metadata
license: apache-2.0
pipeline_tag: depth-estimation
FastDepth
Use case : Depth Estimation
Model description
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, which builds upon a MobileNetV1 based feature extractor and a fast decoder.
Although the original training dataset is not explicitly provided, it is most likely NYU Depth V2, a standard benchmark dataset for indoor depth estimation.
Network information
| Network Information | Value |
|---|---|
| Framework | TensorFlowLite |
| Quantization | int8 |
| Provenance | PINTO Model Zoo #146 |
| Paper | Link to Paper |
The models are quantized using tensorflow lite converter.
Network inputs / outputs
| Input Shape | Description |
|---|---|
| (1, H, W, 3) | Single RGB image (int8) |
| Output Shape | Description |
|---|---|
| (1, H, W, 1) | Single-channel depth prediction (int8) |
Recommended platforms
| Platform | Supported | Recommended |
|---|---|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32H7 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [x] | [x] |
| STM32N6 | [x] | [x] |
Performances
Metrics
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 | NYU depth v2 | Int8 | 224x224x3 | STM32N6 | 2728.5 | 0 | 1347.97 | 3.0.0 |
| Fast Depth | NYU depth v2 | Int8 | 256x256x3 | STM32N6 | 2688 | 1024 | 1354.09 | 3.0.0 |
| Fast Depth | NYU depth v2 | Int8 | 320x320x3 | STM32N6 | 2800 | 2800 | 1376.78 | 3.0.0 |
Reference NPU inference time
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|
| Fast Depth | NYU depth v2 | Int8 | 224x224x3 | STM32N6570-DK | NPU/MCU | 24.49 | 40.83 | 3.0.0 |
| Fast Depth | NYU depth v2 | Int8 | 256x256x3 | STM32N6570-DK | NPU/MCU | 75.01 | 13.33 | 3.0.0 |
| Fast Depth | NYU depth v2 | Int8 | 320x320x3 | STM32N6570-DK | NPU/MCU | 477.93 | 2.09 | 3.0.0 |
Please refer to the stm32ai-modelzoo-services GitHub here