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_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: 0.6585365853658537

hushem_1x_deit_tiny_adamax_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: 0.8924
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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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	0.67	1	1.9330	0.2439
No log	2.0	3	1.4362	0.3659
No log	2.67	4	1.3806	0.3902
No log	4.0	6	1.3304	0.4634
No log	4.67	7	1.3017	0.4390
No log	6.0	9	1.1836	0.4878
1.2323	6.67	10	1.1688	0.5610
1.2323	8.0	12	1.1361	0.5366
1.2323	8.67	13	1.1291	0.5366
1.2323	10.0	15	1.0782	0.6098
1.2323	10.67	16	1.0358	0.6585
1.2323	12.0	18	1.0020	0.6098
1.2323	12.67	19	1.0059	0.6098
0.3527	14.0	21	0.9293	0.6098
0.3527	14.67	22	0.9162	0.6341
0.3527	16.0	24	0.9233	0.6098
0.3527	16.67	25	0.9213	0.6098
0.3527	18.0	27	0.9193	0.6098
0.3527	18.67	28	0.9345	0.6098
0.04	20.0	30	0.8872	0.6585
0.04	20.67	31	0.8549	0.6829
0.04	22.0	33	0.8221	0.6829
0.04	22.67	34	0.8117	0.7073
0.04	24.0	36	0.8041	0.7561
0.04	24.67	37	0.8128	0.7561
0.04	26.0	39	0.8413	0.6829
0.0062	26.67	40	0.8565	0.6585
0.0062	28.0	42	0.8789	0.6585
0.0062	28.67	43	0.8864	0.6585
0.0062	30.0	45	0.8920	0.6585
0.0062	30.67	46	0.8925	0.6585
0.0062	32.0	48	0.8929	0.6585
0.0062	32.67	49	0.8927	0.6585
0.0031	33.33	50	0.8924	0.6585

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1