README.md

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_lr0001_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: 0.5609756097560976

hushem_1x_deit_tiny_rms_lr0001_fold5

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: 2.1537
• Accuracy: 0.5610

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	1.5741	0.2683
1.8922	2.0	12	1.3978	0.2683
1.8922	3.0	18	1.4032	0.2439
1.5101	4.0	24	1.4021	0.2683
1.38	5.0	30	2.1528	0.2439
1.38	6.0	36	1.4141	0.2439
1.4096	7.0	42	1.2484	0.4390
1.4096	8.0	48	1.2607	0.4390
1.2381	9.0	54	0.9950	0.5366
1.1539	10.0	60	1.0350	0.5610
1.1539	11.0	66	1.2716	0.3415
0.9039	12.0	72	1.0596	0.5854
0.9039	13.0	78	1.5972	0.4146
0.6191	14.0	84	1.9855	0.4390
0.4358	15.0	90	1.2403	0.4878
0.4358	16.0	96	2.3374	0.4390
0.2291	17.0	102	1.5475	0.4390
0.2291	18.0	108	1.2789	0.6341
0.1203	19.0	114	1.8441	0.4390
0.0604	20.0	120	1.7948	0.4878
0.0604	21.0	126	2.0211	0.4634
0.0322	22.0	132	1.8178	0.5366
0.0322	23.0	138	2.0950	0.4878
0.017	24.0	144	2.0410	0.5122
0.0011	25.0	150	2.0405	0.5122
0.0011	26.0	156	2.0495	0.5122
0.0007	27.0	162	2.0594	0.5122
0.0007	28.0	168	2.0747	0.5122
0.0006	29.0	174	2.0825	0.5610
0.0005	30.0	180	2.0915	0.5610
0.0005	31.0	186	2.1017	0.5610
0.0004	32.0	192	2.1110	0.5610
0.0004	33.0	198	2.1199	0.5610
0.0004	34.0	204	2.1276	0.5610
0.0004	35.0	210	2.1335	0.5610
0.0004	36.0	216	2.1398	0.5610
0.0004	37.0	222	2.1439	0.5610
0.0004	38.0	228	2.1473	0.5610
0.0003	39.0	234	2.1497	0.5610
0.0003	40.0	240	2.1519	0.5610
0.0003	41.0	246	2.1532	0.5610
0.0003	42.0	252	2.1537	0.5610
0.0003	43.0	258	2.1537	0.5610
0.0003	44.0	264	2.1537	0.5610
0.0003	45.0	270	2.1537	0.5610
0.0003	46.0	276	2.1537	0.5610
0.0003	47.0	282	2.1537	0.5610
0.0003	48.0	288	2.1537	0.5610
0.0003	49.0	294	2.1537	0.5610
0.0003	50.0	300	2.1537	0.5610

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1