Comments (3)
Hey @cds4488, to use your custom dataset, you have to train YOLOv8 as normal. Then, pass the new model path to the launch file using the model
parameter.
For instance, you have the following example. Replace the coco128.yaml with you YAML file and choose the base YOLOv8 model.
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
Btw, there is no RGBD dataset, I am using the depth images along with the bounding boxes to create the 3D boxes.
from yolov8_ros.
@mgonzs13 Thanks for your answering, I will try later!
from yolov8_ros.
Hey @cds4488, how is this going?
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Related Issues (20)
- ZeroDivision error HOT 7
- How to Deploy a Single Instance of yolov8_ros Node for Multiple Camera Detections HOT 7
- Could not transform: "base_link" passed to lookupTransform argument target_frame does not exist HOT 2
- Train HOT 6
- how can i convert 3d coordinate to real word coordinate? HOT 2
- Issue when trying to launch yolov8 - using realsense camera HOT 10
- base_link passed to lookupTransform argument target_frame does not exist HOT 2
- detect_3d_node.py: on_detections not being called! HOT 4
- 3D object detection Transformation issue HOT 15
- How to test this project for ROS noetic HOT 2
- UBUNUT & ROS VERSION HOT 1
- Lifecycle nodes HOT 2
- No image in Rviz2 using realsense camera! HOT 10
- missing pip module lap HOT 1
- custom yolo pretrained problem. HOT 5
- ros2 version HOT 10
- Encoding error by ultralytics prediction HOT 6
- distance to object HOT 5
- Operating system HOT 14
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