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_tiny_adamax_001_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: 0.8966666666666666

smids_5x_deit_tiny_adamax_001_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: 1.0054
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: 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
0.4039	1.0	375	0.4314	0.825
0.345	2.0	750	0.2900	0.8967
0.2226	3.0	1125	0.4260	0.8533
0.2463	4.0	1500	0.4152	0.84
0.132	5.0	1875	0.3792	0.8767
0.0976	6.0	2250	0.4696	0.8783
0.1565	7.0	2625	0.3805	0.8833
0.0928	8.0	3000	0.5026	0.8867
0.0864	9.0	3375	0.4372	0.8817
0.0498	10.0	3750	0.5601	0.8617
0.0645	11.0	4125	0.5273	0.8983
0.0761	12.0	4500	0.5529	0.8867
0.0594	13.0	4875	0.5381	0.88
0.039	14.0	5250	0.6308	0.895
0.0193	15.0	5625	0.5515	0.89
0.0113	16.0	6000	0.7221	0.8667
0.008	17.0	6375	0.6792	0.9017
0.0388	18.0	6750	0.7109	0.8883
0.0009	19.0	7125	0.6719	0.8967
0.023	20.0	7500	0.7328	0.8917
0.0093	21.0	7875	0.8033	0.8917
0.0223	22.0	8250	0.6700	0.8983
0.0002	23.0	8625	0.7140	0.8967
0.0504	24.0	9000	0.8112	0.9
0.0006	25.0	9375	0.7975	0.8833
0.0027	26.0	9750	0.9326	0.885
0.0053	27.0	10125	0.8820	0.8817
0.0	28.0	10500	0.9014	0.89
0.0046	29.0	10875	0.8788	0.8917
0.0025	30.0	11250	0.9368	0.8967
0.0	31.0	11625	0.9273	0.895
0.0	32.0	12000	1.0112	0.8817
0.0014	33.0	12375	0.8964	0.89
0.0	34.0	12750	0.9035	0.8983
0.0	35.0	13125	0.9036	0.8983
0.0	36.0	13500	0.9211	0.8983
0.0	37.0	13875	0.9473	0.9033
0.0039	38.0	14250	0.9999	0.8883
0.0	39.0	14625	0.9726	0.8967
0.0	40.0	15000	0.9581	0.8967
0.0	41.0	15375	0.9592	0.9
0.0	42.0	15750	0.9776	0.9
0.0	43.0	16125	0.9762	0.8967
0.0	44.0	16500	0.9706	0.8983
0.0	45.0	16875	0.9775	0.8967
0.0	46.0	17250	0.9930	0.8983
0.0029	47.0	17625	0.9955	0.8983
0.0	48.0	18000	1.0006	0.8983
0.0	49.0	18375	1.0043	0.8967
0.0021	50.0	18750	1.0054	0.8967

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2