metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: datrix-image-classification-job_98cc84ca
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9785714285714285
datrix-image-classification-job_98cc84ca
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0538
- Accuracy: 0.9786
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5603 | 1.0 | 35 | 0.1752 | 0.9714 |
| 0.1090 | 2.0 | 70 | 0.0774 | 0.9786 |
| 0.0645 | 3.0 | 105 | 0.0651 | 0.9786 |
| 0.0398 | 4.0 | 140 | 0.0504 | 0.9786 |
| 0.0656 | 5.0 | 175 | 0.0528 | 0.9786 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2