metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_tiny_adamax_00001_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: 0.7857142857142857

hushem_5x_deit_tiny_adamax_00001_fold4

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: 0.4803
Accuracy: 0.7857

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: 1e-05
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
1.3199	1.0	28	1.2249	0.5
1.0134	2.0	56	1.0887	0.5476
0.8677	3.0	84	1.0102	0.6190
0.708	4.0	112	0.9014	0.5952
0.5921	5.0	140	0.8309	0.6429
0.5207	6.0	168	0.7657	0.6905
0.3875	7.0	196	0.7178	0.6667
0.3518	8.0	224	0.6618	0.6667
0.2677	9.0	252	0.6279	0.7143
0.2022	10.0	280	0.5907	0.7381
0.2088	11.0	308	0.5564	0.7857
0.1641	12.0	336	0.5320	0.7857
0.1049	13.0	364	0.5289	0.7857
0.092	14.0	392	0.5023	0.8095
0.0557	15.0	420	0.4953	0.7381
0.0471	16.0	448	0.4998	0.7857
0.0348	17.0	476	0.4480	0.8095
0.0266	18.0	504	0.4459	0.7857
0.0161	19.0	532	0.4594	0.7857
0.0135	20.0	560	0.4976	0.7619
0.0093	21.0	588	0.4434	0.7619
0.0077	22.0	616	0.4474	0.7619
0.0056	23.0	644	0.4598	0.7143
0.0046	24.0	672	0.4362	0.7381
0.0037	25.0	700	0.4189	0.7857
0.0032	26.0	728	0.4491	0.7857
0.0028	27.0	756	0.4480	0.7857
0.0026	28.0	784	0.4540	0.7857
0.0022	29.0	812	0.4510	0.7857
0.0021	30.0	840	0.4557	0.8095
0.0018	31.0	868	0.4556	0.7857
0.0017	32.0	896	0.4590	0.8095
0.0016	33.0	924	0.4610	0.8095
0.0015	34.0	952	0.4618	0.8095
0.0015	35.0	980	0.4661	0.8095
0.0013	36.0	1008	0.4626	0.8095
0.0012	37.0	1036	0.4685	0.8095
0.0013	38.0	1064	0.4710	0.8095
0.0012	39.0	1092	0.4730	0.8095
0.0011	40.0	1120	0.4760	0.7857
0.0011	41.0	1148	0.4762	0.7857
0.001	42.0	1176	0.4741	0.8095
0.0011	43.0	1204	0.4784	0.8095
0.001	44.0	1232	0.4806	0.7857
0.001	45.0	1260	0.4792	0.7857
0.001	46.0	1288	0.4801	0.7857
0.001	47.0	1316	0.4802	0.7857
0.0009	48.0	1344	0.4803	0.7857
0.001	49.0	1372	0.4803	0.7857
0.001	50.0	1400	0.4803	0.7857

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0