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deeplab_v3

Image Segmentation
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@@ -70,9 +70,9 @@ Measures are done with default STM32Cube.AI configuration with enabled input / o
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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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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 256x256x3 | STM32N6 | 2253.5 | 0.0 | 1001.25 | 10.0.0 | 2.0.0 |
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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) |person COCO 2017 + PASCAL VOC 2012 | Int8 | 320x320x3 | STM32N6 | 2446.0 | 0.0 | 1000.41 | 10.0.0 | 2.0.0 |
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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 416x416x3 | STM32N6 | 2743.5 | 2028.0 | 2721.19 | 10.0.0 | 2.0.0 |
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@@ -81,24 +81,24 @@ Measures are done with default STM32Cube.AI configuration with enabled input / o
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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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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 27.36 | 36.54 | 10.0.0 | 2.0.0 |
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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 44.99 | 22.22 | 10.0.0 | 2.0.0 |
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- | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 416x416x3 | STM32N6570-DK | NPU/MCU | 191.91 | 5.21 | 10.0.0 | 2.0.0 |
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  ### Reference **MPU** inference time based on COCO 2017 + PASCAL VOC 2012 segmentation dataset 21 classes and a derivative person dataset from it (see Accuracy for details on dataset)
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  | Model | Dataset | Format | Resolution | Quantization | Board| Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version |Framework |
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  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------|------------|----------------|-------------------|------------------|-----------|---------------------|-------|--------|------|--------------------|-----------------------|
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  | [DeepLabV3 per tensor (no ASPP)](https://www.st.com/en/embedded-software/x-linux-ai.html) | COCO 2017 + PASCAL VOC 2012 | Int8 | 257x257x3 | per-tensor | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 52.75 | 99.2 | 0.80 | 0 | v5.1.0 | OpenVX | | | | | v5.1.0
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- | [DeepLabV3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8.tflite) | COCO 2017 + PASCAL VOC 2012 | Int8 (tflite) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 806.12 | 8.73| 91.27 | 0 | v5.1.0 | OpenVX |
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- | [DeepLabV3 MobileNetv2 ASPPv1 mixed precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8_f32.tflite) | COCO 2017 + PASCAL VOC 2012 | Int8 & float32 (tflite) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 894.56 | 7.67 | 92.33 | 0 | v5.1.0 | OpenVX |
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- | [DeepLabV3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_qdq_int8.onnx) | COCO 2017 + PASCAL VOC 2012 | Int8 (onnx) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 729.62 | 3.0 | 97.0 | 0 | v5.1.0| OpenVX |
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  - **DeepLabV3 per tensor**:
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  This model, which does not include ASPP (Atrous Spatial Pyramid Pooling), was downloaded from the TensorFlow DeepLabV3 page on [Kaggle](https://www.kaggle.com/models/tensorflow/deeplabv3/).
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  - **The onnx DeepLabv3 MobileNetv2 ASPPv1 per channel**:
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- The quantized TFLite model is derived from the DeepLabV3 float precision model. The ONNX quantized model is obtained by quantizing the DeepLabV3 float model using the [deeplab_v3_mobilenetv2_05_16_512_asppv1_onnx_config](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_onnx_config.yaml) YAML file.
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  **Note:** These results were obtained using the exact YAML files mentioned above and a specific quantization set containing 4 images from the PASCAL VOC dataset with the following IDs:
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  - 2008_004804
@@ -139,10 +139,10 @@ Moreover, IoU are averaged on all classes including background.
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  | Model Description | Resolution | Format | Accuracy | Averaed IoU |
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  |--------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|------------|----------|--------------|
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  | [DeepLabV3 per tensor (no ASPP)](https://www.st.com/en/embedded-software/x-linux-ai.html) | 257x257x3 | Int8 (tflite)| 88.6% | 59.33% |
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- | [Deeplabv3 MobileNetv2 ASPPv1 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1.h5) | 512x512x3 | Float | 93.29% | 73.44% |
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- | [DeepLabv3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8.tflite) | 512x512x3 | Int8 (tflite) | 91.3% | 67.32% |
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- | [DDeepLabv3 MobileNetv2 ASPPv1 mixed precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8_f32.tflite) | 512x512x3 | Int8/Float (tflite)| 92.83% | 71.93% |
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- | [DeepLabv3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_qdq_int8.onnx) | 512x512x3 | Int8 (onnx) | 93.15%| 72.39% |
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  ### Accuracy with Person COCO 2017 + PASCAL VOC 2012
@@ -151,12 +151,12 @@ Moreover, IoU are averaged on all classes including background.
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  | Models Description | Resolution | Format | Accuracy (%) | average IoU |
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  |--------------------------------------------|-----------|---------------|--------------|-------------|
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- | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2.h5) | 256x256x3 | TensorFlow | 94.65 % | 76.96 % |
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- | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | 256x256x | ONNX | 94.57 % | 76.62 % |
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- | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2.h5) | 320x320x3 | TensorFlow | 95.16 % | 79.04 % |
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- | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) | 320x320x3 | ONNX | 94.98 % | 78.35 % |
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- | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2.h5) | 416x416x3 | TensorFlow | 95.48 % | 80.62 % |
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- | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | 416x416x3 | ONNX | 95.44 % | 80.36 % |
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  Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
 
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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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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 256x256x3 | STM32N6 | 2253.5 | 0.0 | 1001.25 | 10.0.0 | 2.0.0 |
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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) |person COCO 2017 + PASCAL VOC 2012 | Int8 | 320x320x3 | STM32N6 | 2446.0 | 0.0 | 1000.41 | 10.0.0 | 2.0.0 |
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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 416x416x3 | STM32N6 | 2743.5 | 2028.0 | 2721.19 | 10.0.0 | 2.0.0 |
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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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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 27.36 | 36.54 | 10.0.0 | 2.0.0 |
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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 44.99 | 22.22 | 10.0.0 | 2.0.0 |
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+ | [DeepLabv3 MobileNetv2 ASPPv2](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | person COCO 2017 + PASCAL VOC 2012 | Int8 | 416x416x3 | STM32N6570-DK | NPU/MCU | 191.91 | 5.21 | 10.0.0 | 2.0.0 |
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  ### Reference **MPU** inference time based on COCO 2017 + PASCAL VOC 2012 segmentation dataset 21 classes and a derivative person dataset from it (see Accuracy for details on dataset)
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  | Model | Dataset | Format | Resolution | Quantization | Board| Execution Engine | Frequency | Inference time (ms) | %NPU | %GPU | %CPU | X-LINUX-AI version |Framework |
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  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------|------------|----------------|-------------------|------------------|-----------|---------------------|-------|--------|------|--------------------|-----------------------|
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  | [DeepLabV3 per tensor (no ASPP)](https://www.st.com/en/embedded-software/x-linux-ai.html) | COCO 2017 + PASCAL VOC 2012 | Int8 | 257x257x3 | per-tensor | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 52.75 | 99.2 | 0.80 | 0 | v5.1.0 | OpenVX | | | | | v5.1.0
93
+ | [DeepLabV3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8.tflite) | COCO 2017 + PASCAL VOC 2012 | Int8 (tflite) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 806.12 | 8.73| 91.27 | 0 | v5.1.0 | OpenVX |
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+ | [DeepLabV3 MobileNetv2 ASPPv1 mixed precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8_f32.tflite) | COCO 2017 + PASCAL VOC 2012 | Int8 & float32 (tflite) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 894.56 | 7.67 | 92.33 | 0 | v5.1.0 | OpenVX |
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+ | [DeepLabV3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_qdq_int8.onnx) | COCO 2017 + PASCAL VOC 2012 | Int8 (onnx) | 512x512x3 | per-channel ** | STM32MP257F-DK2 | NPU/GPU | 1500 MHz | 729.62 | 3.0 | 97.0 | 0 | v5.1.0| OpenVX |
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  - **DeepLabV3 per tensor**:
98
  This model, which does not include ASPP (Atrous Spatial Pyramid Pooling), was downloaded from the TensorFlow DeepLabV3 page on [Kaggle](https://www.kaggle.com/models/tensorflow/deeplabv3/).
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  - **The onnx DeepLabv3 MobileNetv2 ASPPv1 per channel**:
101
+ The quantized TFLite model is derived from the DeepLabV3 float precision model. The ONNX quantized model is obtained by quantizing the DeepLabV3 float model using the [deeplab_v3_mobilenetv2_05_16_512_asppv1_onnx_config](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_onnx_config.yaml) YAML file.
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  **Note:** These results were obtained using the exact YAML files mentioned above and a specific quantization set containing 4 images from the PASCAL VOC dataset with the following IDs:
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  - 2008_004804
 
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  | Model Description | Resolution | Format | Accuracy | Averaed IoU |
140
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|------------|----------|--------------|
141
  | [DeepLabV3 per tensor (no ASPP)](https://www.st.com/en/embedded-software/x-linux-ai.html) | 257x257x3 | Int8 (tflite)| 88.6% | 59.33% |
142
+ | [Deeplabv3 MobileNetv2 ASPPv1 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1.h5) | 512x512x3 | Float | 93.29% | 73.44% |
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+ | [DeepLabv3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8.tflite) | 512x512x3 | Int8 (tflite) | 91.3% | 67.32% |
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+ | [DDeepLabv3 MobileNetv2 ASPPv1 mixed precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_int8_f32.tflite) | 512x512x3 | Int8/Float (tflite)| 92.83% | 71.93% |
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+ | [DeepLabv3 MobileNetv2 ASPPv1 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_512/deeplab_v3_mobilenetv2_05_16_512_asppv1_qdq_int8.onnx) | 512x512x3 | Int8 (onnx) | 93.15%| 72.39% |
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  ### Accuracy with Person COCO 2017 + PASCAL VOC 2012
 
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  | Models Description | Resolution | Format | Accuracy (%) | average IoU |
153
  |--------------------------------------------|-----------|---------------|--------------|-------------|
154
+ | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2.h5) | 256x256x3 | TensorFlow | 94.65 % | 76.96 % |
155
+ | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_256/deeplab_v3_mobilenetv2_05_16_256_asppv2_qdq_int8.onnx) | 256x256x | ONNX | 94.57 % | 76.62 % |
156
+ | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2.h5) | 320x320x3 | TensorFlow | 95.16 % | 79.04 % |
157
+ | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_320/deeplab_v3_mobilenetv2_05_16_320_asppv2_qdq_int8.onnx) | 320x320x3 | ONNX | 94.98 % | 78.35 % |
158
+ | [Deeplabv3 MobileNetv2 ASPPv2 float precision](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2.h5) | 416x416x3 | TensorFlow | 95.48 % | 80.62 % |
159
+ | [DeepLabv3 MobileNetv2 ASPPv2 per channel](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/semantic_segmentation/deeplab_v3/ST_pretrainedmodel_public_dataset/person_coco_2017_pascal_voc_2012/deeplab_v3_mobilenetv2_05_16_416/deeplab_v3_mobilenetv2_05_16_416_asppv2_qdq_int8.onnx) | 416x416x3 | ONNX | 95.44 % | 80.36 % |
160
 
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  Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)