metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-papsmear
    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: 0.9411764705882353

vit-base-patch16-224-in21k-finetuned-papsmear

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: 0.2523
Accuracy: 0.9412

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.6954	0.9935	38	1.6106	0.3456
1.2818	1.9869	76	1.2412	0.5735
1.0023	2.9804	114	0.9875	0.7132
0.7163	4.0	153	0.8399	0.6912
0.5173	4.9935	191	0.6546	0.8162
0.5057	5.9869	229	0.6251	0.8309
0.4313	6.9804	267	0.5696	0.8309
0.325	8.0	306	0.5507	0.8309
0.3811	8.9935	344	0.4429	0.8676
0.2341	9.9869	382	0.4222	0.875
0.3082	10.9804	420	0.6573	0.7721
0.2571	12.0	459	0.4229	0.8897
0.2374	12.9935	497	0.4233	0.875
0.128	13.9869	535	0.3671	0.8971
0.1718	14.9804	573	0.3430	0.8971
0.16	16.0	612	0.4104	0.875
0.1096	16.9935	650	0.2920	0.9118
0.1408	17.9869	688	0.2630	0.9044
0.113	18.9804	726	0.3084	0.8824
0.1272	20.0	765	0.2523	0.9412
0.119	20.9935	803	0.4254	0.8824
0.1068	21.9869	841	0.3519	0.8971
0.0723	22.9804	879	0.3293	0.9191
0.0769	24.0	918	0.2613	0.9265
0.095	24.9935	956	0.2609	0.9412
0.0863	25.9869	994	0.2650	0.9265
0.0795	26.9804	1032	0.2978	0.9118
0.0564	28.0	1071	0.2737	0.9191
0.0562	28.9935	1109	0.2941	0.9191
0.0751	29.8039	1140	0.3111	0.9191

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1