Image Classification
Transformers
PyTorch
English
vision
damage-detection
classification
vit
household-items
Instructions to use narinzar/damage-classifier-multi-task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use narinzar/damage-classifier-multi-task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="narinzar/damage-classifier-multi-task") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("narinzar/damage-classifier-multi-task", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 426 Bytes
6ac5a86 | 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 | {
"classification_type": "multi_task",
"num_labels": null,
"model_name": "google/vit-base-patch16-224",
"item_categories": [
"microwave",
"wall",
"window",
"fence",
"glass",
"fishbowl"
],
"damage_types": [
"scratch",
"dent",
"break",
"burn",
"water_damage"
],
"damage_severity": [
"no_damage",
"minor_damage",
"moderate_damage",
"severe_damage"
]
} |