Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("harrytechiz/vit-base-patch16-224-blur_vs_clean")
model = AutoModelForImageClassification.from_pretrained("harrytechiz/vit-base-patch16-224-blur_vs_clean")Quick Links
vit-base-patch16-224-blur_vs_clean
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0714
- Accuracy: 0.9754
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: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0539 | 1.0 | 151 | 0.1078 | 0.9596 |
| 0.0611 | 2.0 | 302 | 0.0846 | 0.9698 |
| 0.049 | 3.0 | 453 | 0.0714 | 0.9754 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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Model tree for harrytechiz/vit-base-patch16-224-blur_vs_clean
Base model
google/vit-base-patch16-224Evaluation results
- Accuracy on imagefolderself-reported0.975
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="harrytechiz/vit-base-patch16-224-blur_vs_clean") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")