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_00001_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: 0.6585365853658537

hushem_1x_deit_tiny_rms_00001_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: 1.1280
• Accuracy: 0.6585

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
No log	1.0	6	1.2888	0.4390
1.3565	2.0	12	1.0130	0.5366
1.3565	3.0	18	0.9361	0.5366
0.667	4.0	24	0.8831	0.6585
0.2929	5.0	30	0.8739	0.5854
0.2929	6.0	36	0.9329	0.5854
0.1055	7.0	42	0.9159	0.6585
0.1055	8.0	48	1.0700	0.5854
0.04	9.0	54	1.0357	0.5854
0.013	10.0	60	0.9379	0.6585
0.013	11.0	66	0.9964	0.6341
0.0046	12.0	72	1.0009	0.6585
0.0046	13.0	78	0.9889	0.6585
0.0029	14.0	84	1.0074	0.6585
0.0023	15.0	90	1.0258	0.6585
0.0023	16.0	96	1.0330	0.6585
0.0018	17.0	102	1.0391	0.6585
0.0018	18.0	108	1.0476	0.6585
0.0015	19.0	114	1.0552	0.6585
0.0013	20.0	120	1.0615	0.6585
0.0013	21.0	126	1.0642	0.6585
0.0011	22.0	132	1.0600	0.6585
0.0011	23.0	138	1.0791	0.6341
0.001	24.0	144	1.0890	0.6585
0.001	25.0	150	1.0948	0.6585
0.001	26.0	156	1.1067	0.6585
0.0008	27.0	162	1.0949	0.6585
0.0008	28.0	168	1.1017	0.6585
0.0008	29.0	174	1.1094	0.6585
0.0007	30.0	180	1.1105	0.6585
0.0007	31.0	186	1.1156	0.6585
0.0007	32.0	192	1.1158	0.6585
0.0007	33.0	198	1.1174	0.6585
0.0007	34.0	204	1.1167	0.6585
0.0006	35.0	210	1.1206	0.6585
0.0006	36.0	216	1.1224	0.6585
0.0006	37.0	222	1.1230	0.6585
0.0006	38.0	228	1.1253	0.6585
0.0006	39.0	234	1.1272	0.6585
0.0006	40.0	240	1.1276	0.6585
0.0006	41.0	246	1.1278	0.6585
0.0006	42.0	252	1.1280	0.6585
0.0006	43.0	258	1.1280	0.6585
0.0006	44.0	264	1.1280	0.6585
0.0006	45.0	270	1.1280	0.6585
0.0006	46.0	276	1.1280	0.6585
0.0006	47.0	282	1.1280	0.6585
0.0006	48.0	288	1.1280	0.6585
0.0006	49.0	294	1.1280	0.6585
0.0006	50.0	300	1.1280	0.6585

Framework versions

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