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license: apache-2.0 |
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pipeline_tag: depth-estimation |
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--- |
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# FastDepth |
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## **Use case** : `Depth Estimation` |
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# 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 | |
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|-------------------------|----------------------------------------------------------------| |
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| Framework | TensorFlowLite | |
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| Quantization | int8 | |
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| Provenance | PINTO Model Zoo #146 | |
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| 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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## Network inputs / outputs |
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| Input Shape | Description | |
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|--------------|-----------------------------------------------------| |
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| (1, H, W, 3) | Single RGB image (int8) | |
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| Output Shape | Description | |
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|---------------|-------------------------------------------------| |
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| (1, H, W, 1) | Single-channel depth prediction (int8)| |
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## Recommended platforms |
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| Platform | Supported | Recommended | |
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|----------|--------|-----------| |
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| STM32L0 |[]|[]| |
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| STM32L4 |[]|[]| |
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| STM32U5 |[]|[]| |
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| STM32H7 |[]|[]| |
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| STM32MP1 |[]|[]| |
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| STM32MP2 |[x]|[x]| |
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| 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. |
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### Reference **NPU** memory footprint |
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| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version | |
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|------------|---------------|----------|------------|-----------|--------------|--------------|---------------|-----------------------| |
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| [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 | |
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| [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 | |
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| [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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### Reference **NPU** inference time |
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| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
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|------------|---------------|----------|------------|------------------|------------------|---------------------|-------------|-------------------------| |
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| [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 | |
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| [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 | |
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| [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) |
