metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_small_adamax_00001_fold5
    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: 0.8048780487804879

hushem_5x_deit_small_adamax_00001_fold5

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6350
Accuracy: 0.8049

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.307	1.0	28	1.2048	0.4878
1.0055	2.0	56	1.0344	0.5366
0.7917	3.0	84	0.8814	0.6829
0.5612	4.0	112	0.7794	0.6585
0.4121	5.0	140	0.6731	0.7805
0.3453	6.0	168	0.6198	0.7561
0.2136	7.0	196	0.5552	0.7805
0.1402	8.0	224	0.5538	0.7805
0.1098	9.0	252	0.5179	0.8049
0.0661	10.0	280	0.4716	0.8293
0.0459	11.0	308	0.4940	0.8049
0.0201	12.0	336	0.4943	0.7805
0.0128	13.0	364	0.4835	0.8049
0.013	14.0	392	0.5177	0.8049
0.005	15.0	420	0.5313	0.7805
0.0049	16.0	448	0.5255	0.8293
0.0033	17.0	476	0.5525	0.8049
0.0027	18.0	504	0.5486	0.8049
0.0024	19.0	532	0.5501	0.8049
0.0021	20.0	560	0.5689	0.8049
0.0017	21.0	588	0.5750	0.8049
0.0016	22.0	616	0.5752	0.8049
0.0015	23.0	644	0.5846	0.8049
0.0013	24.0	672	0.5888	0.8049
0.0012	25.0	700	0.5919	0.8049
0.0012	26.0	728	0.5956	0.8049
0.0011	27.0	756	0.5988	0.8049
0.0011	28.0	784	0.6017	0.8049
0.001	29.0	812	0.6080	0.8049
0.0009	30.0	840	0.6107	0.8049
0.0009	31.0	868	0.6102	0.8049
0.0008	32.0	896	0.6145	0.8049
0.0008	33.0	924	0.6168	0.8049
0.0008	34.0	952	0.6219	0.8049
0.0008	35.0	980	0.6219	0.8049
0.0008	36.0	1008	0.6245	0.8049
0.0007	37.0	1036	0.6250	0.8049
0.0007	38.0	1064	0.6281	0.8049
0.0007	39.0	1092	0.6275	0.8049
0.0006	40.0	1120	0.6308	0.8049
0.0007	41.0	1148	0.6308	0.8049
0.0006	42.0	1176	0.6332	0.8049
0.0006	43.0	1204	0.6344	0.8049
0.0006	44.0	1232	0.6352	0.8049
0.0006	45.0	1260	0.6342	0.8049
0.0006	46.0	1288	0.6344	0.8049
0.0006	47.0	1316	0.6347	0.8049
0.0006	48.0	1344	0.6350	0.8049
0.0006	49.0	1372	0.6350	0.8049
0.0006	50.0	1400	0.6350	0.8049

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0