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_sgd_001_fold4
    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: 0.8616666666666667

smids_1x_deit_small_sgd_001_fold4

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: 0.3679
Accuracy: 0.8617

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: 0.001
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
0.9928	1.0	75	0.9393	0.6083
0.8712	2.0	150	0.8295	0.6433
0.7828	3.0	225	0.7388	0.7083
0.6696	4.0	300	0.6714	0.7567
0.6657	5.0	375	0.6202	0.7767
0.5692	6.0	450	0.5823	0.7883
0.568	7.0	525	0.5504	0.795
0.5196	8.0	600	0.5262	0.805
0.5314	9.0	675	0.5056	0.8167
0.4867	10.0	750	0.4885	0.8183
0.4734	11.0	825	0.4740	0.8183
0.4623	12.0	900	0.4619	0.8267
0.4683	13.0	975	0.4510	0.835
0.4153	14.0	1050	0.4430	0.8317
0.3992	15.0	1125	0.4360	0.8333
0.3763	16.0	1200	0.4269	0.845
0.3576	17.0	1275	0.4206	0.85
0.3638	18.0	1350	0.4146	0.845
0.3788	19.0	1425	0.4098	0.8433
0.333	20.0	1500	0.4060	0.85
0.3671	21.0	1575	0.4016	0.85
0.3178	22.0	1650	0.3983	0.8533
0.3335	23.0	1725	0.3950	0.8467
0.3527	24.0	1800	0.3923	0.8533
0.3211	25.0	1875	0.3897	0.8483
0.3209	26.0	1950	0.3876	0.855
0.2907	27.0	2025	0.3854	0.8517
0.3294	28.0	2100	0.3833	0.86
0.2805	29.0	2175	0.3818	0.8533
0.3183	30.0	2250	0.3799	0.855
0.2622	31.0	2325	0.3784	0.86
0.3165	32.0	2400	0.3771	0.86
0.2898	33.0	2475	0.3761	0.8617
0.2776	34.0	2550	0.3750	0.8617
0.2847	35.0	2625	0.3739	0.8633
0.2631	36.0	2700	0.3731	0.86
0.2584	37.0	2775	0.3723	0.8583
0.2889	38.0	2850	0.3718	0.8617
0.3213	39.0	2925	0.3709	0.8617
0.2873	40.0	3000	0.3704	0.865
0.284	41.0	3075	0.3700	0.865
0.2939	42.0	3150	0.3694	0.8633
0.3121	43.0	3225	0.3692	0.8633
0.2744	44.0	3300	0.3688	0.8617
0.2881	45.0	3375	0.3686	0.8617
0.302	46.0	3450	0.3683	0.8617
0.2645	47.0	3525	0.3682	0.8617
0.307	48.0	3600	0.3681	0.8617
0.2733	49.0	3675	0.3680	0.8617
0.2676	50.0	3750	0.3679	0.8617

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0