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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 353 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 | {
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "ViTFeatureExtractor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
}
}
|