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
File size: 976 Bytes
26f5f82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"run": "pallet_pose_20261029T110641464517Z",
"best_epoch": 95,
"epochs_completed": 100,
"counts": {
"train": 124,
"val": 31,
"test": 0
},
"metrics": {
"metrics/precision(B)": 0.9973,
"metrics/recall(B)": 1.0,
"metrics/mAP50(B)": 0.995,
"metrics/mAP50-95(B)": 0.84352,
"metrics/precision(P)": 0.96062,
"metrics/recall(P)": 0.91667,
"metrics/mAP50(P)": 0.915,
"metrics/mAP50-95(P)": 0.915,
"val/box_loss": 0.91957,
"val/pose_loss": 0.07617,
"val/kobj_loss": 0.12809,
"val/cls_loss": 0.12259,
"val/l1_loss": 0.00793,
"val/rle_loss": 0.0,
"fitness": 1.75852
},
"weights_sha256": "6b91d8fbb1dc62eb6ccd1c10ae312908d0204e87d62cb3af78e5a1d25065b8ca",
"dataset": {
"repo_id": "dl1wjd2/Logistics_AX",
"revision": "v2026-10-29-155",
"manifest_sha256": "558615a635ffd24dc612fb81e143998cccb9ca7cb30518fe4213578c7b5f2b19",
"commit": "ca1a17875de52c422d557107432be7616171c6e9"
}
}
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