metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cards-vit-base-patch16-224-finetuned-v1
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: 0.31704202872849796
cards-vit-base-patch16-224-finetuned-v1
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: 1.9972
- Accuracy: 0.3170
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7068 | 0.9993 | 378 | 1.9533 | 0.2753 |
| 1.6691 | 1.9987 | 756 | 1.9642 | 0.2864 |
| 1.6278 | 2.9980 | 1134 | 1.9935 | 0.3018 |
| 1.5837 | 4.0 | 1513 | 2.0155 | 0.3077 |
| 1.5263 | 4.9993 | 1891 | 2.0283 | 0.3063 |
| 1.4969 | 5.9987 | 2269 | 2.0026 | 0.3081 |
| 1.5088 | 6.9980 | 2647 | 2.0275 | 0.3098 |
| 1.4623 | 8.0 | 3026 | 2.0096 | 0.3137 |
| 1.4305 | 8.9993 | 3404 | 2.0239 | 0.3154 |
| 1.3895 | 9.9934 | 3780 | 1.9972 | 0.3170 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1