metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weather-mod
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: dataset
split: train
args: dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.9745222929936306
weather-mod
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0907
- Accuracy: 0.9745
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4096 | 1.0 | 118 | 0.3402 | 0.8790 |
| 0.2749 | 2.0 | 236 | 0.1482 | 0.9490 |
| 0.1989 | 3.0 | 354 | 0.1297 | 0.9660 |
| 0.1129 | 4.0 | 472 | 0.1074 | 0.9788 |
| 0.0827 | 5.0 | 590 | 0.1023 | 0.9745 |
| 0.0644 | 6.0 | 708 | 0.0907 | 0.9745 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2