Instructions to use Augusto777/vit-base-patch16-224-dmae-va-U5-100bc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Augusto777/vit-base-patch16-224-dmae-va-U5-100bc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Augusto777/vit-base-patch16-224-dmae-va-U5-100bc") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Augusto777/vit-base-patch16-224-dmae-va-U5-100bc") model = AutoModelForImageClassification.from_pretrained("Augusto777/vit-base-patch16-224-dmae-va-U5-100bc") - Notebooks
- Google Colab
- Kaggle
vit-base-patch16-224-dmae-va-U5-100bc
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5017
- Accuracy: 0.8667
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9 | 7 | 0.5017 | 0.8667 |
| 0.3168 | 1.94 | 15 | 0.5970 | 0.8 |
| 0.2613 | 2.97 | 23 | 0.5442 | 0.8167 |
| 0.222 | 4.0 | 31 | 0.7156 | 0.7667 |
| 0.222 | 4.9 | 38 | 0.5175 | 0.85 |
| 0.1783 | 5.94 | 46 | 0.6035 | 0.8167 |
| 0.168 | 6.97 | 54 | 0.5045 | 0.85 |
| 0.1456 | 8.0 | 62 | 0.4923 | 0.85 |
| 0.1456 | 8.9 | 69 | 0.5346 | 0.85 |
| 0.1236 | 9.03 | 70 | 0.5346 | 0.85 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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