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
| { | |
| "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" | |
| ] | |
| } |