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_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: 0.8883333333333333

smids_1x_deit_tiny_adamax_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: 0.7938
Accuracy: 0.8883

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
0.3497	1.0	75	0.3438	0.8583
0.2686	2.0	150	0.4010	0.835
0.2132	3.0	225	0.3099	0.895
0.1475	4.0	300	0.4693	0.8433
0.07	5.0	375	0.3951	0.8967
0.0348	6.0	450	0.4706	0.8833
0.1076	7.0	525	0.4897	0.8817
0.0371	8.0	600	0.5659	0.8817
0.0539	9.0	675	0.5525	0.8917
0.0289	10.0	750	0.6768	0.8783
0.053	11.0	825	0.5526	0.9017
0.0394	12.0	900	0.6804	0.8867
0.0028	13.0	975	0.6916	0.8717
0.0003	14.0	1050	0.6282	0.8983
0.0001	15.0	1125	0.6544	0.8883
0.0001	16.0	1200	0.6284	0.9
0.0001	17.0	1275	0.6590	0.89
0.0087	18.0	1350	0.7168	0.885
0.0041	19.0	1425	0.6749	0.8933
0.0108	20.0	1500	0.7413	0.885
0.0001	21.0	1575	0.6857	0.8867
0.0	22.0	1650	0.7103	0.8833
0.0055	23.0	1725	0.7459	0.8883
0.0	24.0	1800	0.7428	0.8833
0.0	25.0	1875	0.7179	0.885
0.0041	26.0	1950	0.7150	0.8883
0.0	27.0	2025	0.7370	0.8833
0.0	28.0	2100	0.7342	0.885
0.0	29.0	2175	0.7378	0.8833
0.0	30.0	2250	0.7711	0.8883
0.0	31.0	2325	0.7434	0.8883
0.0	32.0	2400	0.7467	0.8883
0.0	33.0	2475	0.7543	0.885
0.0049	34.0	2550	0.7604	0.885
0.0078	35.0	2625	0.7800	0.8833
0.0032	36.0	2700	0.7784	0.885
0.0024	37.0	2775	0.7619	0.89
0.0	38.0	2850	0.7714	0.89
0.0	39.0	2925	0.7703	0.8883
0.0	40.0	3000	0.7842	0.885
0.0	41.0	3075	0.7774	0.8867
0.0028	42.0	3150	0.7773	0.8883
0.003	43.0	3225	0.7810	0.8867
0.0	44.0	3300	0.7850	0.8867
0.0027	45.0	3375	0.7856	0.8867
0.0025	46.0	3450	0.7889	0.8883
0.0052	47.0	3525	0.7872	0.8883
0.0	48.0	3600	0.7928	0.8883
0.0	49.0	3675	0.7922	0.8867
0.0045	50.0	3750	0.7938	0.8883

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0