Instructions to use cvmil/deit-base-patch16-224_augmented-v2_fft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvmil/deit-base-patch16-224_augmented-v2_fft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cvmil/deit-base-patch16-224_augmented-v2_fft") 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("cvmil/deit-base-patch16-224_augmented-v2_fft") model = AutoModelForImageClassification.from_pretrained("cvmil/deit-base-patch16-224_augmented-v2_fft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from cvmil/deit-base-patch16-224_augmented-v2_fft: direct link, hf CLI and curl.
- Browser
- Download file 2.54 kB
-
https://huggingface.co/cvmil/deit-base-patch16-224_augmented-v2_fft/resolve/main/README.md
- Command line
-
hf download hf://cvmil/deit-base-patch16-224_augmented-v2_fft/README.md
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curl -L -o README.md https://huggingface.co/cvmil/deit-base-patch16-224_augmented-v2_fft/resolve/main/README.md
2.54 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deit-base-patch16-224_rice-leaf-disease-augmented-v2_fft
results: []
deit-base-patch16-224_rice-leaf-disease-augmented-v2_fft
This model is a fine-tuned version of facebook/deit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4335
- Accuracy: 0.8810
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:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7917 | 1.0 | 125 | 1.1504 | 0.6577 |
| 0.6305 | 2.0 | 250 | 0.4746 | 0.8363 |
| 0.1802 | 3.0 | 375 | 0.3663 | 0.8631 |
| 0.0508 | 4.0 | 500 | 0.3550 | 0.8690 |
| 0.0152 | 5.0 | 625 | 0.3373 | 0.8839 |
| 0.0092 | 6.0 | 750 | 0.3433 | 0.8839 |
| 0.0067 | 7.0 | 875 | 0.3768 | 0.8839 |
| 0.002 | 8.0 | 1000 | 0.3861 | 0.875 |
| 0.001 | 9.0 | 1125 | 0.3976 | 0.8810 |
| 0.0009 | 10.0 | 1250 | 0.3989 | 0.8839 |
| 0.0008 | 11.0 | 1375 | 0.4085 | 0.8839 |
| 0.0006 | 12.0 | 1500 | 0.4185 | 0.8810 |
| 0.0004 | 13.0 | 1625 | 0.4294 | 0.8780 |
| 0.0004 | 14.0 | 1750 | 0.4326 | 0.8780 |
| 0.0004 | 15.0 | 1875 | 0.4335 | 0.8810 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0