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: 0.9338235294117647

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: 0.2870
Accuracy: 0.9338

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9231	9	1.7879	0.2059
1.8037	1.9487	19	1.6485	0.4044
1.6961	2.9744	29	1.4882	0.3971
1.5407	4.0	39	1.3069	0.5221
1.3308	4.9231	48	1.1339	0.6029
1.1074	5.9487	58	0.9396	0.75
0.9162	6.9744	68	0.8551	0.7647
0.8174	8.0	78	0.8291	0.7574
0.7135	8.9231	87	0.7505	0.7941
0.6222	9.9487	97	0.6434	0.8456
0.5445	10.9744	107	0.5996	0.8529
0.4935	12.0	117	0.5514	0.8529
0.4131	12.9231	126	0.5029	0.8603
0.4012	13.9487	136	0.5566	0.8382
0.3689	14.9744	146	0.5533	0.8382
0.3533	16.0	156	0.4232	0.8971
0.2954	16.9231	165	0.4589	0.8897
0.2907	17.9487	175	0.4223	0.8971
0.2804	18.9744	185	0.4056	0.8971
0.2469	20.0	195	0.3904	0.9118
0.2643	20.9231	204	0.3866	0.9044
0.2212	21.9487	214	0.4173	0.875
0.2476	22.9744	224	0.6001	0.8015
0.2347	24.0	234	0.3900	0.9044
0.207	24.9231	243	0.4033	0.8897
0.1803	25.9487	253	0.3510	0.9265
0.1979	26.9744	263	0.3723	0.9191
0.1821	28.0	273	0.4320	0.8824
0.1992	28.9231	282	0.3557	0.9118
0.2154	29.9487	292	0.3362	0.9191
0.1801	30.9744	302	0.4358	0.875
0.1794	32.0	312	0.3500	0.9191
0.1566	32.9231	321	0.3046	0.9265
0.1432	33.9487	331	0.3239	0.9265
0.145	34.9744	341	0.3311	0.9338
0.1578	36.0	351	0.3029	0.9338
0.1511	36.9231	360	0.3010	0.9338
0.139	37.9487	370	0.2982	0.9265
0.1294	38.9744	380	0.3261	0.9191
0.1263	40.0	390	0.2932	0.9338
0.1263	40.9231	399	0.2944	0.9338
0.1216	41.9487	409	0.2867	0.9338
0.1199	42.9744	419	0.2887	0.9338
0.128	44.0	429	0.2825	0.9338
0.1115	44.9231	438	0.2880	0.9338
0.1179	45.9487	448	0.2871	0.9338
0.12	46.1538	450	0.2870	0.9338

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1