metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_sgd_0001_fold4
    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: 0.8233333333333334

smids_5x_deit_small_sgd_0001_fold4

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: 0.4830
Accuracy: 0.8233

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
1.0422	1.0	375	1.0355	0.445
0.9877	2.0	750	0.9987	0.5017
0.9301	3.0	1125	0.9591	0.5367
0.9069	4.0	1500	0.9204	0.5917
0.8815	5.0	1875	0.8838	0.6217
0.8208	6.0	2250	0.8478	0.6383
0.7819	7.0	2625	0.8141	0.6817
0.7955	8.0	3000	0.7823	0.7033
0.7492	9.0	3375	0.7528	0.7233
0.7403	10.0	3750	0.7259	0.7317
0.7047	11.0	4125	0.7009	0.745
0.6669	12.0	4500	0.6790	0.76
0.6557	13.0	4875	0.6594	0.7667
0.6563	14.0	5250	0.6418	0.77
0.5999	15.0	5625	0.6263	0.7667
0.589	16.0	6000	0.6125	0.77
0.5618	17.0	6375	0.5999	0.7767
0.5666	18.0	6750	0.5885	0.7817
0.6067	19.0	7125	0.5784	0.7867
0.5796	20.0	7500	0.5694	0.79
0.547	21.0	7875	0.5612	0.7883
0.5698	22.0	8250	0.5540	0.7867
0.5377	23.0	8625	0.5473	0.7917
0.5508	24.0	9000	0.5411	0.7967
0.5752	25.0	9375	0.5355	0.7983
0.5019	26.0	9750	0.5303	0.8
0.5146	27.0	10125	0.5255	0.8017
0.5114	28.0	10500	0.5210	0.8033
0.4588	29.0	10875	0.5170	0.8033
0.5045	30.0	11250	0.5133	0.805
0.5118	31.0	11625	0.5098	0.805
0.4619	32.0	12000	0.5067	0.8083
0.4796	33.0	12375	0.5037	0.81
0.5217	34.0	12750	0.5011	0.81
0.4423	35.0	13125	0.4986	0.8133
0.4692	36.0	13500	0.4964	0.815
0.4889	37.0	13875	0.4944	0.815
0.487	38.0	14250	0.4925	0.82
0.5206	39.0	14625	0.4909	0.82
0.4988	40.0	15000	0.4894	0.82
0.4485	41.0	15375	0.4881	0.8217
0.4284	42.0	15750	0.4870	0.8217
0.4979	43.0	16125	0.4860	0.8217
0.454	44.0	16500	0.4851	0.8217
0.4865	45.0	16875	0.4845	0.8217
0.4847	46.0	17250	0.4839	0.8217
0.5681	47.0	17625	0.4835	0.8217
0.4795	48.0	18000	0.4832	0.8217
0.4757	49.0	18375	0.4831	0.8233
0.4471	50.0	18750	0.4830	0.8233

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2