metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_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: 0.8766666666666667

smids_10x_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: 0.3291
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
0.5469	1.0	750	0.5533	0.7983
0.4148	2.0	1500	0.4326	0.8367
0.3982	3.0	2250	0.3912	0.8467
0.355	4.0	3000	0.3693	0.8533
0.3032	5.0	3750	0.3569	0.8583
0.2345	6.0	4500	0.3466	0.8617
0.2053	7.0	5250	0.3412	0.865
0.2443	8.0	6000	0.3381	0.8633
0.259	9.0	6750	0.3314	0.875
0.2146	10.0	7500	0.3275	0.8717
0.2301	11.0	8250	0.3262	0.8733
0.298	12.0	9000	0.3264	0.8733
0.2031	13.0	9750	0.3234	0.8783
0.1941	14.0	10500	0.3276	0.8783
0.1822	15.0	11250	0.3209	0.88
0.2209	16.0	12000	0.3226	0.8767
0.1294	17.0	12750	0.3179	0.8817
0.1726	18.0	13500	0.3224	0.88
0.2222	19.0	14250	0.3196	0.8833
0.1604	20.0	15000	0.3199	0.8817
0.1742	21.0	15750	0.3204	0.8783
0.1599	22.0	16500	0.3188	0.88
0.1753	23.0	17250	0.3189	0.8817
0.1975	24.0	18000	0.3189	0.8817
0.1797	25.0	18750	0.3190	0.8817
0.1646	26.0	19500	0.3244	0.8817
0.1585	27.0	20250	0.3244	0.885
0.1303	28.0	21000	0.3225	0.8817
0.1144	29.0	21750	0.3207	0.8817
0.1409	30.0	22500	0.3230	0.8817
0.1303	31.0	23250	0.3219	0.8833
0.1405	32.0	24000	0.3260	0.8817
0.1503	33.0	24750	0.3248	0.88
0.1402	34.0	25500	0.3257	0.8817
0.1266	35.0	26250	0.3227	0.88
0.1495	36.0	27000	0.3271	0.8817
0.1021	37.0	27750	0.3248	0.8833
0.1616	38.0	28500	0.3242	0.885
0.158	39.0	29250	0.3254	0.88
0.1668	40.0	30000	0.3256	0.8833
0.1276	41.0	30750	0.3297	0.88
0.1072	42.0	31500	0.3307	0.88
0.1457	43.0	32250	0.3289	0.8783
0.1691	44.0	33000	0.3278	0.8817
0.1442	45.0	33750	0.3288	0.88
0.1231	46.0	34500	0.3279	0.88
0.1011	47.0	35250	0.3276	0.8767
0.1059	48.0	36000	0.3287	0.8767
0.1263	49.0	36750	0.3292	0.8767
0.1053	50.0	37500	0.3291	0.8767

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2