_nano_416_0.33_0.25.h5) | Float | 416x416x3 | 62.5 % | \* 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](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) # 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} }