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_rms_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: 0.7966666666666666

smids_5x_deit_small_rms_001_fold3

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: 0.5638
Accuracy: 0.7967

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
0.8408	1.0	375	0.8491	0.5383
0.836	2.0	750	0.8820	0.4983
0.8155	3.0	1125	0.8200	0.5917
0.829	4.0	1500	0.7980	0.5933
0.8032	5.0	1875	0.8027	0.5967
0.8095	6.0	2250	0.7557	0.635
0.7468	7.0	2625	0.7635	0.65
0.7133	8.0	3000	0.7025	0.6667
0.6424	9.0	3375	0.8608	0.6467
0.6511	10.0	3750	0.6834	0.6817
0.6928	11.0	4125	0.7883	0.6183
0.6757	12.0	4500	0.7380	0.635
0.6473	13.0	4875	0.6942	0.6633
0.5828	14.0	5250	0.6863	0.7117
0.5787	15.0	5625	0.6877	0.6933
0.5711	16.0	6000	0.7012	0.685
0.6198	17.0	6375	0.6000	0.7183
0.6331	18.0	6750	0.6316	0.7217
0.5457	19.0	7125	0.6381	0.7333
0.585	20.0	7500	0.6083	0.7367
0.4779	21.0	7875	0.6292	0.7
0.4504	22.0	8250	0.5995	0.7533
0.513	23.0	8625	0.6005	0.735
0.5931	24.0	9000	0.5450	0.76
0.4836	25.0	9375	0.5749	0.7517
0.4981	26.0	9750	0.5577	0.77
0.5035	27.0	10125	0.5452	0.7583
0.4996	28.0	10500	0.5583	0.765
0.4767	29.0	10875	0.5589	0.765
0.4202	30.0	11250	0.5291	0.78
0.4307	31.0	11625	0.5250	0.7967
0.5107	32.0	12000	0.5223	0.7917
0.4923	33.0	12375	0.5101	0.7917
0.4996	34.0	12750	0.5329	0.79
0.3762	35.0	13125	0.5542	0.79
0.4379	36.0	13500	0.5598	0.7883
0.4018	37.0	13875	0.5521	0.7983
0.4033	38.0	14250	0.5506	0.7767
0.4228	39.0	14625	0.5150	0.7917
0.366	40.0	15000	0.5580	0.8017
0.3549	41.0	15375	0.5360	0.8067
0.3677	42.0	15750	0.5521	0.8
0.4255	43.0	16125	0.5412	0.8033
0.355	44.0	16500	0.5640	0.7717
0.3586	45.0	16875	0.5441	0.7783
0.3404	46.0	17250	0.5592	0.7867
0.3867	47.0	17625	0.5593	0.8
0.3586	48.0	18000	0.5571	0.8067
0.2696	49.0	18375	0.5541	0.8
0.3761	50.0	18750	0.5638	0.7967

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2