Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus") 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("Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus") model = AutoModelForImageClassification.from_pretrained("Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus") - Notebooks
- Google Colab
- Kaggle
beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus
This model is a fine-tuned version of Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013 on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0089
- Accuracy: 1.0
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9918 | 0.97 | 27 | 0.2528 | 0.8985 |
| 0.3355 | 1.97 | 54 | 0.0703 | 0.9797 |
| 0.2484 | 2.97 | 81 | 0.0232 | 0.9848 |
| 0.1971 | 3.97 | 108 | 0.0197 | 0.9848 |
| 0.1731 | 4.97 | 135 | 0.0089 | 1.0 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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Evaluation results
- Accuracy on image_folderself-reported1.000