Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task.
This task is designed to segment any object within an image based on various possible user interaction prompts.
FastSAM significantly reduces computational demands while maintaining competitive performance, making it a practical choice for a variety of vision tasks.
We implement here the s version of the model.
Developed by: Ultralytics
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
The model was trained on only 2%
of the dataset. Segment Anything 1 Billion (SA-1B) is a dataset designed for training general-purpose object segmentation models from open world images.
Testing Details
Testing details are only available for the bigger FastSAM x variant.
Technical Specifications
Input/Output Details
Input:
Name: image
Info: NCHW BGR un-normalized image
Output:
Name: Multiple (please consult NN archive config.json)
Info: Unprocessed outputs of a multitude of detections, masks and protos
Model Architecture
Backbone: CSPDarknet53
Head: Anchor-free object segmentation head from YOLOv8 seg model (pruned of concatenation)
* 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 a full 128-image 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.
FastSAMParser that outputs message (mask of each of the segmented objects).
Get parsed output(s):
while pipeline.isRuning():
parser_output: SegmentationMask = parser_output_queue.get()
Notes
CLIP textual and visual models are used for text prompting, to determine image segments
corresponding to the given text prompt. Both these models are encoders, the difference between them is
that textual CLIP is used to encode an input text, whereas visual CLIP is used to encode images (or segments of images).
To learn more about various prompting methods, please refer to the .
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:
