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_adamax_00001_fold3
    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: 0.5581395348837209

hushem_1x_deit_tiny_adamax_00001_fold3

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: 0.8253
• Accuracy: 0.5581

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	1.4425	0.2791
1.416	2.0	12	1.3728	0.3023
1.416	3.0	18	1.3124	0.3488
1.2388	4.0	24	1.2509	0.3721
1.1051	5.0	30	1.1962	0.3488
1.1051	6.0	36	1.1517	0.3721
0.9682	7.0	42	1.1212	0.3721
0.9682	8.0	48	1.0990	0.4186
0.8769	9.0	54	1.0709	0.4884
0.7643	10.0	60	1.0587	0.5116
0.7643	11.0	66	1.0451	0.4884
0.6717	12.0	72	1.0399	0.5581
0.6717	13.0	78	1.0224	0.5349
0.5988	14.0	84	1.0021	0.4884
0.5291	15.0	90	0.9852	0.4884
0.5291	16.0	96	0.9774	0.5116
0.4581	17.0	102	0.9701	0.5116
0.4581	18.0	108	0.9598	0.5116
0.3895	19.0	114	0.9410	0.5814
0.3415	20.0	120	0.9223	0.5581
0.3415	21.0	126	0.9172	0.5349
0.3044	22.0	132	0.9106	0.5349
0.3044	23.0	138	0.9037	0.5581
0.2632	24.0	144	0.8935	0.5581
0.2425	25.0	150	0.8847	0.5814
0.2425	26.0	156	0.8721	0.5581
0.2102	27.0	162	0.8625	0.5581
0.2102	28.0	168	0.8546	0.5581
0.189	29.0	174	0.8540	0.5814
0.1637	30.0	180	0.8496	0.6047
0.1637	31.0	186	0.8464	0.6047
0.1512	32.0	192	0.8420	0.5581
0.1512	33.0	198	0.8380	0.5581
0.1374	34.0	204	0.8346	0.5581
0.1287	35.0	210	0.8327	0.5581
0.1287	36.0	216	0.8290	0.5581
0.124	37.0	222	0.8276	0.5581
0.124	38.0	228	0.8271	0.5581
0.1186	39.0	234	0.8265	0.5581
0.1159	40.0	240	0.8255	0.5581
0.1159	41.0	246	0.8253	0.5581
0.1139	42.0	252	0.8253	0.5581
0.1139	43.0	258	0.8253	0.5581
0.1142	44.0	264	0.8253	0.5581
0.1107	45.0	270	0.8253	0.5581
0.1107	46.0	276	0.8253	0.5581
0.1118	47.0	282	0.8253	0.5581
0.1118	48.0	288	0.8253	0.5581
0.1159	49.0	294	0.8253	0.5581
0.1095	50.0	300	0.8253	0.5581

Framework versions

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