README.md

metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_rms_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: 0.5813953488372093

hushem_1x_deit_tiny_rms_0001_fold3

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

• Loss: 2.2114
• Accuracy: 0.5814

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
No log	1.0	6	1.4590	0.2558
2.1915	2.0	12	1.4820	0.2558
2.1915	3.0	18	1.4635	0.3488
1.4733	4.0	24	1.6507	0.2558
1.4003	5.0	30	1.5038	0.2558
1.4003	6.0	36	1.5372	0.2093
1.28	7.0	42	1.4420	0.3023
1.28	8.0	48	1.3681	0.3488
1.2064	9.0	54	1.4133	0.3023
1.1588	10.0	60	1.2991	0.4419
1.1588	11.0	66	1.2547	0.4651
1.133	12.0	72	1.2924	0.4884
1.133	13.0	78	1.2566	0.4884
1.0357	14.0	84	1.1915	0.5349
0.8616	15.0	90	1.2058	0.5116
0.8616	16.0	96	1.1399	0.5349
0.6595	17.0	102	1.1462	0.5581
0.6595	18.0	108	1.2856	0.5116
0.501	19.0	114	1.1528	0.6047
0.3761	20.0	120	1.2487	0.6047
0.3761	21.0	126	1.9335	0.5581
0.1818	22.0	132	2.0855	0.5349
0.1818	23.0	138	2.8198	0.5349
0.0677	24.0	144	1.5837	0.6279
0.0703	25.0	150	2.1739	0.5116
0.0703	26.0	156	2.0640	0.5581
0.0053	27.0	162	2.0886	0.5814
0.0053	28.0	168	2.1352	0.5814
0.0006	29.0	174	2.1434	0.5814
0.0004	30.0	180	2.1524	0.5814
0.0004	31.0	186	2.1594	0.5814
0.0003	32.0	192	2.1659	0.5814
0.0003	33.0	198	2.1759	0.5814
0.0003	34.0	204	2.1825	0.5814
0.0003	35.0	210	2.1918	0.5814
0.0003	36.0	216	2.1964	0.5814
0.0002	37.0	222	2.2014	0.5814
0.0002	38.0	228	2.2049	0.5814
0.0002	39.0	234	2.2075	0.5814
0.0002	40.0	240	2.2099	0.5814
0.0002	41.0	246	2.2110	0.5814
0.0002	42.0	252	2.2114	0.5814
0.0002	43.0	258	2.2114	0.5814
0.0002	44.0	264	2.2114	0.5814
0.0002	45.0	270	2.2114	0.5814
0.0002	46.0	276	2.2114	0.5814
0.0002	47.0	282	2.2114	0.5814
0.0002	48.0	288	2.2114	0.5814
0.0002	49.0	294	2.2114	0.5814
0.0002	50.0	300	2.2114	0.5814

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1