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Update Readme ST Model Zoo

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@@ -1,10 +1,3 @@
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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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  ## **Use case** : `Object detection`
@@ -21,8 +14,8 @@ This is an optimized ST version of the well known yolo x, quantized in int8 form
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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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@@ -36,7 +29,7 @@ For an image resolution of NxM and NC classes
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  | Output Shape | Description |
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  | ----- | ----------- |
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- | TO DO |
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  ## Recommended Platforms
@@ -59,65 +52,64 @@ For an image resolution of NxM and NC classes
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  Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
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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 |
63
- |----------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [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 |
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- | [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 |
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- | [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 |
67
- | [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 |
68
- | [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 |
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-
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  ### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
71
- | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
72
- |--------|------------------|--------|-------------|------------------|------------------|---------------------|-------|----------------------|-------------------------|
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- | [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 |
74
- | [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 |
75
- | [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 |
76
- | [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 |
77
- | [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 |
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- STM32N6570-DK | NPU/MCU | 17.99 | 55.59 | 10.0.0 | 2.0.0 |
79
-
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  ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
81
 
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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 |
83
- |-------------------|--------|--------------|---------|----------------|-------------|---------------|------------|-------------|--------------|-----------------------|
84
- | [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 |
85
- | [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 |
86
- | [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 |
87
- | [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 |
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-
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  ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
91
 
92
 
93
- | 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/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 |
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- | [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 |
97
- | [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 |
98
- | [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 |
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  ### AP on COCO Person dataset
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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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- | Model | Format | Resolution | AP |
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- |-------|--------|------------|----------------|
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- | [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 % |
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- | [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 % |
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- | [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 % |
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- | [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 % |
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- | [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 % |
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- | [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 % |
115
- | [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 % |
116
- | [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 % |
117
- | [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 % |
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- | [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 % |
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120
- \* EVAL_IOU = 0.4, NMS_THRESH = 0.5, SCORE_THRESH =0.001
 
 
 
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122
  ## Retraining and Integration in a simple example:
123
 
@@ -150,5 +142,4 @@ Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/S
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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}
153
- }
154
-
 
 
 
 
 
 
 
 
1
  # ST Yolo X quantized
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  ## **Use case** : `Object detection`
 
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  |-------------------------|-----------------|
15
  | Framework | TensorFlow Lite |
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  | Quantization | int8 |
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+ | Provenance | |
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+ | Paper | |
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  | Output Shape | Description |
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  | ----- | ----------- |
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+ | | |
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35
  ## Recommended Platforms
 
52
  Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
53
 
54
  ### Reference **NPU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
55
+ | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
56
+ |-----------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|----------|----------------------|----------------------|-----------------------|------------------------|-------------------------|
57
+ | [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 | 297 | 0 | 980.38 | 10.2.0 | 2.2.0 |
58
+ | [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 | 560 | 0 | 980.31 | 10.2.0 | 2.2.0 |
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+ | [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 | 2452.39 | 10.2.0 | 2.2.0 |
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+ | [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 | 847.5 | 0 | 980.31 | 10.2.0 | 2.2.0 |
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+ | [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 | 2682.88 | 0 | 980.31 | 10.2.0 | 2.2.0 |
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+ | [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_480/st_yolo_x_nano_480_1.0_0.25_3_int8.tflite) | COCO-Person | Int8 | 480x480x3 | STM32N6 | 2418.75 | 0 | 1383.56 | 10.2.0 | 2.2.0 |
63
  ### Reference **NPU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
64
+ | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
65
+ |-----------------------------------------------------------------------------------------------------------------------------------|-------------|----------|--------------|---------------|--------------------|-----------------------|-------------|------------------------|-------------------------|
66
+ | [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 | 6.01 | 166.39 | 10.2.0 | 2.2.0 |
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+ | [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 | 8.59 | 116.41 | 10.2.0 | 2.2.0 |
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+ | [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 | 21.27 | 47.01 | 10.2.0 | 2.2.0 |
69
+ | [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 | 11.89 | 84.1 | 10.2.0 | 2.2.0 |
70
+ | [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 | 17.69 | 56.53 | 10.2.0 | 2.2.0 |
71
+ | [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_480/st_yolo_x_nano_480_1.0_0.25_3_int8.tflite) | COCO-Person | Int8 | 480x480x3 | STM32N6570-DK | NPU/MCU | 32.4 | 30.8 | 10.2.0 | 2.2.0 |
 
72
  ### Reference **MCU** memory footprint based on COCO Person dataset (see Accuracy for details on dataset)
73
 
74
+ | Model | Format | Resolution | Series | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM | Total Flash | STM32Cube.AI version |
75
+ |----------------------------------------------------------------------------------------------------------------------------------|----------|--------------|----------|------------------------|---------------------|-----------------------|--------------------|-------------|---------------|------------------------|
76
+ | [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 | 165.3 | 226.47 | 1056.48 | 10.2.0 |
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+ | [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 | 165.31 | 348.97 | 1056.49 | 10.2.0 |
78
+ | [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 | 227.33 | 547.7 | 2663.09 | 10.2.0 |
79
+ | [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 | 165.36 | 506.47 | 1056.54 | 10.2.0 |
 
80
 
81
  ### Reference **MCU** inference time based on COCO Person dataset (see Accuracy for details on dataset)
82
 
83
 
84
+ | Model | Format | Resolution | Board | Execution Engine | Frequency | Inference time (ms) | STM32Cube.AI version |
85
+ |----------------------------------------------------------------------------------------------------------------------------------|----------|--------------|------------------|--------------------|-------------|-----------------------|------------------------|
86
+ | [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 | 335.19 | 10.2.0 |
87
+ | [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 | 603.06 | 10.2.0 |
88
+ | [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 | 1708.16 | 10.2.0 |
89
+ | [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 | 967.8 | 10.2.0 |
 
90
 
91
 
92
  ### AP on COCO Person dataset
93
 
 
94
  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
95
 
96
+ | Model | Format | Resolution | Depth Multiplier | Width Multiplier | Anchors | AP |
97
+ |-------|--------|------------|------------------|------------------|---------|----|
98
+ | [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 | 0.33 | 0.25 | 1 | 36.1 % |
99
+ | [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 | 0.33 | 0.25 | 1 | 36.1 % |
100
+ | [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 | 0.33 | 0.25 | 1 | 44.2 % |
101
+ | [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 | 0.33 | 0.25 | 1 | 44.1 % |
102
+ | [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 | 0.5 | 0.4 | 1 | 50.1 % |
103
+ | [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 | 0.5 | 0.4 | 1 | 50.0 % |
104
+ | [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 | 0.33 | 0.25 | 1 | 48.8 % |
105
+ | [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 | 0.33 | 0.25 | 1 | 48.5 % |
106
+ | [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 | 0.33 | 0.25 | 1 | 54.0 % |
107
+ | [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 | 0.33 | 0.25 | 1 | 54.5 % |
108
+ | [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_480/st_yolo_x_nano_480_1.0_0.25_3_int8.tflite) | Int8 | 480x480x3 | 1.0 | 0.25 | 3 | 61.4 % |
109
+ | [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_480/st_yolo_x_nano_480_1.0_0.25_3.h5) | Float | 480x480x3 | 1.0 | 0.25 | 3 | 62.1 % |
110
+
111
+
112
+ \* EVAL_IOU = 0.5, NMS_THRESH = 0.5, SCORE_THRESH = 0.001, MAX_DETECTIONS = 100
113
 
114
  ## Retraining and Integration in a simple example:
115
 
 
142
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
143
  biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
144
  bibsource = {dblp computer science bibliography, https://dblp.org}
145
+ }