README.md

---
license: other
license_name: sla0044
license_link: >-
  https://github.com/STMicroelectronics/stm32aimodelzoo/object_detection/tiny_yolo_v2/ST_pretrainedmodel_custom_dataset/LICENSE.md
pipeline_tag: object-detection
---
# 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | COCO-Person | Int8     | 224x224x3  | STM32N6   | 392 | 0.0 | 10804.81 | 10.0.0 | 2.0.0 |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_custom_dataset/st_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | ST-Person      | Int8     | 224x224x3  | STM32N6 | 392 | 0.0 | 10804.81 | 10.0.0        |     2.0.0   |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | COCO-Person      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU | 30.67 | 32.61 |10.0.0 | 2.0.0 |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_custom_dataset/st_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | ST-Person      | Int8     | 224x224x3  | STM32N6570-DK   |   NPU/MCU | 30.67 | 32.61| 10.0.0 | 2.0.0 |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_192/tiny_yolo_v2_192_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_192/tiny_yolo_v2_192_int8.tflite) | Int8   | 192x192x3  | STM32H747I-DISCO | 1 CPU            | 400 MHz     | 3006.3 ms           | 10.0.0                 |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | Int8   | 224x224x3  | STM32H747I-DISCO | 1 CPU            | 400 MHz     | 2742.3 ms           | 10.0.0                 |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | 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](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       |
|-------|--------|------------|----------------|
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_192/tiny_yolo_v2_192_int8.tflite) | Int8 | 192x192x3   | 33.7 % |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_192/tiny_yolo_v2_192.h5) | Float | 192x192x3   | 34.5 % |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | Int8 | 224x224x3   | 37.3 % |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_224/tiny_yolo_v2_224.h5) | Float | 224x224x3   | 38.4 % |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416_int8.tflite) | Int8 | 416x416x3   | 50.7 % |
| [tiny_yolo_v2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_public_dataset/coco_2017_person/tiny_yolo_v2_416/tiny_yolo_v2_416.h5) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/object_detection/tiny_yolo_v2/ST_pretrainedmodel_custom_dataset/st_person/tiny_yolo_v2_224/tiny_yolo_v2_224_int8.tflite) | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)


# References


<a id="1">[1]</a>
“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}
}