NanoSAM

👍 Usage - ⏱️ Performance - 🛠️ Setup - 🤸 Examples - 🏋️ Training

• 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also

NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.

NanoSAM is trained by distilling the MobileSAM image encoder on unlabeled images. For an introduction to knowledge distillation, we recommend checking out this tutorial.

👍 Usage

Using NanoSAM from Python looks like this

from nanosam.utils.predictor import Predictor

predictor = Predictor(
    image_encoder="data/resnet18_image_encoder.engine",
    mask_decoder="data/mobile_sam_mask_decoder.engine"
)

image = PIL.Image.open("dog.jpg")

predictor.set_image(image)

mask, _, _ = predictor.predict(np.array([[x, y]]), np.array([1]))

Notes

The point labels may be

Follow the instructions below for how to build the engine files.

⏱️ Performance

NanoSAM runs real-time on Jetson Orin Nano.

Notes

† The MobileSAM image encoder is optimized with FP32 precision because it produced erroneous results when built for FP16 precision with TensorRT. The NanoSAM image encoder is built with FP16 precision as we did not notice a significant accuracy degredation. Both pipelines use the same mask decoder which is built with FP32 precision. For all models, the accuracy reported uses the same model configuration used to measure latency.

‡ Accuracy is computed by prompting SAM with ground-truth object bounding box annotations from the COCO 2017 validation dataset. The IoU is then computed between the mask output of the SAM model for the object and the ground-truth COCO segmentation mask for the object. The mIoU is the average IoU over all objects in the COCO 2017 validation set matching the target object size (small, medium, large).

🛠️ Setup

NanoSAM is fairly easy to get started with.

1. Install the dependencies

   1. Install PyTorch

   2. Install torch2trt

   3. Install NVIDIA TensorRT

   4. (optional) Install TRTPose - For the pose example.

      bash git clone https://github.com/NVIDIA-AI-IOT/trt_pose cd trt_pose python3 setup.py develop --user

   5. (optional) Install the Transformers library - For the OWL ViT example.

      bash python3 -m pip install transformers

2. Install the NanoSAM Python package

   bash git clone https://github.com/NVIDIA-AI-IOT/nanosam cd nanosam python3 setup.py develop --user

3. Build the TensorRT engine for the mask decoder

   1. Export the MobileSAM mask decoder ONNX file (or download directly from here)

      bash python3 -m nanosam.tools.export_sam_mask_decoder_onnx \ --model-type=vit_t \ --checkpoint=assets/mobile_sam.pt \ --output=data/mobile_sam_mask_decoder.onnx

   2. Build the TensorRT engine

      bash trtexec \ --onnx=data/mobile_sam_mask_decoder.onnx \ --saveEngine=data/mobile_sam_mask_decoder.engine \ --minShapes=point_coords:1x1x2,point_labels:1x1 \ --optShapes=point_coords:1x1x2,point_labels:1x1 \ --maxShapes=point_coords:1x10x2,point_labels:1x10

      This assumes the mask decoder ONNX file is downloaded to data/mobile_sam_mask_decoder.onnx

Notes

    This command builds the engine to support up to 10 keypoints.  You can increase
    this limit as needed by specifying a different max shape.

1. Build the TensorRT engine for the NanoSAM image encoder

   1. Download the image encoder: resnet18_image_encoder.onnx

   2. Build the TensorRT engine

      bash trtexec \ --onnx=data/resnet18_image_encoder.onnx \ --saveEngine=data/resnet18_image_encoder.engine \ --fp16

2. Run the basic usage example

   python3 examples/basic_usage.py \ --image_encoder=data/resnet18_image_encoder.engine \ --mask_decoder=data/mobile_sam_mask_decoder.engine

   This outputs a result to data/basic_usage_out.jpg

That's it! From there, you can read the example code for examples on how to use NanoSAM with Python. Or try running the more advanced examples below.

🤸 Examples

NanoSAM can be applied in many creative ways.

Example 1 - Segment with bounding box

This example uses a known image with a fixed bounding box to control NanoSAM segmentation.

To run the example, call

python3 examples/basic_usage.py \
    --image_encoder="data/resnet18_image_encoder.engine" \
    --mask_decoder="data/mobile_sam_mask_decoder.engine"

Example 2 - Segment with bounding box (using OWL-ViT detections)

This example demonstrates using OWL-ViT to detect objects using a text prompt(s), and then segmenting these objects using NanoSAM.

To run the example, call

python3 examples/segment_from_owl.py \
    --prompt="A tree" \
    --image_encoder="data/resnet18_image_encoder.engine" \
    --mask_decoder="data/mobile_sam_mask_decoder.engine

Notes

• While OWL-ViT does not run real-time on Jetson Orin Nano (3sec/img), it is nice for experimentation as it allows you to detect a wide variety of objects. You could substitute any other real-time pre-trained object detector to take full advantage of NanoSAM's speed.

Example 3 - Segment with keypoints (offline using TRTPose detections)

This example demonstrates how to use human pose keypoints from TRTPose to control NanoSAM segmentation.

To run the example, call

python3 examples/segment_from_pose.py

This will save an output figure to data/segment_from_pose_out.png.

Example 4 - Segment with keypoints (online using TRTPose detections)

This example demonstrates how to use human pose to control segmentation on a live camera feed. This example requires an attached display and camera.

To run the example, call

python3 examples/demo_pose_tshirt.py

Example 5 - Segment and track (experimental)

This example demonstrates a rudimentary segmentation tracking with NanoSAM. This example requires an attached display and camera.

To run the example, call

python3 examples/demo_click_segment_track.py <image_encoder_engine> <mask_decoder_engine>

Once the example is running double click an object you want to track.

Notes

This tracking method is very simple and can get lost easily. It is intended to demonstrate creative ways you can use NanoSAM, but would likely be improved with more work.

🏋️ Training

You can train NanoSAM on a single GPU

1. Download and extract the COCO 2017 train images

   ```bash

   mkdir -p data/coco # uncomment if it doesn't exist

   mkdir -p data/coco cd data/coco wget http://images.cocodataset.org/zips/train2017.zip unzip train2017.zip cd ../.. ```

2. Build the MobileSAM image encoder (used as teacher model)

   1. Export to ONNX

      bash python3 -m nanosam.tools.export_sam_image_encoder_onnx \ --checkpoint="assets/mobile_sam.pt" \ --output="data/mobile_sam_image_encoder_bs16.onnx" \ --model_type=vit_t \ --batch_size=16

   2. Build the TensorRT engine with batch size 16

      bash trtexec \ --onnx=data/mobile_sam_image_encoder_bs16.onnx \ --shapes=image:16x3x1024x1024 \ --saveEngine=data/mobile_sam_image_encoder_bs16.engine

3. Train the NanoSAM image encoder by distilling MobileSAM

   bash python3 -m nanosam.tools.train \ --images=data/coco/train2017 \ --output_dir=data/models/resnet18 \ --model_name=resnet18 \ --teacher_image_encoder_engine=data/mobile_sam_image_encoder_bs16.engine \ --batch_size=16

Notes

Once training, visualizations of progress and checkpoints will be saved to
the specified output directory.  You can stop training and resume from the last
saved checkpoint if needed.

For a list of arguments, you can type

```bash
python3 -m nanosam.tools.train --help
```

1. Export the trained NanoSAM image encoder to ONNX

   bash python3 -m nanosam.tools.export_image_encoder_onnx \ --model_name=resnet18 \ --checkpoint="data/models/resnet18/checkpoint.pth" \ --output="data/resnet18_image_encoder.onnx"

You can then build the TensorRT engine as detailed in the getting started section.

🧐 Evaluation

You can reproduce the accuracy results above by evaluating against COCO ground truth masks

1. Download and extract the COCO 2017 validation set.

   ```bash

   mkdir -p data/coco # uncomment if it doesn't exist

   cd data/coco wget http://images.cocodataset.org/zips/val2017.zip wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip unzip val2017.zip unzip annotations_trainval2017.zip cd ../.. ```

2. Compute the IoU of NanoSAM mask predictions against the ground truth COCO mask annotation.

   ```bash python3 -m nanosam.tools.eval_coco \ --coco_root=data/coco/val2