Instructions to use Luwayy/disaster_images_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Luwayy/disaster_images_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Luwayy/disaster_images_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Luwayy/disaster_images_model") model = AutoModelForImageClassification.from_pretrained("Luwayy/disaster_images_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 787 Bytes
1615e64 | 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 27 28 29 30 31 32 33 34 35 | {
"do_normalize": true,
"do_resize": true,
"image_mean": [0.5, 0.5, 0.5],
"image_processor_type": "ViTImageProcessor",
"image_std": [0.5, 0.5, 0.5],
"size": {
"height": 224,
"width": 224
},
"image_formats": [".png", ".jpg", ".jpeg", ".bmp", ".gif", ".tiff"],
"do_verify_images": true,
"exclude_labels": ["Land_Slide"],
"resample_method": "bilinear",
"training_args": {
"batch_size": 16,
"num_epochs": 3,
"learning_rate": 3e-5,
"weight_decay": 0.01,
"evaluation_strategy": "epoch",
"save_strategy": "epoch"
},
"dataset_split": {
"train_test_ratio": 0.8
},
"image_augmentation": {
"resize": [224, 224],
"to_tensor": true,
"normalize": {
"mean": [0.5, 0.5, 0.5],
"std": [0.5, 0.5, 0.5]
}
}
}
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