metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vitmae_largeFunny5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.683083511777302
vitmae_largeFunny5
This model is a fine-tuned version of facebook/vit-mae-large on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6622
- Accuracy: 0.6831
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6984 | 0.52 | 100 | 0.6662 | 0.5974 |
| 0.6214 | 1.04 | 200 | 0.6404 | 0.6081 |
| 0.6203 | 1.56 | 300 | 0.6205 | 0.6638 |
| 0.5689 | 2.08 | 400 | 0.6171 | 0.6745 |
| 0.5278 | 2.6 | 500 | 0.6377 | 0.6788 |
| 0.4602 | 3.12 | 600 | 0.6414 | 0.6895 |
| 0.373 | 3.65 | 700 | 0.6622 | 0.6831 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3