# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Abhiram4/VitTea")
model = AutoModelForImageClassification.from_pretrained("Abhiram4/VitTea")Quick Links
VitTea
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 1.9377
- Accuracy: 0.3034
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|---|---|---|---|---|
| No log | 0.8 | 2 | 2.0404 | 0.1573 |
| No log | 2.0 | 5 | 1.9487 | 0.3034 |
| No log | 2.4 | 6 | 1.9377 | 0.3034 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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Model tree for Abhiram4/VitTea
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on image_folderself-reported0.303
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Abhiram4/VitTea") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")