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Segment-Anything-Model

Image Segmentation
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ONNX
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---
library_name: pytorch
license: apache-2.0
tags:
- foundation
- android
pipeline_tag: image-segmentation
---
![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/sam/web-assets/model_demo.png)
# Segment-Anything-Model: Optimized for Mobile Deployment
## High-quality segmentation mask generation around any object in an image with simple input prompt
Transformer based encoder-decoder where prompts specify what to segment in an image thereby allowing segmentation without the need for additional training. The image encoder generates embeddings and the lightweight decoder operates on the embeddings for point and mask based image segmentation.
This model is an implementation of Segment-Anything-Model found [here](https://github.com/facebookresearch/segment-anything).
This repository provides scripts to run Segment-Anything-Model on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/sam).
### Model Details
- **Model Type:** Semantic segmentation
- **Model Stats:**
- Model checkpoint: vit_l
- Input resolution: 720p (720x1280)
- Number of parameters (SAMDecoder): 5.11M
- Model size (SAMDecoder): 19.6 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| SAMDecoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 8.448 ms | 1 - 57 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMDecoder.onnx) |
| SAMDecoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 5.889 ms | 6 - 68 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMDecoder.onnx) |
| SAMDecoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 5.73 ms | 4 - 55 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMDecoder.onnx) |
| SAMDecoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.67 ms | 11 - 11 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMDecoder.onnx) |
| SAMEncoderPart1 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 228.979 ms | 12 - 181 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart1.onnx) |
| SAMEncoderPart1 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 161.859 ms | 36 - 838 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart1.onnx) |
| SAMEncoderPart1 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 158.396 ms | 35 - 789 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart1.onnx) |
| SAMEncoderPart1 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 231.597 ms | 43 - 43 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart1.onnx) |
| SAMEncoderPart2 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 781.713 ms | 12 - 147 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart2.onnx) |
| SAMEncoderPart2 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 567.288 ms | 36 - 736 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart2.onnx) |
| SAMEncoderPart2 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 531.384 ms | 12 - 686 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart2.onnx) |
| SAMEncoderPart2 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 736.185 ms | 33 - 33 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart2.onnx) |
| SAMEncoderPart3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 779.664 ms | 12 - 159 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart3.onnx) |
| SAMEncoderPart3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 576.302 ms | 22 - 724 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart3.onnx) |
| SAMEncoderPart3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 530.988 ms | 12 - 686 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart3.onnx) |
| SAMEncoderPart3 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 729.557 ms | 33 - 33 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart3.onnx) |
| SAMEncoderPart4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 770.123 ms | 12 - 151 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart4.onnx) |
| SAMEncoderPart4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 569.238 ms | 24 - 722 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart4.onnx) |
| SAMEncoderPart4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 478.143 ms | 24 - 699 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart4.onnx) |
| SAMEncoderPart4 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 730.872 ms | 33 - 33 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart4.onnx) |
| SAMEncoderPart5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 772.375 ms | 0 - 133 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart5.onnx) |
| SAMEncoderPart5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 568.921 ms | 24 - 720 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart5.onnx) |
| SAMEncoderPart5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 481.0 ms | 12 - 686 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart5.onnx) |
| SAMEncoderPart5 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 737.772 ms | 33 - 33 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart5.onnx) |
| SAMEncoderPart6 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 768.673 ms | 12 - 148 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart6.onnx) |
| SAMEncoderPart6 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 568.747 ms | 22 - 726 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart6.onnx) |
| SAMEncoderPart6 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 531.699 ms | 12 - 686 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart6.onnx) |
| SAMEncoderPart6 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 727.465 ms | 33 - 33 MB | FP16 | NPU | [Segment-Anything-Model.onnx](https://huggingface.co/qualcomm/Segment-Anything-Model/blob/main/SAMEncoderPart6.onnx) |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[sam]"
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.sam.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.sam.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.sam.export
```
```
Profiling Results
------------------------------------------------------------
SAMDecoder
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 8.4
Estimated peak memory usage (MB): [1, 57]
Total # Ops : 868
Compute Unit(s) : NPU (868 ops)
------------------------------------------------------------
SAMEncoderPart1
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 229.0
Estimated peak memory usage (MB): [12, 181]
Total # Ops : 623
Compute Unit(s) : NPU (623 ops)
------------------------------------------------------------
SAMEncoderPart2
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 781.7
Estimated peak memory usage (MB): [12, 147]
Total # Ops : 610
Compute Unit(s) : NPU (610 ops)
------------------------------------------------------------
SAMEncoderPart3
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 779.7
Estimated peak memory usage (MB): [12, 159]
Total # Ops : 610
Compute Unit(s) : NPU (610 ops)
------------------------------------------------------------
SAMEncoderPart4
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 770.1
Estimated peak memory usage (MB): [12, 151]
Total # Ops : 610
Compute Unit(s) : NPU (610 ops)
------------------------------------------------------------
SAMEncoderPart5
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 772.4
Estimated peak memory usage (MB): [0, 133]
Total # Ops : 610
Compute Unit(s) : NPU (610 ops)
------------------------------------------------------------
SAMEncoderPart6
Device : Samsung Galaxy S23 (13)
Runtime : ONNX
Estimated inference time (ms) : 768.7
Estimated peak memory usage (MB): [12, 148]
Total # Ops : 610
Compute Unit(s) : NPU (610 ops)
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/sam/qai_hub_models/models/Segment-Anything-Model/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.sam import Model
# Load the model
model = Model.from_pretrained()
decoder_model = model.decoder
encoder_splits[0]_model = model.encoder_splits[0]
encoder_splits[1]_model = model.encoder_splits[1]
encoder_splits[2]_model = model.encoder_splits[2]
encoder_splits[3]_model = model.encoder_splits[3]
encoder_splits[4]_model = model.encoder_splits[4]
encoder_splits[5]_model = model.encoder_splits[5]
# Device
device = hub.Device("Samsung Galaxy S23")
# Trace model
decoder_input_shape = decoder_model.get_input_spec()
decoder_sample_inputs = decoder_model.sample_inputs()
traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()])
# Compile model on a specific device
decoder_compile_job = hub.submit_compile_job(
model=traced_decoder_model ,
device=device,
input_specs=decoder_model.get_input_spec(),
)
# Get target model to run on-device
decoder_target_model = decoder_compile_job.get_target_model()
# Trace model
encoder_splits[0]_input_shape = encoder_splits[0]_model.get_input_spec()
encoder_splits[0]_sample_inputs = encoder_splits[0]_model.sample_inputs()
traced_encoder_splits[0]_model = torch.jit.trace(encoder_splits[0]_model, [torch.tensor(data[0]) for _, data in encoder_splits[0]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[0]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[0]_model ,
device=device,
input_specs=encoder_splits[0]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[0]_target_model = encoder_splits[0]_compile_job.get_target_model()
# Trace model
encoder_splits[1]_input_shape = encoder_splits[1]_model.get_input_spec()
encoder_splits[1]_sample_inputs = encoder_splits[1]_model.sample_inputs()
traced_encoder_splits[1]_model = torch.jit.trace(encoder_splits[1]_model, [torch.tensor(data[0]) for _, data in encoder_splits[1]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[1]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[1]_model ,
device=device,
input_specs=encoder_splits[1]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[1]_target_model = encoder_splits[1]_compile_job.get_target_model()
# Trace model
encoder_splits[2]_input_shape = encoder_splits[2]_model.get_input_spec()
encoder_splits[2]_sample_inputs = encoder_splits[2]_model.sample_inputs()
traced_encoder_splits[2]_model = torch.jit.trace(encoder_splits[2]_model, [torch.tensor(data[0]) for _, data in encoder_splits[2]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[2]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[2]_model ,
device=device,
input_specs=encoder_splits[2]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[2]_target_model = encoder_splits[2]_compile_job.get_target_model()
# Trace model
encoder_splits[3]_input_shape = encoder_splits[3]_model.get_input_spec()
encoder_splits[3]_sample_inputs = encoder_splits[3]_model.sample_inputs()
traced_encoder_splits[3]_model = torch.jit.trace(encoder_splits[3]_model, [torch.tensor(data[0]) for _, data in encoder_splits[3]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[3]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[3]_model ,
device=device,
input_specs=encoder_splits[3]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[3]_target_model = encoder_splits[3]_compile_job.get_target_model()
# Trace model
encoder_splits[4]_input_shape = encoder_splits[4]_model.get_input_spec()
encoder_splits[4]_sample_inputs = encoder_splits[4]_model.sample_inputs()
traced_encoder_splits[4]_model = torch.jit.trace(encoder_splits[4]_model, [torch.tensor(data[0]) for _, data in encoder_splits[4]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[4]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[4]_model ,
device=device,
input_specs=encoder_splits[4]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[4]_target_model = encoder_splits[4]_compile_job.get_target_model()
# Trace model
encoder_splits[5]_input_shape = encoder_splits[5]_model.get_input_spec()
encoder_splits[5]_sample_inputs = encoder_splits[5]_model.sample_inputs()
traced_encoder_splits[5]_model = torch.jit.trace(encoder_splits[5]_model, [torch.tensor(data[0]) for _, data in encoder_splits[5]_sample_inputs.items()])
# Compile model on a specific device
encoder_splits[5]_compile_job = hub.submit_compile_job(
model=traced_encoder_splits[5]_model ,
device=device,
input_specs=encoder_splits[5]_model.get_input_spec(),
)
# Get target model to run on-device
encoder_splits[5]_target_model = encoder_splits[5]_compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
decoder_profile_job = hub.submit_profile_job(
model=decoder_target_model,
device=device,
)
encoder_splits[0]_profile_job = hub.submit_profile_job(
model=encoder_splits[0]_target_model,
device=device,
)
encoder_splits[1]_profile_job = hub.submit_profile_job(
model=encoder_splits[1]_target_model,
device=device,
)
encoder_splits[2]_profile_job = hub.submit_profile_job(
model=encoder_splits[2]_target_model,
device=device,
)
encoder_splits[3]_profile_job = hub.submit_profile_job(
model=encoder_splits[3]_target_model,
device=device,
)
encoder_splits[4]_profile_job = hub.submit_profile_job(
model=encoder_splits[4]_target_model,
device=device,
)
encoder_splits[5]_profile_job = hub.submit_profile_job(
model=encoder_splits[5]_target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
decoder_input_data = decoder_model.sample_inputs()
decoder_inference_job = hub.submit_inference_job(
model=decoder_target_model,
device=device,
inputs=decoder_input_data,
)
decoder_inference_job.download_output_data()
encoder_splits[0]_input_data = encoder_splits[0]_model.sample_inputs()
encoder_splits[0]_inference_job = hub.submit_inference_job(
model=encoder_splits[0]_target_model,
device=device,
inputs=encoder_splits[0]_input_data,
)
encoder_splits[0]_inference_job.download_output_data()
encoder_splits[1]_input_data = encoder_splits[1]_model.sample_inputs()
encoder_splits[1]_inference_job = hub.submit_inference_job(
model=encoder_splits[1]_target_model,
device=device,
inputs=encoder_splits[1]_input_data,
)
encoder_splits[1]_inference_job.download_output_data()
encoder_splits[2]_input_data = encoder_splits[2]_model.sample_inputs()
encoder_splits[2]_inference_job = hub.submit_inference_job(
model=encoder_splits[2]_target_model,
device=device,
inputs=encoder_splits[2]_input_data,
)
encoder_splits[2]_inference_job.download_output_data()
encoder_splits[3]_input_data = encoder_splits[3]_model.sample_inputs()
encoder_splits[3]_inference_job = hub.submit_inference_job(
model=encoder_splits[3]_target_model,
device=device,
inputs=encoder_splits[3]_input_data,
)
encoder_splits[3]_inference_job.download_output_data()
encoder_splits[4]_input_data = encoder_splits[4]_model.sample_inputs()
encoder_splits[4]_inference_job = hub.submit_inference_job(
model=encoder_splits[4]_target_model,
device=device,
inputs=encoder_splits[4]_input_data,
)
encoder_splits[4]_inference_job.download_output_data()
encoder_splits[5]_input_data = encoder_splits[5]_model.sample_inputs()
encoder_splits[5]_inference_job = hub.submit_inference_job(
model=encoder_splits[5]_target_model,
device=device,
inputs=encoder_splits[5]_input_data,
)
encoder_splits[5]_inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.sam.demo --on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.sam.demo -- --on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on Segment-Anything-Model's performance across various devices [here](https://aihub.qualcomm.com/models/sam).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Segment-Anything-Model can be found
[here](https://github.com/facebookresearch/segment-anything/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Segment Anything](https://arxiv.org/abs/2304.02643)
* [Source Model Implementation](https://github.com/facebookresearch/segment-anything)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).