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_2
    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: 0.9586206896551724
          - name: Recall
            type: recall
            value: 0.9621148459383753

deit_fold_2

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: 1.1405
• Accuracy: 0.9586
• F1 Score: 0.9620
• Recall: 0.9621

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
2.8182	1.0	19	2.7982	0.2966	0.2243	0.2444
2.7152	2.0	38	2.6468	0.3793	0.2689	0.3032
2.5094	3.0	57	2.4150	0.5241	0.4335	0.4481
2.1993	4.0	76	2.0760	0.7172	0.7150	0.6964
1.7513	5.0	95	1.7070	0.8069	0.8177	0.8141
1.4469	6.0	114	1.4631	0.8586	0.8728	0.8742
1.2482	7.0	133	1.3515	0.8759	0.8882	0.8890
1.2058	8.0	152	1.3018	0.8897	0.8990	0.8998
1.1470	9.0	171	1.2735	0.9069	0.9141	0.9169
1.0887	10.0	190	1.2533	0.9207	0.9291	0.9306
1.0903	11.0	209	1.2413	0.9103	0.9182	0.9159
1.0576	12.0	228	1.2260	0.9207	0.9286	0.9317
1.0231	13.0	247	1.2594	0.9207	0.9278	0.9303
1.0264	14.0	266	1.2164	0.9276	0.9339	0.9305
1.0175	15.0	285	1.2294	0.9241	0.9301	0.9267
1.0135	16.0	304	1.2143	0.9345	0.9406	0.9402
1.0178	17.0	323	1.2314	0.9276	0.9351	0.9380
0.9823	18.0	342	1.2160	0.9310	0.9373	0.9365
0.9965	19.0	361	1.2154	0.9172	0.9232	0.9206
0.9854	20.0	380	1.1846	0.9345	0.9411	0.9415
0.9807	21.0	399	1.2096	0.9345	0.9414	0.9414
0.9860	22.0	418	1.1996	0.9276	0.9338	0.9316
0.9796	23.0	437	1.1967	0.9310	0.9365	0.9317
0.9826	24.0	456	1.2180	0.9172	0.9239	0.9183
0.9809	25.0	475	1.2030	0.9345	0.9405	0.9378
0.9803	26.0	494	1.1866	0.9345	0.9403	0.9389
0.9748	27.0	513	1.1626	0.9448	0.9497	0.9487
0.9659	28.0	532	1.1405	0.9586	0.9620	0.9621
0.9718	29.0	551	1.1410	0.9483	0.9525	0.9525
0.9668	30.0	570	1.1485	0.9552	0.9589	0.9584
0.9715	31.0	589	1.1423	0.9448	0.9499	0.9502
0.9729	32.0	608	1.1560	0.9483	0.9535	0.9550
0.9766	33.0	627	1.1721	0.9483	0.9541	0.9561
0.9687	34.0	646	1.1706	0.9448	0.9509	0.9524
0.9769	35.0	665	1.1539	0.9345	0.9410	0.9403
0.9609	36.0	684	1.1552	0.9483	0.9539	0.9549
0.9587	37.0	703	1.1565	0.9517	0.9569	0.9597
0.9673	38.0	722	1.1675	0.9483	0.9537	0.9562

Framework versions

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