metadata

base_model: google/vit-base-patch16-224-in21k
datasets:
  - webdataset
license: apache-2.0
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.963716814159292
            name: Accuracy
          - type: f1
            value: 0.9118279569892475
            name: F1
          - type: precision
            value: 0.905982905982906
            name: Precision
          - type: recall
            value: 0.9177489177489178
            name: Recall

vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost

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

Loss: 0.0965
Accuracy: 0.9637
F1: 0.9118
Precision: 0.9060
Recall: 0.9177

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
0.0728	1.5625	100	0.0659	0.9841	0.9607	0.9692	0.9524
0.0871	3.125	200	0.1244	0.9566	0.8942	0.8922	0.8961
0.0999	4.6875	300	0.1043	0.9637	0.9126	0.8992	0.9264
0.0743	6.25	400	0.1043	0.9611	0.9043	0.9083	0.9004
0.0655	7.8125	500	0.0965	0.9637	0.9118	0.9060	0.9177
0.0559	9.375	600	0.1038	0.9619	0.9087	0.8917	0.9264
0.0517	10.9375	700	0.0972	0.9584	0.8998	0.8866	0.9134
0.0407	12.5	800	0.1120	0.9637	0.9111	0.9130	0.9091
0.0513	14.0625	900	0.1093	0.9558	0.8894	0.9095	0.8701
0.0378	15.625	1000	0.1197	0.9549	0.8889	0.8947	0.8831
0.0487	17.1875	1100	0.0955	0.9646	0.9138	0.9099	0.9177
0.0272	18.75	1200	0.1088	0.9566	0.8928	0.9027	0.8831
0.0241	20.3125	1300	0.0979	0.9637	0.9114	0.9095	0.9134
0.0311	21.875	1400	0.1134	0.9655	0.9158	0.9138	0.9177
0.0303	23.4375	1500	0.1092	0.9628	0.9079	0.92	0.8961
0.0225	25.0	1600	0.1121	0.9628	0.9083	0.9163	0.9004
0.0292	26.5625	1700	0.1149	0.9619	0.9071	0.9052	0.9091
0.0261	28.125	1800	0.1107	0.9619	0.9079	0.8983	0.9177
0.0166	29.6875	1900	0.1110	0.9611	0.9052	0.9013	0.9091

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1