
Midas is a deep learning-based monocular depth estimation model that accurately predicts scene depth from a single RGB image without relying on stereo vision or depth sensors. By integrating a hybrid CNN-Transformer architecture and pretraining on diverse datasets (e.g., MegaDepth, KITTI), it achieves strong cross-scene generalization, adapting to complex lighting, occlusions, and varied environments (indoor/outdoor). The model supports dynamic resolution inputs (down to 256x256 pixels) while preserving detail perception, with optimized computational efficiency for real-time performance and lightweight deployment on mobile/edge devices. It is widely used in autonomous driving (obstacle detection), AR/VR (3D reconstruction), and robotic navigation, significantly reducing hardware costs. Ongoing updates (e.g., Midas-v3) enhance small-object recognition and edge accuracy.
Source model
- Input shape: 1x3x256x256
- Number of parameters: 20.33M
- Model size: 82.17M
- Output shape: 1x1x256x256
The source model can be found here
When the user has fine-tuned the source model, the model conversion process must be performed again.
Users can refer to either of the following two methods to complete the model conversion:
Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.
Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.
The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.
For models in .bin
format, you can use either of the following two inference engines to run inference on Qualcomm chips:
Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation
Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation
Inference Example Code
The inference example code is implemented using the AidLite SDK.
Click Model & Code to download the model files and the inference code package. The file structure is as follows:
/model_farm_{model_name}_aidlite
|__ models # folder where model files are stored
|__ python # aidlite python model inference example
|__ cpp # aidlite cpp model inference example
|__ README.md