metadata

library_name: transformers
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: vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.83375
          - name: Precision
            type: precision
            value: 0.8588680878951838
          - name: Recall
            type: recall
            value: 0.83375
          - name: F1
            type: f1
            value: 0.8355968544321966

vit-base-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX

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: 0.4940
Accuracy: 0.8337
Precision: 0.8589
Recall: 0.8337
F1: 0.8356

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: 32
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.1919	0.3333	100	0.4940	0.8337	0.8589	0.8337	0.8356
0.1697	0.6667	200	0.6993	0.8092	0.8485	0.8092	0.8059
0.1514	1.0	300	0.5555	0.8442	0.8565	0.8442	0.8443
0.0991	1.3333	400	0.5918	0.8467	0.8741	0.8467	0.8453
0.0415	1.6667	500	0.6080	0.8558	0.8690	0.8558	0.8553
0.1112	2.0	600	0.9788	0.7983	0.8485	0.7983	0.8028
0.0658	2.3333	700	1.0272	0.8004	0.8310	0.8004	0.8002
0.0977	2.6667	800	0.6861	0.8479	0.8570	0.8479	0.8482
0.03	3.0	900	0.8317	0.8025	0.8225	0.8025	0.8048
0.0253	3.3333	1000	0.8574	0.8242	0.8408	0.8242	0.8254
0.0564	3.6667	1100	0.8591	0.8392	0.8513	0.8392	0.8343
0.0285	4.0	1200	1.3453	0.7512	0.8090	0.7512	0.7484
0.002	4.3333	1300	0.9746	0.8192	0.8381	0.8192	0.8227
0.0214	4.6667	1400	0.7404	0.8646	0.8641	0.8646	0.8572
0.0282	5.0	1500	1.0063	0.8233	0.8486	0.8233	0.8219
0.03	5.3333	1600	1.0066	0.8025	0.8376	0.8025	0.8058
0.028	5.6667	1700	1.1451	0.8108	0.8325	0.8108	0.8067
0.0078	6.0	1800	1.0700	0.805	0.8220	0.805	0.8045
0.0008	6.3333	1900	1.0180	0.8146	0.8303	0.8146	0.8165
0.0008	6.6667	2000	0.9882	0.8246	0.8401	0.8246	0.8236
0.0006	7.0	2100	1.0366	0.8283	0.8424	0.8283	0.8270
0.0009	7.3333	2200	1.1136	0.8121	0.8309	0.8121	0.8143
0.0068	7.6667	2300	1.0873	0.8117	0.8128	0.8117	0.8015
0.0006	8.0	2400	0.8601	0.8325	0.8383	0.8325	0.8292
0.0187	8.3333	2500	0.9700	0.8258	0.8375	0.8258	0.8241
0.0005	8.6667	2600	0.8825	0.8175	0.8339	0.8175	0.8199
0.0005	9.0	2700	1.0314	0.8242	0.8455	0.8242	0.8230
0.0004	9.3333	2800	1.0323	0.8233	0.8443	0.8233	0.8230
0.0003	9.6667	2900	1.0397	0.8229	0.8433	0.8229	0.8229
0.0003	10.0	3000	1.0473	0.8237	0.8437	0.8237	0.8239
0.0003	10.3333	3100	1.0536	0.8229	0.8428	0.8229	0.8233
0.0003	10.6667	3200	1.0605	0.8229	0.8429	0.8229	0.8234
0.0003	11.0	3300	1.0667	0.8229	0.8429	0.8229	0.8234
0.0002	11.3333	3400	1.0711	0.8237	0.8436	0.8237	0.8243
0.0002	11.6667	3500	1.0750	0.8246	0.8441	0.8246	0.8251
0.0002	12.0	3600	1.0804	0.825	0.8443	0.825	0.8257
0.0002	12.3333	3700	1.0839	0.825	0.8440	0.825	0.8257
0.0002	12.6667	3800	1.0875	0.8246	0.8436	0.8246	0.8253
0.0002	13.0	3900	1.0909	0.8246	0.8436	0.8246	0.8253
0.0002	13.3333	4000	1.0930	0.8246	0.8436	0.8246	0.8253
0.0002	13.6667	4100	1.0954	0.8237	0.8429	0.8237	0.8246
0.0002	14.0	4200	1.0975	0.8237	0.8429	0.8237	0.8246
0.0002	14.3333	4300	1.0988	0.8237	0.8429	0.8237	0.8246
0.0002	14.6667	4400	1.0997	0.8237	0.8429	0.8237	0.8246
0.0002	15.0	4500	1.1000	0.8237	0.8429	0.8237	0.8246

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu126
Datasets 3.2.0
Tokenizers 0.21.0