metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_small_sgd_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: 0.835

smids_10x_deit_small_sgd_0001_fold5

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: 0.4025
Accuracy: 0.835

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
0.999	1.0	750	1.0177	0.4867
0.9125	2.0	1500	0.9538	0.56
0.8354	3.0	2250	0.8848	0.64
0.7909	4.0	3000	0.8172	0.685
0.7315	5.0	3750	0.7535	0.7183
0.6641	6.0	4500	0.7023	0.7433
0.61	7.0	5250	0.6582	0.755
0.5883	8.0	6000	0.6232	0.7783
0.6057	9.0	6750	0.5936	0.79
0.5434	10.0	7500	0.5693	0.795
0.5298	11.0	8250	0.5500	0.7917
0.4881	12.0	9000	0.5324	0.8
0.5014	13.0	9750	0.5180	0.8
0.4862	14.0	10500	0.5060	0.8083
0.4712	15.0	11250	0.4949	0.81
0.4371	16.0	12000	0.4864	0.8117
0.4626	17.0	12750	0.4789	0.815
0.4294	18.0	13500	0.4706	0.815
0.4498	19.0	14250	0.4650	0.815
0.425	20.0	15000	0.4594	0.815
0.4212	21.0	15750	0.4532	0.8167
0.4517	22.0	16500	0.4489	0.82
0.4104	23.0	17250	0.4443	0.8167
0.4051	24.0	18000	0.4407	0.82
0.4019	25.0	18750	0.4371	0.8217
0.3884	26.0	19500	0.4338	0.825
0.3154	27.0	20250	0.4302	0.825
0.3994	28.0	21000	0.4273	0.8283
0.4061	29.0	21750	0.4246	0.83
0.4059	30.0	22500	0.4225	0.8283
0.3637	31.0	23250	0.4202	0.8267
0.3501	32.0	24000	0.4181	0.8283
0.4209	33.0	24750	0.4163	0.8317
0.3255	34.0	25500	0.4145	0.8317
0.3933	35.0	26250	0.4127	0.8317
0.3766	36.0	27000	0.4115	0.8317
0.3145	37.0	27750	0.4102	0.8317
0.3874	38.0	28500	0.4090	0.83
0.3898	39.0	29250	0.4079	0.83
0.365	40.0	30000	0.4069	0.8317
0.3728	41.0	30750	0.4059	0.8317
0.3865	42.0	31500	0.4051	0.8317
0.3813	43.0	32250	0.4045	0.8317
0.3607	44.0	33000	0.4040	0.8317
0.3955	45.0	33750	0.4034	0.8333
0.3317	46.0	34500	0.4031	0.835
0.4022	47.0	35250	0.4028	0.835
0.3888	48.0	36000	0.4026	0.835
0.3745	49.0	36750	0.4025	0.835
0.3	50.0	37500	0.4025	0.835

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2