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_0001_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: 0.9066666666666666

smids_5x_deit_base_adamax_0001_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: 0.9417
Accuracy: 0.9067

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: 0.0001
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
0.2147	1.0	375	0.4092	0.8317
0.1408	2.0	750	0.3269	0.915
0.0228	3.0	1125	0.4567	0.9067
0.0111	4.0	1500	0.5788	0.9033
0.0156	5.0	1875	0.6062	0.9
0.0288	6.0	2250	0.6656	0.8917
0.0007	7.0	2625	0.6456	0.9017
0.0002	8.0	3000	0.6407	0.8917
0.0002	9.0	3375	0.6824	0.9083
0.0084	10.0	3750	0.6593	0.905
0.0203	11.0	4125	0.7617	0.9017
0.0033	12.0	4500	0.7022	0.9167
0.0	13.0	4875	0.8023	0.9033
0.0	14.0	5250	0.8062	0.9083
0.0	15.0	5625	0.8735	0.905
0.0293	16.0	6000	0.8124	0.9133
0.0	17.0	6375	0.8110	0.915
0.0	18.0	6750	0.7934	0.9167
0.0	19.0	7125	0.8257	0.9117
0.0	20.0	7500	0.8169	0.905
0.0	21.0	7875	0.7971	0.9167
0.0	22.0	8250	0.8206	0.905
0.0031	23.0	8625	0.8887	0.9067
0.0	24.0	9000	0.8570	0.91
0.0	25.0	9375	0.9027	0.9017
0.0	26.0	9750	0.8809	0.9067
0.0	27.0	10125	0.8772	0.9083
0.0	28.0	10500	0.8815	0.9083
0.0	29.0	10875	0.8462	0.91
0.0028	30.0	11250	0.8854	0.9083
0.0	31.0	11625	0.8584	0.9083
0.0	32.0	12000	0.8933	0.905
0.0	33.0	12375	0.8718	0.9083
0.0	34.0	12750	0.8798	0.9067
0.0	35.0	13125	0.8653	0.9083
0.0	36.0	13500	0.8742	0.9133
0.0	37.0	13875	0.8914	0.9083
0.0	38.0	14250	0.8921	0.91
0.0	39.0	14625	0.9001	0.9083
0.0025	40.0	15000	0.9101	0.9083
0.0	41.0	15375	0.9161	0.9067
0.0	42.0	15750	0.9182	0.9083
0.0	43.0	16125	0.9246	0.905
0.0	44.0	16500	0.9291	0.9083
0.0	45.0	16875	0.9302	0.9067
0.0	46.0	17250	0.9341	0.9067
0.0	47.0	17625	0.9378	0.9067
0.0	48.0	18000	0.9402	0.9067
0.0	49.0	18375	0.9417	0.9067
0.0	50.0	18750	0.9417	0.9067

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2