# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Countigo/vit-base-beans")
model = AutoModelForImageClassification.from_pretrained("Countigo/vit-base-beans")Quick Links
vit-base-beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.2258
- Accuracy: 0.9699
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9859 | 1.0 | 17 | 0.7492 | 0.9323 |
| 0.6763 | 2.0 | 34 | 0.5276 | 0.9624 |
| 0.4605 | 3.0 | 51 | 0.3726 | 0.9624 |
| 0.404 | 4.0 | 68 | 0.2965 | 0.9699 |
| 0.3169 | 5.0 | 85 | 0.2538 | 0.9699 |
| 0.2536 | 6.0 | 102 | 0.2273 | 0.9774 |
| 0.2633 | 7.0 | 119 | 0.2258 | 0.9699 |
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Countigo/vit-base-beans
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Countigo/vit-base-beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")