Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("johnnydevriese/vit_beans")
model = AutoModelForImageClassification.from_pretrained("johnnydevriese/vit_beans")Quick Links
vit_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: 0.1176
- 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.10.3
- Downloads last month
- 17
Dataset used to train johnnydevriese/vit_beans
Evaluation results
- Accuracy on beansself-reported0.970
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="johnnydevriese/vit_beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")