metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_tiny_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: 0.8292682926829268

hushem_5x_deit_tiny_adamax_00001_fold5

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

Loss: 1.0196
Accuracy: 0.8293

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
1.3464	1.0	28	1.2206	0.4390
1.038	2.0	56	1.1163	0.4390
0.9036	3.0	84	1.0326	0.5122
0.7256	4.0	112	0.9850	0.4634
0.6091	5.0	140	0.9299	0.5366
0.5118	6.0	168	0.8096	0.6098
0.3976	7.0	196	0.8337	0.6341
0.2983	8.0	224	0.8361	0.6829
0.2464	9.0	252	0.7489	0.6829
0.1797	10.0	280	0.7126	0.7317
0.155	11.0	308	0.7190	0.7317
0.1087	12.0	336	0.7349	0.7561
0.0817	13.0	364	0.6756	0.7805
0.0808	14.0	392	0.7587	0.7561
0.0526	15.0	420	0.6534	0.7805
0.0415	16.0	448	0.7396	0.7805
0.0249	17.0	476	0.7772	0.8049
0.0224	18.0	504	0.7783	0.8049
0.016	19.0	532	0.8153	0.7805
0.0121	20.0	560	0.8052	0.8049
0.0082	21.0	588	0.8047	0.8049
0.0059	22.0	616	0.8544	0.8049
0.0042	23.0	644	0.9271	0.7805
0.0032	24.0	672	0.8999	0.8049
0.0029	25.0	700	0.9068	0.8293
0.0025	26.0	728	0.9094	0.8293
0.0022	27.0	756	0.9291	0.8293
0.0019	28.0	784	0.9347	0.8293
0.0016	29.0	812	0.9448	0.8293
0.0016	30.0	840	0.9586	0.8293
0.0015	31.0	868	0.9704	0.8293
0.0013	32.0	896	0.9735	0.8293
0.0013	33.0	924	0.9776	0.8293
0.0012	34.0	952	0.9829	0.8293
0.0011	35.0	980	0.9923	0.8293
0.0011	36.0	1008	0.9922	0.8293
0.001	37.0	1036	0.9983	0.8293
0.001	38.0	1064	1.0035	0.8293
0.0009	39.0	1092	0.9985	0.8293
0.0009	40.0	1120	1.0064	0.8293
0.0009	41.0	1148	1.0089	0.8293
0.0008	42.0	1176	1.0130	0.8293
0.0008	43.0	1204	1.0152	0.8293
0.0009	44.0	1232	1.0185	0.8293
0.0009	45.0	1260	1.0165	0.8293
0.0009	46.0	1288	1.0180	0.8293
0.0008	47.0	1316	1.0182	0.8293
0.0008	48.0	1344	1.0196	0.8293
0.0008	49.0	1372	1.0196	0.8293
0.0008	50.0	1400	1.0196	0.8293

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0