| | --- |
| | license: apache-2.0 |
| | pipeline_tag: depth-estimation |
| | --- |
| | # FastDepth |
| |
|
| | ## **Use case** : `Depth Estimation` |
| |
|
| | # Model description |
| |
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| | FastDepth is a lightweight encoder-decoder network designed for real-time monocular depth estimation, optimized for edge devices. This implementation is based on model number 146 from [PINTO's model zoo](https://github.com/PINTO0309/PINTO_model_zoo), which builds upon a MobileNetV1 based feature extractor and a fast decoder. |
| |
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| | Although the original training dataset is not explicitly provided, it is most likely **NYU Depth V2**, a standard benchmark dataset for indoor depth estimation. |
| |
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| | ## Network information |
| |
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| |
|
| | | Network Information | Value | |
| | |-------------------------|----------------------------------------------------------------| |
| | | Framework | TensorFlowLite | |
| | | Quantization | int8 | |
| | | Provenance | PINTO Model Zoo #146 | |
| | | Paper | [Link to Paper](https://arxiv.org/pdf/1903.03273)| |
| |
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| | The models are quantized using tensorflow lite converter. |
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| |
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| | ## Network inputs / outputs |
| |
|
| | | Input Shape | Description | |
| | |--------------|-----------------------------------------------------| |
| | | (1, H, W, 3) | Single RGB image (int8) | |
| |
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| | | Output Shape | Description | |
| | |---------------|-------------------------------------------------| |
| | | (1, H, W, 1) | Single-channel depth prediction (int8)| |
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| |
|
| | ## Recommended platforms |
| |
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| |
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| | | Platform | Supported | Recommended | |
| | |----------|--------|-----------| |
| | | STM32L0 |[]|[]| |
| | | STM32L4 |[]|[]| |
| | | STM32U5 |[]|[]| |
| | | STM32H7 |[]|[]| |
| | | STM32MP1 |[]|[]| |
| | | STM32MP2 |[x]|[x]| |
| | | STM32N6 |[x]|[x]| |
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| | # Performances |
| |
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| | ## Metrics |
| |
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| | Measures are done with default STEdgeAI Core version configuration with enabled input / output allocated option. |
| |
|
| | ### Reference **NPU** memory footprint |
| |
|
| | | Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
| | |------------|---------------|----------|------------|-----------|--------------|--------------|---------------|-----------------------| |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_224/fastdepth_224_int8.tflite) | NYU depth v2 | Int8 | 224x224x3 | STM32N6 | 2728.5 | 0 | 1347.97 | 3.0.0 | |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_256/fastdepth_256_int8.tflite) | NYU depth v2 | Int8 | 256x256x3 | STM32N6 | 2688 | 1024 | 1354.09 | 3.0.0 | |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_320/fastdepth_320_int8.tflite) | NYU depth v2 | Int8 | 320x320x3 | STM32N6 | 2800 | 2800 | 1376.78 | 3.0.0 | |
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| |
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| | ### Reference **NPU** inference time |
| |
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| |
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| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |------------|---------------|----------|------------|------------------|------------------|---------------------|-------------|-------------------------| |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_224/fastdepth_224_int8.tflite) | NYU depth v2 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 24.49 | 40.83 | 3.0.0 | |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_256/fastdepth_256_int8.tflite) | NYU depth v2 | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 75.01 | 13.33 | 3.0.0 | |
| | | [Fast Depth](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/depth_estimation/fastdepth/Public_pretrainedmodel_public_dataset/nyu_depthv2/fastdepth_320/fastdepth_320_int8.tflite) | NYU depth v2 | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 477.93 | 2.09 | 3.0.0 | |
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| | Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
