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_adamax_0001_fold5
    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: 0.905

smids_10x_deit_tiny_adamax_0001_fold5

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: 0.9737
Accuracy: 0.905

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
0.2005	1.0	750	0.2656	0.9017
0.1545	2.0	1500	0.2591	0.905
0.0978	3.0	2250	0.4162	0.8983
0.0801	4.0	3000	0.4834	0.8967
0.0206	5.0	3750	0.5823	0.8983
0.0031	6.0	4500	0.5789	0.91
0.063	7.0	5250	0.6700	0.905
0.0223	8.0	6000	0.7780	0.9
0.0158	9.0	6750	0.7077	0.91
0.0007	10.0	7500	0.8470	0.9133
0.0055	11.0	8250	0.8576	0.8983
0.0	12.0	9000	0.8487	0.9117
0.0005	13.0	9750	1.0023	0.9
0.0	14.0	10500	0.8480	0.91
0.0003	15.0	11250	0.8518	0.91
0.0	16.0	12000	0.9913	0.9033
0.006	17.0	12750	0.9222	0.9033
0.0005	18.0	13500	0.9286	0.905
0.0012	19.0	14250	0.9239	0.905
0.0	20.0	15000	0.9679	0.9033
0.0004	21.0	15750	0.9229	0.9017
0.0	22.0	16500	0.9725	0.9033
0.0024	23.0	17250	0.9148	0.9133
0.0	24.0	18000	0.9300	0.9117
0.0	25.0	18750	0.9072	0.9067
0.0	26.0	19500	0.9306	0.9017
0.0	27.0	20250	0.8933	0.9117
0.0	28.0	21000	0.8908	0.91
0.0	29.0	21750	0.9728	0.9
0.0036	30.0	22500	0.9189	0.9067
0.0	31.0	23250	0.9100	0.9133
0.0	32.0	24000	0.9330	0.9067
0.0	33.0	24750	0.9244	0.9083
0.0	34.0	25500	0.9397	0.9067
0.0	35.0	26250	0.9397	0.9083
0.0	36.0	27000	0.9534	0.9067
0.0047	37.0	27750	0.9577	0.9067
0.0	38.0	28500	0.9431	0.9067
0.0	39.0	29250	0.9640	0.905
0.0	40.0	30000	0.9546	0.9067
0.0	41.0	30750	0.9625	0.9033
0.0	42.0	31500	0.9609	0.905
0.0	43.0	32250	0.9631	0.905
0.0	44.0	33000	0.9649	0.905
0.0	45.0	33750	0.9667	0.905
0.0	46.0	34500	0.9694	0.905
0.0	47.0	35250	0.9714	0.905
0.0	48.0	36000	0.9719	0.905
0.0	49.0	36750	0.9733	0.905
0.0	50.0	37500	0.9737	0.905

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2