Instructions to use NInjaQuarrior/vit-base-patch16-224-in21k-disaster with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NInjaQuarrior/vit-base-patch16-224-in21k-disaster with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NInjaQuarrior/vit-base-patch16-224-in21k-disaster") 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("NInjaQuarrior/vit-base-patch16-224-in21k-disaster") model = AutoModelForImageClassification.from_pretrained("NInjaQuarrior/vit-base-patch16-224-in21k-disaster", device_map="auto") - Notebooks
- Google Colab
- Kaggle
NInjaQuarrior/vit-base-patch16-224-in21k-disaster
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1536
- Train Accuracy: 0.9803
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.1509
- Validation Accuracy: 0.9733
- Validation Top-3-accuracy: 1.0
- Epoch: 1
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 219, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.4890 | 0.9378 | 1.0 | 0.1937 | 0.9733 | 1.0 | 0 |
| 0.1536 | 0.9803 | 1.0 | 0.1509 | 0.9733 | 1.0 | 1 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.8.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for NInjaQuarrior/vit-base-patch16-224-in21k-disaster
Base model
google/vit-base-patch16-224-in21k