Step 1: setup nvidia jetson
refer to this guide:
https://github.com/saadwazir/nvidia-jetson_deepstream_yolo_pytorch_and_torchvision_setup_guide/

step 2: clone this repo and install requirements

pip install -r requirements.txt

step 3:

pip install git+https://github.com/openai/CLIP.git

step 4: Download model weights in PyTorch format from here : https://github.com/CASIA-IVA-Lab/FastSAM#model-checkpoints

step 5: convert to TensorRT. Create a new Python script and enter the following code:

from ultralytics import YOLO

model = YOLO('FastSAM-s.pt')  # load a custom trained
# TensorRT FP32 export
# model.export(format='engine', device='0', imgsz=640)

# TensorRT FP16 export
model.export(format='engine', device='0', imgsz=640, half=True)

Save and execute the file.

step 6: Run (using webcam)

python3 Inference_video.py --model_path FastSAM-s.engine --img_path /dev/video2 --imgsz 640

"/dev/video2" is the path of webcam in Linux.

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This repo contains code and instructions from these repo's
https://github.com/yuyoujiang/FastSAM-with-video-on-NVIDIA-Jetson
https://github.com/CASIA-IVA-Lab/FastSAM