metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_small_sgd_00001_fold2
    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: 0.2

hushem_5x_deit_small_sgd_00001_fold2

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: 1.4921
Accuracy: 0.2

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
1.5247	1.0	27	1.5122	0.1778
1.5208	2.0	54	1.5113	0.1778
1.5431	3.0	81	1.5104	0.1778
1.5874	4.0	108	1.5095	0.1778
1.5185	5.0	135	1.5086	0.1778
1.5124	6.0	162	1.5078	0.1778
1.4656	7.0	189	1.5070	0.1778
1.5113	8.0	216	1.5062	0.1778
1.5043	9.0	243	1.5054	0.1778
1.505	10.0	270	1.5047	0.1778
1.4599	11.0	297	1.5040	0.1778
1.5036	12.0	324	1.5033	0.1778
1.5237	13.0	351	1.5026	0.1778
1.511	14.0	378	1.5019	0.1778
1.5324	15.0	405	1.5013	0.1778
1.5272	16.0	432	1.5007	0.1778
1.5263	17.0	459	1.5002	0.1778
1.4937	18.0	486	1.4996	0.1778
1.5117	19.0	513	1.4991	0.1778
1.516	20.0	540	1.4985	0.1778
1.5298	21.0	567	1.4981	0.1778
1.5031	22.0	594	1.4976	0.1778
1.496	23.0	621	1.4971	0.1778
1.4984	24.0	648	1.4967	0.2
1.4849	25.0	675	1.4963	0.2
1.5277	26.0	702	1.4959	0.2
1.4813	27.0	729	1.4955	0.2
1.5008	28.0	756	1.4952	0.2
1.5143	29.0	783	1.4948	0.2
1.5063	30.0	810	1.4945	0.2
1.5197	31.0	837	1.4942	0.2
1.4689	32.0	864	1.4940	0.2
1.5261	33.0	891	1.4937	0.2
1.5047	34.0	918	1.4935	0.2
1.4608	35.0	945	1.4933	0.2
1.5134	36.0	972	1.4931	0.2
1.4999	37.0	999	1.4929	0.2
1.4901	38.0	1026	1.4928	0.2
1.4933	39.0	1053	1.4926	0.2
1.5285	40.0	1080	1.4925	0.2
1.5189	41.0	1107	1.4924	0.2
1.5357	42.0	1134	1.4923	0.2
1.5726	43.0	1161	1.4923	0.2
1.4926	44.0	1188	1.4922	0.2
1.4915	45.0	1215	1.4922	0.2
1.4934	46.0	1242	1.4921	0.2
1.5214	47.0	1269	1.4921	0.2
1.5071	48.0	1296	1.4921	0.2
1.5711	49.0	1323	1.4921	0.2
1.4665	50.0	1350	1.4921	0.2

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0