metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_rms_0001_fold1
    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: 0.5555555555555556

hushem_1x_deit_tiny_rms_0001_fold1

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: 3.4166
Accuracy: 0.5556

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
No log	1.0	6	2.1314	0.2444
2.0481	2.0	12	1.5573	0.2444
2.0481	3.0	18	1.4598	0.2444
1.5099	4.0	24	1.4194	0.2444
1.4253	5.0	30	1.3528	0.2667
1.4253	6.0	36	1.6348	0.2444
1.3319	7.0	42	1.3901	0.4444
1.3319	8.0	48	1.3151	0.2889
1.2142	9.0	54	1.3395	0.3333
1.1416	10.0	60	1.4176	0.3556
1.1416	11.0	66	1.9072	0.2667
0.9889	12.0	72	1.7446	0.3111
0.9889	13.0	78	1.4748	0.3778
0.8552	14.0	84	1.7450	0.3778
0.6798	15.0	90	1.6042	0.4889
0.6798	16.0	96	1.5863	0.4222
0.563	17.0	102	1.9311	0.4
0.563	18.0	108	1.9509	0.4444
0.3845	19.0	114	2.1256	0.4667
0.2041	20.0	120	2.4131	0.4889
0.2041	21.0	126	2.1029	0.4667
0.1874	22.0	132	2.0412	0.5778
0.1874	23.0	138	2.4952	0.4889
0.0735	24.0	144	2.8992	0.4667
0.0229	25.0	150	2.7495	0.5556
0.0229	26.0	156	3.2879	0.4667
0.0293	27.0	162	3.1526	0.5111
0.0293	28.0	168	3.0123	0.5333
0.0023	29.0	174	3.0812	0.5556
0.0008	30.0	180	3.1384	0.5556
0.0008	31.0	186	3.2017	0.5556
0.0005	32.0	192	3.2443	0.5556
0.0005	33.0	198	3.2806	0.5556
0.0005	34.0	204	3.3167	0.5556
0.0004	35.0	210	3.3393	0.5556
0.0004	36.0	216	3.3662	0.5556
0.0004	37.0	222	3.3843	0.5556
0.0004	38.0	228	3.3970	0.5556
0.0003	39.0	234	3.4053	0.5556
0.0003	40.0	240	3.4123	0.5556
0.0003	41.0	246	3.4159	0.5556
0.0003	42.0	252	3.4166	0.5556
0.0003	43.0	258	3.4166	0.5556
0.0003	44.0	264	3.4166	0.5556
0.0003	45.0	270	3.4166	0.5556
0.0003	46.0	276	3.4166	0.5556
0.0003	47.0	282	3.4166	0.5556
0.0003	48.0	288	3.4166	0.5556
0.0003	49.0	294	3.4166	0.5556
0.0003	50.0	300	3.4166	0.5556

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1