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_0001_fold1
    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: 0.6193656093489148

smids_1x_deit_small_sgd_0001_fold1

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: 0.9238
Accuracy: 0.6194

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
1.1708	1.0	76	1.1671	0.2154
1.1329	2.0	152	1.1541	0.2421
1.1491	3.0	228	1.1427	0.2538
1.1383	4.0	304	1.1321	0.2654
1.095	5.0	380	1.1227	0.2838
1.1047	6.0	456	1.1138	0.3055
1.1073	7.0	532	1.1055	0.3322
1.1015	8.0	608	1.0974	0.3456
1.0955	9.0	684	1.0899	0.3639
1.0349	10.0	760	1.0825	0.3706
1.0617	11.0	836	1.0752	0.3957
1.0611	12.0	912	1.0681	0.4140
1.0517	13.0	988	1.0610	0.4240
1.0458	14.0	1064	1.0541	0.4357
1.0495	15.0	1140	1.0471	0.4391
1.032	16.0	1216	1.0402	0.4457
1.0199	17.0	1292	1.0334	0.4608
1.0216	18.0	1368	1.0267	0.4691
1.0137	19.0	1444	1.0202	0.4825
1.0117	20.0	1520	1.0136	0.5058
0.9951	21.0	1596	1.0073	0.5142
0.9978	22.0	1672	1.0011	0.5175
0.9715	23.0	1748	0.9952	0.5242
0.9775	24.0	1824	0.9895	0.5392
0.9696	25.0	1900	0.9841	0.5409
0.9601	26.0	1976	0.9789	0.5492
0.9807	27.0	2052	0.9740	0.5593
0.9357	28.0	2128	0.9694	0.5626
0.9396	29.0	2204	0.9650	0.5710
0.9629	30.0	2280	0.9608	0.5743
0.9473	31.0	2356	0.9570	0.5826
0.9153	32.0	2432	0.9532	0.5860
0.9343	33.0	2508	0.9497	0.5927
0.953	34.0	2584	0.9465	0.5993
0.949	35.0	2660	0.9435	0.6027
0.9108	36.0	2736	0.9407	0.6043
0.9432	37.0	2812	0.9382	0.6060
0.9019	38.0	2888	0.9358	0.6093
0.9269	39.0	2964	0.9337	0.6093
0.9369	40.0	3040	0.9318	0.6110
0.8967	41.0	3116	0.9301	0.6110
0.9379	42.0	3192	0.9286	0.6144
0.8809	43.0	3268	0.9273	0.6160
0.9188	44.0	3344	0.9263	0.6177
0.8925	45.0	3420	0.9254	0.6177
0.8985	46.0	3496	0.9247	0.6177
0.9127	47.0	3572	0.9242	0.6194
0.9099	48.0	3648	0.9239	0.6194
0.9072	49.0	3724	0.9238	0.6194
0.9001	50.0	3800	0.9238	0.6194

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0