metadata

base_model: google/vit-base-patch16-384
datasets:
  - webdataset
license: apache-2.0
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: vit-base-patch16-384-finetuned_v2024-7-25-frost
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9746666666666667
            name: Accuracy
          - type: f1
            value: 0.9372937293729373
            name: F1
          - type: precision
            value: 0.9342105263157895
            name: Precision
          - type: recall
            value: 0.9403973509933775
            name: Recall

vit-base-patch16-384-finetuned_v2024-7-25-frost

This model is a fine-tuned version of google/vit-base-patch16-384 on the webdataset dataset. It achieves the following results on the evaluation set:

Loss: 0.0795
Accuracy: 0.9747
F1: 0.9373
Precision: 0.9342
Recall: 0.9404

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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0624	1.7544	100	0.0458	0.9867	0.9665	0.9774	0.9558
0.0729	3.5088	200	0.0942	0.9689	0.9220	0.9303	0.9139
0.0566	5.2632	300	0.0802	0.972	0.9311	0.9221	0.9404
0.051	7.0175	400	0.0965	0.9631	0.9066	0.9243	0.8896
0.0686	8.7719	500	0.0795	0.9747	0.9373	0.9342	0.9404
0.0271	10.5263	600	0.0935	0.9693	0.9239	0.9229	0.9249
0.0273	12.2807	700	0.0975	0.9716	0.9300	0.9219	0.9382
0.0445	14.0351	800	0.0910	0.9698	0.9248	0.9268	0.9227
0.0217	15.7895	900	0.0942	0.9698	0.9243	0.9326	0.9161
0.0257	17.5439	1000	0.0906	0.9684	0.9210	0.9283	0.9139
0.0188	19.2982	1100	0.1028	0.9676	0.9181	0.9338	0.9029
0.0196	21.0526	1200	0.1020	0.9698	0.9244	0.9306	0.9183
0.025	22.8070	1300	0.1005	0.9702	0.9258	0.9289	0.9227
0.009	24.5614	1400	0.0976	0.9729	0.9324	0.9356	0.9294
0.0184	26.3158	1500	0.0987	0.9716	0.9290	0.9332	0.9249
0.0048	28.0702	1600	0.0958	0.972	0.9301	0.9353	0.9249
0.0072	29.8246	1700	0.0948	0.972	0.9301	0.9353	0.9249

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1