metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_sgd_001_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: 0.8766666666666667

smids_3x_deit_tiny_sgd_001_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: 0.2846
Accuracy: 0.8767

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
0.8735	1.0	225	0.9170	0.545
0.6877	2.0	450	0.6945	0.7167
0.5875	3.0	675	0.5531	0.79
0.4761	4.0	900	0.4755	0.82
0.4339	5.0	1125	0.4319	0.83
0.3839	6.0	1350	0.4018	0.8483
0.4408	7.0	1575	0.3811	0.85
0.4281	8.0	1800	0.3653	0.8583
0.3652	9.0	2025	0.3551	0.8533
0.324	10.0	2250	0.3480	0.8567
0.3571	11.0	2475	0.3391	0.86
0.3339	12.0	2700	0.3292	0.86
0.343	13.0	2925	0.3232	0.865
0.3099	14.0	3150	0.3188	0.8633
0.2636	15.0	3375	0.3160	0.87
0.2725	16.0	3600	0.3109	0.8633
0.2598	17.0	3825	0.3041	0.8717
0.2377	18.0	4050	0.3051	0.8683
0.2636	19.0	4275	0.2990	0.8683
0.2944	20.0	4500	0.2992	0.8733
0.2247	21.0	4725	0.2983	0.8733
0.2126	22.0	4950	0.2963	0.8733
0.2221	23.0	5175	0.2922	0.8783
0.2198	24.0	5400	0.2918	0.8717
0.2574	25.0	5625	0.2955	0.88
0.2932	26.0	5850	0.2903	0.88
0.2755	27.0	6075	0.2866	0.8767
0.2735	28.0	6300	0.2890	0.8783
0.2207	29.0	6525	0.2874	0.88
0.1879	30.0	6750	0.2869	0.875
0.1763	31.0	6975	0.2867	0.88
0.2308	32.0	7200	0.2871	0.8733
0.1914	33.0	7425	0.2850	0.8767
0.1699	34.0	7650	0.2866	0.875
0.1804	35.0	7875	0.2842	0.88
0.182	36.0	8100	0.2861	0.8783
0.2385	37.0	8325	0.2854	0.8783
0.1637	38.0	8550	0.2879	0.8783
0.1554	39.0	8775	0.2876	0.8767
0.2151	40.0	9000	0.2856	0.8783
0.1931	41.0	9225	0.2849	0.8783
0.1711	42.0	9450	0.2846	0.8783
0.2357	43.0	9675	0.2864	0.8783
0.202	44.0	9900	0.2845	0.88
0.1905	45.0	10125	0.2855	0.88
0.1822	46.0	10350	0.2847	0.8767
0.2034	47.0	10575	0.2849	0.8767
0.1793	48.0	10800	0.2851	0.8767
0.2049	49.0	11025	0.2846	0.8767
0.168	50.0	11250	0.2846	0.8767

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2