metadata

license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_base_adamax_001_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: 0.89

smids_5x_deit_base_adamax_001_fold3

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.1005
Accuracy: 0.89

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
0.307	1.0	375	0.3243	0.88
0.1951	2.0	750	0.2848	0.8967
0.1756	3.0	1125	0.3260	0.8767
0.1301	4.0	1500	0.3461	0.8933
0.1724	5.0	1875	0.3433	0.8783
0.1105	6.0	2250	0.5327	0.8517
0.105	7.0	2625	0.4495	0.89
0.1373	8.0	3000	0.3477	0.8933
0.0545	9.0	3375	0.5403	0.8767
0.026	10.0	3750	0.6392	0.8717
0.0547	11.0	4125	0.6160	0.875
0.0385	12.0	4500	0.5572	0.885
0.0376	13.0	4875	0.6146	0.8967
0.0031	14.0	5250	0.6509	0.8883
0.0185	15.0	5625	0.6515	0.885
0.0353	16.0	6000	0.7637	0.885
0.0052	17.0	6375	0.7211	0.8817
0.011	18.0	6750	0.5915	0.9067
0.0053	19.0	7125	0.6576	0.89
0.0044	20.0	7500	0.6728	0.8983
0.0003	21.0	7875	0.7362	0.8817
0.0001	22.0	8250	0.7370	0.8817
0.0265	23.0	8625	0.6954	0.895
0.0011	24.0	9000	0.7244	0.8883
0.0056	25.0	9375	0.7383	0.8917
0.0	26.0	9750	0.6944	0.9033
0.0001	27.0	10125	0.8581	0.8933
0.0002	28.0	10500	0.7732	0.8917
0.0001	29.0	10875	0.9540	0.8867
0.005	30.0	11250	0.8145	0.8933
0.0003	31.0	11625	0.8223	0.8967
0.0	32.0	12000	0.8225	0.89
0.0	33.0	12375	0.8479	0.8933
0.0	34.0	12750	0.8571	0.895
0.0	35.0	13125	0.9119	0.8917
0.0	36.0	13500	0.9029	0.8917
0.0	37.0	13875	0.9226	0.8967
0.0	38.0	14250	0.9083	0.895
0.0	39.0	14625	1.0048	0.8933
0.0026	40.0	15000	1.0018	0.8883
0.0	41.0	15375	1.0177	0.8917
0.0	42.0	15750	1.0273	0.8917
0.0	43.0	16125	1.0393	0.8933
0.0	44.0	16500	1.0649	0.895
0.0	45.0	16875	1.0825	0.8883
0.0	46.0	17250	1.0743	0.895
0.0	47.0	17625	1.0848	0.8917
0.0	48.0	18000	1.0902	0.8917
0.0	49.0	18375	1.0954	0.89
0.0	50.0	18750	1.1005	0.89

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2