# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("nailashfrni/image_classification")
model = AutoModelForImageClassification.from_pretrained("nailashfrni/image_classification")Quick Links
image_classification
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: 0.1728
- Accuracy: 0.9420
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 52 | 0.2885 | 0.9179 |
| No log | 2.0 | 104 | 0.1829 | 0.9469 |
| No log | 3.0 | 156 | 0.1789 | 0.9565 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for nailashfrni/image_classification
Base model
google/vit-base-patch16-224-in21kDataset used to train nailashfrni/image_classification
Evaluation results
- Accuracy on beansself-reported0.942
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nailashfrni/image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")