metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_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: 0.8966666666666666

smids_5x_deit_small_rms_00001_fold3

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: 1.0303
Accuracy: 0.8967

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
0.2375	1.0	375	0.2542	0.9083
0.1409	2.0	750	0.2770	0.91
0.0609	3.0	1125	0.3094	0.8983
0.0237	4.0	1500	0.3996	0.8983
0.0338	5.0	1875	0.5715	0.8883
0.0534	6.0	2250	0.6112	0.8933
0.0052	7.0	2625	0.6878	0.8933
0.0006	8.0	3000	0.6908	0.9
0.0283	9.0	3375	0.6671	0.8983
0.0065	10.0	3750	0.7326	0.9067
0.0065	11.0	4125	0.6436	0.915
0.0	12.0	4500	0.7498	0.9
0.0016	13.0	4875	0.8235	0.8933
0.0003	14.0	5250	0.8655	0.895
0.0082	15.0	5625	0.7486	0.9067
0.0001	16.0	6000	0.8142	0.9017
0.0001	17.0	6375	0.7387	0.8917
0.0001	18.0	6750	0.8424	0.9067
0.0	19.0	7125	0.7973	0.9017
0.0378	20.0	7500	0.7948	0.8967
0.0	21.0	7875	0.8629	0.8917
0.0	22.0	8250	0.7939	0.9017
0.0063	23.0	8625	0.8369	0.89
0.0	24.0	9000	0.8848	0.9
0.0	25.0	9375	0.8284	0.91
0.0	26.0	9750	0.9412	0.9
0.0	27.0	10125	0.8363	0.905
0.0	28.0	10500	0.9351	0.8917
0.0	29.0	10875	0.8734	0.9033
0.0037	30.0	11250	0.9770	0.9067
0.0	31.0	11625	0.8887	0.905
0.0	32.0	12000	0.9455	0.9
0.0	33.0	12375	0.9432	0.9033
0.0	34.0	12750	0.9703	0.8983
0.0	35.0	13125	0.9495	0.9067
0.0	36.0	13500	0.9886	0.8983
0.0	37.0	13875	0.9999	0.9
0.0	38.0	14250	1.0388	0.8983
0.0	39.0	14625	1.0645	0.8917
0.0038	40.0	15000	0.9923	0.9017
0.0	41.0	15375	1.0132	0.8983
0.0	42.0	15750	1.0058	0.9017
0.0	43.0	16125	1.0185	0.8983
0.0	44.0	16500	1.0211	0.8967
0.0	45.0	16875	1.0174	0.8967
0.0	46.0	17250	1.0256	0.8967
0.0	47.0	17625	1.0251	0.8967
0.0	48.0	18000	1.0297	0.8967
0.0	49.0	18375	1.0318	0.8967
0.0	50.0	18750	1.0303	0.8967

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2