metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_small_adamax_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: 0.6511627906976745

hushem_1x_deit_small_adamax_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: 0.6553
Accuracy: 0.6512

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
No log	1.0	6	1.3641	0.3953
1.3358	2.0	12	1.2934	0.4186
1.3358	3.0	18	1.2307	0.4419
1.1053	4.0	24	1.1728	0.5814
0.9503	5.0	30	1.1200	0.5814
0.9503	6.0	36	1.0691	0.5814
0.8249	7.0	42	1.0268	0.6047
0.8249	8.0	48	1.0002	0.6279
0.6991	9.0	54	0.9588	0.6279
0.62	10.0	60	0.9254	0.6279
0.62	11.0	66	0.8988	0.6744
0.5003	12.0	72	0.8718	0.6279
0.5003	13.0	78	0.8636	0.6279
0.4251	14.0	84	0.8486	0.6279
0.3584	15.0	90	0.8228	0.6279
0.3584	16.0	96	0.8029	0.6512
0.2955	17.0	102	0.7980	0.6279
0.2955	18.0	108	0.7871	0.6047
0.2345	19.0	114	0.7646	0.6279
0.2022	20.0	120	0.7571	0.6279
0.2022	21.0	126	0.7433	0.6512
0.1667	22.0	132	0.7314	0.6744
0.1667	23.0	138	0.7263	0.6279
0.1461	24.0	144	0.7221	0.6744
0.1251	25.0	150	0.7120	0.6512
0.1251	26.0	156	0.6954	0.6512
0.1033	27.0	162	0.6904	0.6512
0.1033	28.0	168	0.6870	0.6744
0.0941	29.0	174	0.6821	0.6744
0.0792	30.0	180	0.6785	0.6744
0.0792	31.0	186	0.6761	0.6744
0.0681	32.0	192	0.6723	0.6744
0.0681	33.0	198	0.6679	0.6744
0.0621	34.0	204	0.6648	0.6512
0.0554	35.0	210	0.6628	0.6512
0.0554	36.0	216	0.6584	0.6744
0.0533	37.0	222	0.6569	0.6744
0.0533	38.0	228	0.6569	0.6512
0.0487	39.0	234	0.6565	0.6512
0.0478	40.0	240	0.6552	0.6512
0.0478	41.0	246	0.6553	0.6512
0.0459	42.0	252	0.6553	0.6512
0.0459	43.0	258	0.6553	0.6512
0.0488	44.0	264	0.6553	0.6512
0.0454	45.0	270	0.6553	0.6512
0.0454	46.0	276	0.6553	0.6512
0.0445	47.0	282	0.6553	0.6512
0.0445	48.0	288	0.6553	0.6512
0.0487	49.0	294	0.6553	0.6512
0.0463	50.0	300	0.6553	0.6512

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1