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_adamax_0001_fold2
    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: 0.8768718801996672

smids_1x_deit_small_adamax_0001_fold2

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: 0.8040
Accuracy: 0.8769

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: 0.0001
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
0.3172	1.0	75	0.3354	0.8636
0.1909	2.0	150	0.2950	0.8785
0.1696	3.0	225	0.3030	0.8902
0.1088	4.0	300	0.3512	0.8735
0.0828	5.0	375	0.3819	0.8719
0.0816	6.0	450	0.5235	0.8735
0.017	7.0	525	0.4625	0.8802
0.0096	8.0	600	0.6732	0.8536
0.0297	9.0	675	0.5099	0.8852
0.0129	10.0	750	0.6168	0.8819
0.0004	11.0	825	0.6434	0.8769
0.0003	12.0	900	0.6532	0.8752
0.0115	13.0	975	0.7781	0.8669
0.0025	14.0	1050	0.6839	0.8735
0.0031	15.0	1125	0.6481	0.8802
0.0037	16.0	1200	0.7018	0.8719
0.0001	17.0	1275	0.6843	0.8752
0.0057	18.0	1350	0.6963	0.8819
0.0001	19.0	1425	0.6873	0.8802
0.0036	20.0	1500	0.7059	0.8785
0.0001	21.0	1575	0.7123	0.8819
0.0	22.0	1650	0.7298	0.8785
0.0	23.0	1725	0.7182	0.8785
0.0	24.0	1800	0.7389	0.8752
0.0	25.0	1875	0.7283	0.8785
0.0	26.0	1950	0.7283	0.8802
0.0038	27.0	2025	0.7334	0.8819
0.0034	28.0	2100	0.7554	0.8735
0.0022	29.0	2175	0.7526	0.8752
0.0035	30.0	2250	0.7536	0.8769
0.0026	31.0	2325	0.7690	0.8719
0.0	32.0	2400	0.7598	0.8769
0.0	33.0	2475	0.7644	0.8752
0.0	34.0	2550	0.7770	0.8769
0.0081	35.0	2625	0.7696	0.8735
0.0	36.0	2700	0.7747	0.8735
0.0	37.0	2775	0.7776	0.8735
0.0	38.0	2850	0.7800	0.8735
0.0022	39.0	2925	0.7797	0.8735
0.0	40.0	3000	0.7884	0.8752
0.0028	41.0	3075	0.7926	0.8785
0.0	42.0	3150	0.7941	0.8769
0.0025	43.0	3225	0.7995	0.8752
0.0026	44.0	3300	0.7969	0.8752
0.0	45.0	3375	0.7932	0.8785
0.0	46.0	3450	0.8020	0.8752
0.0023	47.0	3525	0.8011	0.8702
0.0	48.0	3600	0.8043	0.8769
0.0022	49.0	3675	0.8040	0.8769
0.0022	50.0	3750	0.8040	0.8769

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0