Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
ESRGAN: Enhanced Super-Resolution Generative Adversarial Network enhances the resolution of low-quality images by generating high-quality, detailed versions through a combination of generative adversarial networks (GANs) and a perceptual loss function.
Developed by: Xintao et al.
Shared by:
Model type: Computer vision
License:
Resources for more information:
Training Details
Training Data
The model was trained on and datasets. The first one contains 800 images and the second one 2650 images.
For more information about training data check the .
Testing Details
Metrics
There are qualitative results described in the . Please check it out.
Technical Specifications
Input/Output Details
Input:
Name: input
Info: NCHW BGR 0-255 image.
Output:
Name: output
Info: Upscaled enhanced image.
Model Architecture
ESRGAN is composed of Residual-in-Residual Dense Block (RRDB), Generator Network, Discriminator Network, and Perceptual Loss.
Please consult the for more information on model architecture.
* 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 (enhanced 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:
