metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: weather-base
    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: 0.9358600583090378

weather-base

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: 0.2184
Accuracy: 0.9359

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
0.3368	1.0	171	0.2780	0.9009
0.2129	2.0	342	0.2333	0.9300
0.1827	3.0	513	0.2440	0.9213
0.1475	4.0	684	0.2306	0.9315
0.1284	5.0	855	0.2192	0.9359
0.0526	6.0	1026	0.2184	0.9359

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2