metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_tiny_rms_00001_fold1
    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: 0.8631051752921536

smids_1x_deit_tiny_rms_00001_fold1

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: 0.8877
Accuracy: 0.8631

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
0.4093	1.0	76	0.4066	0.8264
0.3822	2.0	152	0.3553	0.8614
0.1979	3.0	228	0.3399	0.8631
0.1648	4.0	304	0.3252	0.8815
0.0965	5.0	380	0.3551	0.8531
0.072	6.0	456	0.4036	0.8631
0.0292	7.0	532	0.4208	0.8598
0.0237	8.0	608	0.5314	0.8497
0.0407	9.0	684	0.5484	0.8497
0.0074	10.0	760	0.5780	0.8715
0.0366	11.0	836	0.5799	0.8631
0.0022	12.0	912	0.8054	0.8414
0.0514	13.0	988	0.5849	0.8748
0.0003	14.0	1064	0.6713	0.8664
0.0448	15.0	1140	0.6921	0.8715
0.0014	16.0	1216	0.6848	0.8631
0.0001	17.0	1292	0.7084	0.8648
0.0152	18.0	1368	0.8109	0.8681
0.0001	19.0	1444	0.7361	0.8698
0.004	20.0	1520	0.7743	0.8664
0.0035	21.0	1596	0.7272	0.8748
0.0282	22.0	1672	0.7515	0.8731
0.0001	23.0	1748	0.8060	0.8581
0.0001	24.0	1824	0.7763	0.8581
0.0156	25.0	1900	0.7302	0.8831
0.0068	26.0	1976	0.8763	0.8514
0.0045	27.0	2052	0.8144	0.8664
0.0058	28.0	2128	0.7716	0.8614
0.009	29.0	2204	0.8016	0.8664
0.0	30.0	2280	0.8234	0.8631
0.0087	31.0	2356	0.8420	0.8631
0.0102	32.0	2432	0.8218	0.8698
0.0	33.0	2508	0.8439	0.8564
0.0	34.0	2584	0.8448	0.8598
0.0154	35.0	2660	0.8638	0.8631
0.0044	36.0	2736	0.8664	0.8715
0.0088	37.0	2812	0.8649	0.8598
0.0	38.0	2888	0.8771	0.8598
0.0028	39.0	2964	0.8789	0.8631
0.0	40.0	3040	0.8645	0.8648
0.0044	41.0	3116	0.8681	0.8664
0.0	42.0	3192	0.8746	0.8631
0.0056	43.0	3268	0.8786	0.8664
0.0	44.0	3344	0.8858	0.8648
0.0	45.0	3420	0.8848	0.8648
0.0	46.0	3496	0.8858	0.8648
0.0	47.0	3572	0.8868	0.8631
0.0023	48.0	3648	0.8879	0.8631
0.0	49.0	3724	0.8884	0.8631
0.0	50.0	3800	0.8877	0.8631

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0