Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
MiDaS v2.1 is a robust model for monocular depth estimation.
It calculates the relative distance of objects from the camera and is reported to perform well across various scenarios.
We implement here the Small version of the model, which is optimized for use on edge devices.
Developed by: René Ranftl et al.
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Photo by James Ranieri from
Training Details
Training Data
The model was trained on a diverse set of data taken from the ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, ApolloScape, BlendedMVS, and IRS datasets.
Training on depth data of different modalities was enabled by multi-objective optimization.
Testing Details
Metrics
Test metrics were evaluated on various previously unseen datasets to demonstrate the model's generalizability:
DIW dataset test split,
ETH3D dataset where ground truth is available,
Sintel dataset where ground truth is available,
KITTI dataset validation split for depth estimation and the Eigen test split,
NYU dataset test split,
TUM dataset subset of humans in indoor environments.
The authors used metrics aliged with the dataset ground truth:
Weighted Human Disagreement Rate (WHDR) For DIW,
Mean Absolute Relative Error (AbsRel) for ETH3D and Sintel,
Percentage of pixels with δ>1.25 for KITTI, NYU, and TUM.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 models were quantized to int8 using the HubAI General dataset.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
while pipeline.isRuning():
parser_output: Map2D = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
