metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_small_rms_00001_fold3
    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: 0.905

smids_1x_deit_small_rms_00001_fold3

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: 0.7182
Accuracy: 0.905

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
0.3259	1.0	75	0.3001	0.89
0.2426	2.0	150	0.3217	0.8717
0.1676	3.0	225	0.2596	0.9083
0.1287	4.0	300	0.2827	0.895
0.0316	5.0	375	0.3452	0.885
0.0237	6.0	450	0.3793	0.9017
0.0244	7.0	525	0.4128	0.8967
0.0233	8.0	600	0.4590	0.8883
0.0286	9.0	675	0.4790	0.8983
0.0295	10.0	750	0.4835	0.8917
0.0562	11.0	825	0.4705	0.9067
0.0087	12.0	900	0.5035	0.9033
0.0083	13.0	975	0.5418	0.9017
0.0001	14.0	1050	0.5563	0.9
0.0012	15.0	1125	0.5874	0.8983
0.0001	16.0	1200	0.5698	0.8967
0.0001	17.0	1275	0.5930	0.9033
0.0062	18.0	1350	0.5972	0.9017
0.0048	19.0	1425	0.5918	0.9033
0.0089	20.0	1500	0.6518	0.9017
0.0001	21.0	1575	0.7835	0.885
0.0001	22.0	1650	0.6700	0.9
0.0031	23.0	1725	0.6679	0.8983
0.0	24.0	1800	0.6364	0.9033
0.0001	25.0	1875	0.6464	0.8983
0.003	26.0	1950	0.6535	0.8967
0.0	27.0	2025	0.6525	0.8983
0.0	28.0	2100	0.6526	0.8983
0.0	29.0	2175	0.6663	0.895
0.0	30.0	2250	0.6645	0.8983
0.0	31.0	2325	0.6717	0.9
0.0	32.0	2400	0.6659	0.8983
0.0	33.0	2475	0.6774	0.9017
0.0051	34.0	2550	0.6726	0.905
0.0059	35.0	2625	0.7209	0.8933
0.0031	36.0	2700	0.6818	0.9067
0.0022	37.0	2775	0.6938	0.8967
0.0	38.0	2850	0.6968	0.8967
0.0	39.0	2925	0.7122	0.8983
0.0	40.0	3000	0.7008	0.8983
0.0	41.0	3075	0.7070	0.8983
0.0026	42.0	3150	0.7002	0.9
0.0025	43.0	3225	0.7107	0.9
0.0	44.0	3300	0.7106	0.9033
0.0025	45.0	3375	0.7116	0.905
0.0025	46.0	3450	0.7142	0.905
0.0047	47.0	3525	0.7163	0.9033
0.0	48.0	3600	0.7169	0.9033
0.0	49.0	3675	0.7178	0.9033
0.0045	50.0	3750	0.7182	0.905

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0