Instructions to use dl1wjd2/Logistics_AX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dl1wjd2/Logistics_AX 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("dl1wjd2/Logistics_AX", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download training/data_pose.yaml from dl1wjd2/Logistics_AX: direct link, hf CLI and curl.
- Browser
- Download file 377 Bytes
-
https://huggingface.co/dl1wjd2/Logistics_AX/resolve/main/training/data_pose.yaml
- Command line
-
hf download hf://dl1wjd2/Logistics_AX/training/data_pose.yaml
-
curl -L -o data_pose.yaml https://huggingface.co/dl1wjd2/Logistics_AX/resolve/main/training/data_pose.yaml
377 Bytes
| format: labelme_pose | |
| path: datasets | |
| train: images | |
| val: images | |
| labelme_annotations: annotations | |
| splits_json: datasets/splits.json | |
| allow_missing_keypoints: true | |
| names: | |
| 0: pallet | |
| kpt_shape: | |
| - 8 | |
| - 3 | |
| kpt_names: | |
| 0: | |
| - left_top_left | |
| - left_top_right | |
| - left_bottom_right | |
| - left_bottom_left | |
| - right_top_left | |
| - right_top_right | |
| - right_bottom_right | |
| - right_bottom_left | |