metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_tiny_rms_00001_fold3
    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: 0.8916666666666667

smids_1x_deit_tiny_rms_00001_fold3

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: 0.7866
Accuracy: 0.8917

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
0.4569	1.0	75	0.3524	0.8733
0.257	2.0	150	0.3177	0.8783
0.228	3.0	225	0.2830	0.895
0.1874	4.0	300	0.2625	0.9133
0.0988	5.0	375	0.3112	0.8867
0.0547	6.0	450	0.3480	0.895
0.0671	7.0	525	0.4401	0.8783
0.0314	8.0	600	0.4835	0.8917
0.0373	9.0	675	0.4879	0.8983
0.007	10.0	750	0.5903	0.895
0.0283	11.0	825	0.5783	0.8867
0.0151	12.0	900	0.7372	0.8833
0.0012	13.0	975	0.6965	0.8783
0.0175	14.0	1050	0.6546	0.89
0.0013	15.0	1125	0.7058	0.8783
0.0001	16.0	1200	0.6811	0.8917
0.0007	17.0	1275	0.7469	0.8967
0.0153	18.0	1350	0.6408	0.8917
0.0082	19.0	1425	0.8396	0.8783
0.0026	20.0	1500	0.8283	0.8883
0.0001	21.0	1575	0.7596	0.89
0.0037	22.0	1650	0.8137	0.875
0.007	23.0	1725	0.7276	0.8833
0.0	24.0	1800	0.6779	0.9
0.0001	25.0	1875	0.7204	0.895
0.0081	26.0	1950	0.7595	0.8883
0.0	27.0	2025	0.7620	0.895
0.0	28.0	2100	0.7575	0.8867
0.0001	29.0	2175	0.7827	0.89
0.0	30.0	2250	0.7351	0.8917
0.0	31.0	2325	0.7715	0.89
0.0	32.0	2400	0.7652	0.8917
0.0	33.0	2475	0.7881	0.89
0.0066	34.0	2550	0.7810	0.89
0.0102	35.0	2625	0.8490	0.89
0.0026	36.0	2700	0.7973	0.885
0.0016	37.0	2775	0.7751	0.8983
0.0	38.0	2850	0.7861	0.8933
0.0	39.0	2925	0.7652	0.8917
0.0	40.0	3000	0.7874	0.8917
0.0	41.0	3075	0.7876	0.8883
0.0033	42.0	3150	0.7858	0.8917
0.0029	43.0	3225	0.7835	0.8917
0.0	44.0	3300	0.7876	0.8917
0.0023	45.0	3375	0.7887	0.8917
0.0023	46.0	3450	0.7887	0.8917
0.0053	47.0	3525	0.7882	0.8917
0.0	48.0	3600	0.7869	0.8917
0.0	49.0	3675	0.7873	0.8917
0.0047	50.0	3750	0.7866	0.8917

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0