metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: weather_classification_ViT
    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: 0.9679266895761741
          - name: Precision
            type: precision
            value: 0.9679235596755258
          - name: Recall
            type: recall
            value: 0.9679266895761741
          - name: F1
            type: f1
            value: 0.9678827379290899

weather_classification_ViT

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1268
Accuracy: 0.9679
Precision: 0.9679
Recall: 0.9679
F1: 0.9679
Auc: 0.9974

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: 0.0002
train_batch_size: 16
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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Auc
0.2811	0.2288	100	0.3139	0.8958	0.9147	0.8958	0.8970	0.9903
0.1396	0.4577	200	0.2454	0.9278	0.9307	0.9278	0.9282	0.9919
0.3761	0.6865	300	0.2952	0.9072	0.9117	0.9072	0.9071	0.9889
0.2365	0.9153	400	0.1797	0.9444	0.9447	0.9444	0.9445	0.9940
0.2528	1.1442	500	0.2470	0.9278	0.9307	0.9278	0.9278	0.9924
0.2364	1.3730	600	0.2448	0.9261	0.9306	0.9261	0.9264	0.9934
0.34	1.6018	700	0.1986	0.9404	0.9409	0.9404	0.9405	0.9929
0.2001	1.8307	800	0.1525	0.9542	0.9548	0.9542	0.9539	0.9960
0.0958	2.0595	900	0.1783	0.9507	0.9515	0.9507	0.9505	0.9952
0.1862	2.2883	1000	0.1654	0.9553	0.9558	0.9553	0.9551	0.9952
0.1021	2.5172	1100	0.1654	0.9462	0.9472	0.9462	0.9459	0.9958
0.1178	2.7460	1200	0.1591	0.9525	0.9536	0.9525	0.9523	0.9960
0.0474	2.9748	1300	0.1299	0.9633	0.9635	0.9633	0.9633	0.9975
0.046	3.2037	1400	0.1384	0.9628	0.9628	0.9628	0.9627	0.9972
0.0294	3.4325	1500	0.1388	0.9645	0.9644	0.9645	0.9644	0.9969
0.1833	3.6613	1600	0.1346	0.9633	0.9634	0.9633	0.9633	0.9971
0.0548	3.8902	1700	0.1268	0.9679	0.9679	0.9679	0.9679	0.9974

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1