Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("YoussefSaad/out")
model = AutoModelForImageClassification.from_pretrained("YoussefSaad/out")Quick Links
out
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1475
- Accuracy: 0.9587
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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1402 | 1.39 | 100 | 0.2101 | 0.9371 |
| 0.0538 | 2.78 | 200 | 0.1529 | 0.9548 |
| 0.0164 | 4.17 | 300 | 0.1475 | 0.9587 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Downloads last month
- 10
Evaluation results
- Accuracy on imagefolderself-reported0.959
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="YoussefSaad/out") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")