README.md

metadata

library_name: transformers
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
model-index:
  - name: deit_fold_4
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: None
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9515570934256056
          - name: Recall
            type: recall
            value: 0.9524889695792607

deit_fold_4

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.1613
• Accuracy: 0.9516
• F1 Score: 0.9557
• Recall: 0.9525

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
3.0112	1.0	19	2.9175	0.1349	0.1173	0.1476
2.8882	2.0	38	2.7478	0.2837	0.2479	0.2602
2.6765	3.0	57	2.5537	0.4360	0.3402	0.3670
2.3897	4.0	76	2.2672	0.5882	0.5441	0.5359
1.9781	5.0	95	1.8705	0.7924	0.7997	0.7861
1.6123	6.0	114	1.5627	0.8512	0.8659	0.8675
1.4216	7.0	133	1.3723	0.8962	0.9067	0.9017
1.2460	8.0	152	1.2605	0.9308	0.9380	0.9351
1.1724	9.0	171	1.2218	0.9308	0.9370	0.9337
1.1521	10.0	190	1.1915	0.9446	0.9492	0.9488
1.1116	11.0	209	1.1921	0.9377	0.9440	0.9451
1.0878	12.0	228	1.1973	0.9343	0.9406	0.9428
1.0601	13.0	247	1.1842	0.9377	0.9435	0.9451
1.0711	14.0	266	1.1563	0.9446	0.9497	0.9475
1.0461	15.0	285	1.1533	0.9446	0.9497	0.9475
1.0390	16.0	304	1.1613	0.9516	0.9557	0.9525
1.0080	17.0	323	1.1645	0.9412	0.9463	0.9465
1.0109	18.0	342	1.1656	0.9377	0.9434	0.9438
1.0092	19.0	361	1.1701	0.9446	0.9494	0.9501
0.9972	20.0	380	1.1859	0.9377	0.9432	0.9429
1.0005	21.0	399	1.1708	0.9446	0.9501	0.9522
0.9900	22.0	418	1.1849	0.9412	0.9470	0.9476
0.9758	23.0	437	1.1893	0.9446	0.9496	0.9511
0.9886	24.0	456	1.1739	0.9446	0.9496	0.9511
0.9757	25.0	475	1.1648	0.9412	0.9460	0.9440

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2