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_adamax_00001_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: 0.8666666666666667

smids_1x_deit_small_adamax_00001_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: 0.6794
Accuracy: 0.8667

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: 1e-05
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
0.6229	1.0	75	0.5343	0.7933
0.4114	2.0	150	0.3999	0.8333
0.3246	3.0	225	0.3573	0.835
0.2962	4.0	300	0.3318	0.8633
0.2498	5.0	375	0.3315	0.86
0.1818	6.0	450	0.3035	0.87
0.1932	7.0	525	0.3061	0.875
0.1474	8.0	600	0.3041	0.87
0.0826	9.0	675	0.3169	0.87
0.081	10.0	750	0.3163	0.8667
0.0813	11.0	825	0.3296	0.87
0.0261	12.0	900	0.3349	0.87
0.0464	13.0	975	0.3659	0.8667
0.0215	14.0	1050	0.4099	0.87
0.027	15.0	1125	0.4201	0.87
0.0112	16.0	1200	0.4420	0.8717
0.0162	17.0	1275	0.4669	0.8733
0.016	18.0	1350	0.5089	0.87
0.0103	19.0	1425	0.4963	0.8717
0.0014	20.0	1500	0.5125	0.8733
0.0096	21.0	1575	0.5220	0.8733
0.0069	22.0	1650	0.5718	0.8617
0.0159	23.0	1725	0.5556	0.8717
0.0008	24.0	1800	0.5732	0.8667
0.0352	25.0	1875	0.5727	0.8683
0.0005	26.0	1950	0.5893	0.8683
0.0095	27.0	2025	0.6176	0.8667
0.0202	28.0	2100	0.5996	0.865
0.0236	29.0	2175	0.6069	0.8633
0.0103	30.0	2250	0.6179	0.865
0.0003	31.0	2325	0.6857	0.8633
0.0003	32.0	2400	0.6471	0.8667
0.0077	33.0	2475	0.6466	0.8667
0.0003	34.0	2550	0.6723	0.8633
0.0076	35.0	2625	0.6448	0.865
0.0002	36.0	2700	0.6372	0.87
0.0037	37.0	2775	0.6601	0.87
0.0002	38.0	2850	0.6572	0.8683
0.0002	39.0	2925	0.6720	0.8683
0.0002	40.0	3000	0.6642	0.8683
0.0072	41.0	3075	0.6568	0.8683
0.0002	42.0	3150	0.6668	0.8633
0.0069	43.0	3225	0.6577	0.8683
0.0002	44.0	3300	0.6738	0.8667
0.0042	45.0	3375	0.6742	0.865
0.0002	46.0	3450	0.6815	0.8667
0.0111	47.0	3525	0.6812	0.8667
0.0112	48.0	3600	0.6837	0.8683
0.0002	49.0	3675	0.6809	0.8667
0.0038	50.0	3750	0.6794	0.8667

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0