Object Detection
YOLOv10
OpenVINO
PyTorch
GGUF
Safetensors
English
phi3
text-generation
disaster-response
Instructions to use sohail-kustagi/MAAS-Disaster-Response with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use sohail-kustagi/MAAS-Disaster-Response with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("sohail-kustagi/MAAS-Disaster-Response") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
weights/best_openvino_model/best.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8278a2ea2e4f0816a0b73ef536b9f07b5512406f5180843e092fe9e0e6f9769c
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size 22305684
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weights/best_openvino_model/best.xml
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weights/best_openvino_model/metadata.yaml
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description: Ultralytics best model trained on /kaggle/working/data_fixed.yaml
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author: Ultralytics
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date: '2026-08-10T16:09:51.943381'
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version: 8.4.96
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license: AGPL-3.0 License (https://ultralytics.com/license)
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docs: https://docs.ultralytics.com
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stride: 32
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task: detect
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head: Detect
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batch: 1
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imgsz:
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- 640
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- 640
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names:
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0: infrastructure
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1: person
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2: vehicle
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3: watercraft
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4: fire
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args:
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data: null
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batch: 1
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fraction: 1.0
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quantize: 16
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dynamic: false
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nms: false
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channels: 3
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end2end: false
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