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Model Details
Model Description
EfficientViT is a transformer-based model with novel multi-scale linear attention. Unlike previous state-of-the-art transformer-based models, it is built only by using lightweight and hardware-efficient operations. The model delivers better performance on edge devices.
Developed by:
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
The classification model was trained on dataset, actually on ImageNet-1K with random initialization (300 epochs + 20 warmup epochs) using supervised learning.
Testing Details
Metrics
The get more information about evaluation, please check .
Model
Resolution
ImageNet Top-1 Acc.
ImageNet Top-5 Acc.
EfficientViT-B1
224x224
79.39
94.35
Technical Specifications
Input/Output Details
Input:
Name: input
Info: NCHW BGR un-normalized image
Output:
Name: output
Info: The non-softmaxed values for 1000 classes.
Model Architecture
Backbone:
Composed of an input stem and four stages, each reducing spatial resolution while increasing channel depth.
EfficientViT modules are embedded in Stages 3 and 4 for efficient global and local feature extraction.
Downsampling is handled by MobileNet-like blocks (MBConv).
Head:
The head processes features from Stages 2, 3, and 4 using simple 1x1 convolutions and upsampling.
Features are fused through addition to reduce computational overhead.
Designed for simplicity and effectiveness, suitable for both segmentation and classification tasks.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
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:
