--- 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 [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} }