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_rms_0001_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: 0.7483333333333333

smids_1x_deit_small_rms_0001_fold5

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: 1.2122
Accuracy: 0.7483

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
1.0524	1.0	75	0.9597	0.445
1.1247	2.0	150	1.1111	0.3367
0.979	3.0	225	0.9077	0.5
0.8898	4.0	300	0.8740	0.52
0.8714	5.0	375	0.9443	0.4433
0.8755	6.0	450	0.7908	0.5917
0.8257	7.0	525	0.8028	0.5817
0.7602	8.0	600	0.8435	0.605
0.7994	9.0	675	0.7977	0.6117
0.7424	10.0	750	0.7850	0.6117
0.8101	11.0	825	0.7616	0.6233
0.7712	12.0	900	0.7668	0.6367
0.7209	13.0	975	0.8101	0.62
0.7215	14.0	1050	0.7936	0.62
0.7097	15.0	1125	0.7953	0.61
0.7072	16.0	1200	0.7924	0.6317
0.7074	17.0	1275	0.7452	0.6667
0.6856	18.0	1350	0.7477	0.6717
0.6768	19.0	1425	0.7216	0.6783
0.6919	20.0	1500	0.7445	0.68
0.6145	21.0	1575	0.7497	0.6533
0.5852	22.0	1650	0.7462	0.7083
0.625	23.0	1725	0.7496	0.675
0.549	24.0	1800	0.7315	0.7067
0.5773	25.0	1875	0.7055	0.7033
0.5746	26.0	1950	0.6982	0.7283
0.5717	27.0	2025	0.7187	0.705
0.5927	28.0	2100	0.6996	0.7183
0.5713	29.0	2175	0.6989	0.7217
0.5709	30.0	2250	0.7204	0.7267
0.5164	31.0	2325	0.7778	0.705
0.5059	32.0	2400	0.7021	0.73
0.5725	33.0	2475	0.6873	0.735
0.4839	34.0	2550	0.6931	0.745
0.4617	35.0	2625	0.7517	0.75
0.4294	36.0	2700	0.8099	0.7533
0.3749	37.0	2775	0.7255	0.75
0.4163	38.0	2850	0.7476	0.7533
0.3565	39.0	2925	0.8354	0.735
0.382	40.0	3000	0.8201	0.7467
0.3261	41.0	3075	0.8167	0.7567
0.4372	42.0	3150	0.8428	0.7267
0.3484	43.0	3225	0.8996	0.74
0.3261	44.0	3300	0.9207	0.735
0.2963	45.0	3375	1.0220	0.7283
0.2143	46.0	3450	0.9860	0.755
0.2551	47.0	3525	1.1473	0.7333
0.1675	48.0	3600	1.1351	0.735
0.1431	49.0	3675	1.1685	0.75
0.1393	50.0	3750	1.2122	0.7483

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0