Object Detection
ultralytics
ONNX
English
yolo
yolov11
warehouse
pallet-detection
logistics
computer-vision
industrial
forklift
warehouse-automation
Eval Results (legacy)
Instructions to use EFFGRP/yolov11n-warehouse-pallets-640 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use EFFGRP/yolov11n-warehouse-pallets-640 with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("EFFGRP/yolov11n-warehouse-pallets-640") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 74561e7ea6dfe33f380ad7488c5e0b09b0e3bad91be3d95793ed1d9016136691
- Size of remote file:
- 461 kB
- SHA256:
- 177b66b4ba407249ff06be0cf280e55224ee5b417152c4b0f42ca9d379d58bdc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.