metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9071691176470589
vit-base-patch16-224-finetuned-eurosat
This model is a fine-tuned version of google/vit-base-patch16-224 on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.9072
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|---|---|---|---|---|
| 0.5417 | 0.99 | 76 | 0.5556 | 0.8263 |
| 0.4853 | 1.99 | 152 | 0.5319 | 0.8199 |
| 0.4926 | 2.99 | 228 | 0.5133 | 0.8539 |
| 0.4131 | 3.99 | 304 | 0.4481 | 0.8603 |
| 0.4081 | 4.99 | 380 | 0.4280 | 0.8824 |
| 0.3287 | 5.99 | 456 | 0.4330 | 0.8667 |
| 0.3381 | 6.99 | 532 | 0.3549 | 0.8888 |
| 0.3182 | 7.99 | 608 | 0.3382 | 0.8961 |
| 0.3046 | 8.99 | 684 | 0.3790 | 0.8925 |
| 0.3093 | 9.99 | 760 | 0.3209 | 0.9072 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1