metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold5_vit_42500027
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9984202211690363
S5_M1_fold5_vit_42500027
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: 0.0080
- Accuracy: 0.9984
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.087 | 1.0 | 79 | 0.0385 | 0.9961 |
| 0.0116 | 1.99 | 158 | 0.0212 | 0.9953 |
| 0.0235 | 2.99 | 237 | 0.0064 | 0.9992 |
| 0.007 | 4.0 | 317 | 0.0068 | 0.9992 |
| 0.0016 | 4.98 | 395 | 0.0080 | 0.9984 |
Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0