Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
Real Time (RT) Super Resolution is a robust light-weight model for recovering image resolution.
It requires no guide image to perform the enhancement, thus falling under the category of single-image super-resolution (SISR) rather than guided super-resolution (GSR).
The model is based on framework with a few modifications in the objective function.
Developed by: Rishik Mourya
Model type: Computer Vision
License:
Resources for more information:
Photo by Kampus Production from
Training Details
Training Data
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Testing Details
Metrics
No data available.
Technical Specifications
Input/Output Details
Input:
Name: image
Info: NCHW BGR un-normalized image
Output:
Name: enhanced_image
Info: NCHW RGB 4x upscaled image
Model Architecture
architecture consisting of Residual-in-Residual Dense Block(s).
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 version of the model was quantized using a 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.
ImageOutputParser that outputs message (upscaled image).
Get parsed output(s):
while pipeline.isRuning():
parser_output: dai.ImgFrame = 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:
