metadata
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-roman-numeral
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: farleyknight/roman_numerals
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8308823529411765
vit-base-roman-numeral
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the farleyknight/roman_numerals dataset. It achieves the following results on the evaluation set:
- Loss: 0.6891
- Accuracy: 0.8309
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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9053 | 1.0 | 289 | 1.3241 | 0.7108 |
| 1.3293 | 2.0 | 578 | 0.9333 | 0.7892 |
| 1.1251 | 3.0 | 867 | 0.7989 | 0.7843 |
| 0.9837 | 4.0 | 1156 | 0.6956 | 0.8186 |
| 0.999 | 5.0 | 1445 | 0.6891 | 0.8309 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1