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- ---
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- license: other
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- license_name: sla0044
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- license_link: >-
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- https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/LICENSE.md
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
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+ license_name: sla0044
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+ license_link: >-
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+ https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/st_yolo_x/ST_pretrainedmodel_public_dataset/LICENSE.md
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+ pipeline_tag: object-detection
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+ ---
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+ # ST Yolo X quantized
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+
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+ ## **Use case** : `Object detection`
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+
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+ # Model description
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+
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+
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+ ST Yolo X is a real-time object detection model targeted for real-time processing implemented in Tensorflow.
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+ This is an optimized ST version of the well known yolo x, quantized in int8 format using tensorflow lite converter.
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+
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+ ## Network information
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+
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+ | Network information | Value |
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+ |-------------------------|-----------------|
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+ | Framework | TensorFlow Lite |
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+ | Quantization | int8 |
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+ | Provenance | TO DO |
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+ | Paper | TO DO |
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+
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+
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+
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+ ## Network inputs / outputs
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+
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+ For an image resolution of NxM and NC classes
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+
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+ | Input Shape | Description |
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+ | ----- | ----------- |
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+ | (1, W, H, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
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+
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+ | Output Shape | Description |
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+ | ----- | ----------- |
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+ | TO DO |
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+
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+
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+ ## Recommended Platforms
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+
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+ | Platform | Supported | Recommended |
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+ |----------|-----------|-------------|
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+ | STM32L0 | [] | [] |
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+ | STM32L4 | [] | [] |
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+ | STM32U5 | [] | [] |
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+ | STM32H7 | [x] | [] |
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+ | STM32MP1 | [x] | [] |
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+ | STM32MP2 | [x] | [x] |
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+ | STM32N6 | [x] | [x] |
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+
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+
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+ # Performances
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+
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+ ## Metrics
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+
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+ Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
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+
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+ ### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
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+ |Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB)| Weights Flash (KiB)| STM32Cube.AI version | STEdgeAI Core version |
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+ |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+
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+ ### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
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+ | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
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+ |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ STM32N6570-DK | NPU/MCU | 17.99 | 55.59 | 10.0.0 | 2.0.0 |
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+
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+ ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
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+
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+ | Model | Format | Resolution | Series | Activation RAM (KiB) | Runtime RAM (KiB)| Weights Flash (KiB)| Code Flash (KiB)| Total RAM | Total Flash | STM32Cube.AI version |
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+ |-------------------|--------|--------------|---------|----------------|-------------|---------------|------------|-------------|--------------|-----------------------|
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+
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+
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+ ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
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+
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+
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+ | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
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+ |------------------|--------|------------|------------------|------------------|-------------|---------------------|-----------------------|
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 |
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+
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+
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+
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+ ### AP on COCO Person dataset
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+
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+
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+ 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
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+
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+ | Model | Format | Resolution | AP |
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+ |-------|--------|------------|----------------|
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+ | [st_yolo_x_nano](https://github.com/STMicroelectronics/stm32ai-modelzoo/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 % |
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+
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+ \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
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+
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+ ## Retraining and Integration in a simple example:
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+
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+ Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
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+
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+
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+ # References
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+
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+
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+ <a id="1">[1]</a>
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+ “Microsoft COCO: Common Objects in Context”. [Online]. Available: https://cocodataset.org/#download.
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+ @article{DBLP:journals/corr/LinMBHPRDZ14,
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+ author = {Tsung{-}Yi Lin and
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+ Michael Maire and
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+ Serge J. Belongie and
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+ Lubomir D. Bourdev and
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+ Ross B. Girshick and
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+ James Hays and
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+ Pietro Perona and
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+ Deva Ramanan and
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+ Piotr Doll{'{a} }r and
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+ C. Lawrence Zitnick},
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+ title = {Microsoft {COCO:} Common Objects in Context},
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+ journal = {CoRR},
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+ volume = {abs/1405.0312},
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+ year = {2014},
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+ url = {http://arxiv.org/abs/1405.0312},
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+ archivePrefix = {arXiv},
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+ eprint = {1405.0312},
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+ timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
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+ biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+