metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_tiny_sgd_001_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: 0.895

smids_10x_deit_tiny_sgd_001_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: 0.2982
Accuracy: 0.895

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: 0.001
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
0.5867	1.0	750	0.5827	0.7783
0.3913	2.0	1500	0.4291	0.83
0.3437	3.0	2250	0.3734	0.86
0.3224	4.0	3000	0.3340	0.8633
0.3802	5.0	3750	0.3192	0.875
0.3066	6.0	4500	0.3104	0.88
0.2589	7.0	5250	0.2967	0.8867
0.2794	8.0	6000	0.2987	0.8867
0.1833	9.0	6750	0.2867	0.8933
0.2023	10.0	7500	0.2817	0.9
0.2616	11.0	8250	0.2809	0.8883
0.2286	12.0	9000	0.2812	0.8983
0.191	13.0	9750	0.2821	0.895
0.2573	14.0	10500	0.2824	0.895
0.233	15.0	11250	0.2788	0.9033
0.227	16.0	12000	0.2755	0.9133
0.2065	17.0	12750	0.2819	0.8933
0.1957	18.0	13500	0.2734	0.9033
0.1915	19.0	14250	0.2738	0.9017
0.1774	20.0	15000	0.2840	0.8967
0.1639	21.0	15750	0.2800	0.9
0.18	22.0	16500	0.2722	0.9033
0.1754	23.0	17250	0.2797	0.8983
0.1721	24.0	18000	0.2818	0.8967
0.2322	25.0	18750	0.2867	0.8933
0.1833	26.0	19500	0.2854	0.8933
0.0838	27.0	20250	0.2833	0.9083
0.1291	28.0	21000	0.2872	0.8883
0.1475	29.0	21750	0.2853	0.8933
0.1339	30.0	22500	0.2879	0.8917
0.0869	31.0	23250	0.2884	0.895
0.1341	32.0	24000	0.2859	0.89
0.1322	33.0	24750	0.2895	0.8933
0.1482	34.0	25500	0.2910	0.8933
0.1123	35.0	26250	0.2921	0.8933
0.1145	36.0	27000	0.2928	0.8933
0.1372	37.0	27750	0.2965	0.8917
0.1907	38.0	28500	0.2941	0.8917
0.1101	39.0	29250	0.2932	0.89
0.1502	40.0	30000	0.2921	0.895
0.1006	41.0	30750	0.2941	0.8983
0.1237	42.0	31500	0.2961	0.8967
0.0943	43.0	32250	0.2963	0.895
0.1038	44.0	33000	0.2980	0.8983
0.1286	45.0	33750	0.2956	0.8917
0.0851	46.0	34500	0.2954	0.8917
0.1551	47.0	35250	0.2984	0.8917
0.0707	48.0	36000	0.2985	0.8967
0.143	49.0	36750	0.2982	0.8967
0.1125	50.0	37500	0.2982	0.895

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2