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Model Details
Model Description
EfficientNet-Lite is a mobile-friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. It can classify the image into one of 1000 classes.
Developed by: Google
Shared by:
Model type: Computer vision
License:
Resources for more information:
Training Details
Training Data
EfficientNet-Lite model is trained on at resolution 300x300.
For more information about training data check the .
Testing Details
Metrics
Metric
Value
Top-1 Acc.
81.50%
For more information please check the .
Technical Specifications
Input/Output Details
Input:
Name: images
Info: NCHW BGR 0-255 image.
Output:
Name: Sofmax
Info: Softmaxed scores for 1000 classes.
Model Architecture
The EfficientNet-Lite network consists of:
Stem: Initial layer with a standard convolution followed by a batch normalization and a ReLU6 activation.
Body: Consists of a series of MBConv blocks with different configurations. Each block includes depthwise separable convolutions and squeeze-and-excitation layers.
Head: Includes a final convolutional block, followed by a global average pooling layer.
Please consult the and 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 custom dataset.
This was created by taking 1000-image subset of 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.
ClassificationParser that outputs message (detected classes and scores).
Get parsed output(s):
while pipeline.isRuning():
parser_output: Classifications = 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:
