CV / YOLO26
YOLO26
Jetson Orin NX
YOLO26n/s/m COCO object detection on NVIDIA Jetson through Ultralytics and on reComputer R Series with Hailo-8 or Hailo-10H through HailoRT.
112 downloads
Size
16GB diskMemory
4GB+Precision
JetPack 5/6/7 / Hailo HEFChoose the device you're using, the set up guide and documentation will update accordingly.
Getting Started
Deploy
python3 -m pip install -U jetson-examples && reComputer run yolo26REST API
Use the REST API to run inference. Copy the commands below.
Curl
curl -X POST "http://<Board_IP>:8000/api/models/yolo26n/predict" \
-F "file=@test.jpg"Python
import requests
with open("test.jpg", "rb") as image_file:
response = requests.post(
"http://<Board_IP>:8000/api/models/yolo26n/predict",
files={"file": image_file},
timeout=30,
)
print(response.json())Model Details
reComputer JetsonJetson One-Command Deployment
Run the command in the Getting Started block directly on a Jetson or reComputer Jetson device.
bash
python3 -m pip install -U jetson-examples && reComputer run <demo-name>The command installs or upgrades the jetson-examples Python package from PyPI, then runs the selected demo script through the reComputer CLI.
Requirements
- NVIDIA Jetson or reComputer Jetson device.
- JetPack/L4T version supported by the selected demo.
- Internet access for PyPI packages, Docker images, model weights, or external assets used by the demo.
- Docker and NVIDIA container runtime when the selected demo uses containers.
- Enough disk and memory for the selected demo. The model card lists the main resource requirement when it is known from the demo configuration.
Workflow
- Copy the deployment command from the Run dialog or detail page.
- Run it in a terminal on the target Jetson device.
- Follow any prompts printed by the demo script.
- Open the URL shown in the terminal if the demo starts a web UI or API service.
Notes
reComputer run <demo-name>runs the demo'sinit.shfirst when available, then startsrun.sh.- To inspect all packaged demos on the device, run:
bash
reComputer list- To remove demo data when a cleanup script exists, run:
bash
reComputer clean <demo-name>Inputs and Outputs
Input: image, video, or camera source. Output: COCO 80-class detections through Ultralytics on Jetson or HailoRT on the R Series.