Instructions to use Mattewkang/Abstraction_experiment-camera-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Mattewkang/Abstraction_experiment-camera-models with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("Mattewkang/Abstraction_experiment-camera-models", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Abstraction_experiment camera models
YOLO weights used by the g1_camera package of
Abstraction_experiment
(Unitree G1 vision-guided grasping, elevator-button press and berry picking, ROS Noetic).
| File | Used by | Classes |
|---|---|---|
find_elevator_bottle.pt |
object_detector_node (default detector) |
0: button, 1: drink_can |
button_cls.pt |
object_detector_node button classifier |
up, down, 1, B1..B6, open, close, emergency_yellow, phone |
unified_detector_yolo11s.pt |
berry_detector_node |
0: button, 1: bottle, 2: berry |
unified_classifier_yolo11s_cls.pt |
berry_detector_node ripeness classifier |
black, red, white (+ button classes) |
Fetch
The project pins this repo's commit and every file's sha256 in
g1_camera/config/model_weights.yaml; rosrun g1_camera fetch_weights.py
downloads and verifies them into g1_camera/models/. Manual download:
hf download Mattewkang/Abstraction_experiment-camera-models --local-dir g1_camera/models
- Downloads last month
- 134