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_1
    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: 0.9655172413793104
          - name: Recall
            type: recall
            value: 0.9681372549019608

deit_fold_1

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: 1.1170
• Accuracy: 0.9655
• F1 Score: 0.9681
• Recall: 0.9681

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
2.7052	1.0	19	2.6182	0.3897	0.2859	0.3210
2.6037	2.0	38	2.4936	0.4345	0.3591	0.3697
2.4310	3.0	57	2.2883	0.5552	0.5442	0.5290
2.1283	4.0	76	1.9885	0.7172	0.7322	0.7214
1.7269	5.0	95	1.6339	0.8172	0.8313	0.8241
1.4880	6.0	114	1.3974	0.8655	0.8730	0.8676
1.2856	7.0	133	1.2969	0.9	0.9069	0.9031
1.2589	8.0	152	1.2535	0.9069	0.9112	0.9057
1.1813	9.0	171	1.1953	0.9241	0.9300	0.9304
1.1441	10.0	190	1.1895	0.9379	0.9417	0.9389
1.1279	11.0	209	1.1635	0.9552	0.9589	0.9584
1.0956	12.0	228	1.1425	0.9483	0.9524	0.9511
1.0689	13.0	247	1.1503	0.9483	0.9511	0.9521
1.0613	14.0	266	1.1557	0.9552	0.9574	0.9571
1.0226	15.0	285	1.1592	0.9448	0.9478	0.9449
1.0242	16.0	304	1.1320	0.9483	0.9511	0.9510
1.0160	17.0	323	1.1371	0.9552	0.9584	0.9571
0.9991	18.0	342	1.1267	0.9621	0.9650	0.9657
1.0108	19.0	361	1.1350	0.9517	0.9551	0.9534
1.0175	20.0	380	1.1404	0.9586	0.9617	0.9608
0.9991	21.0	399	1.1422	0.9586	0.9614	0.9596
0.9951	22.0	418	1.1277	0.9552	0.9590	0.9608
0.9753	23.0	437	1.1113	0.9586	0.9620	0.9632
0.9748	24.0	456	1.1117	0.9552	0.9590	0.9608
0.9748	25.0	475	1.1086	0.9621	0.9648	0.9645
0.9704	26.0	494	1.1182	0.9586	0.9619	0.9620
0.9748	27.0	513	1.1061	0.9586	0.9620	0.9632
0.9637	28.0	532	1.0796	0.9621	0.9652	0.9669
0.9696	29.0	551	1.1170	0.9655	0.9681	0.9681
0.9656	30.0	570	1.0977	0.9621	0.9652	0.9669
0.9679	31.0	589	1.1275	0.9586	0.9621	0.9632
0.9699	32.0	608	1.0992	0.9586	0.9622	0.9645
0.9760	33.0	627	1.1147	0.9621	0.9647	0.9632
0.9595	34.0	646	1.0978	0.9621	0.9653	0.9681
0.9619	35.0	665	1.1000	0.9621	0.9648	0.9645
0.9634	36.0	684	1.1140	0.9655	0.9679	0.9669
0.9554	37.0	703	1.1082	0.9655	0.9679	0.9669
0.9644	38.0	722	1.1166	0.9655	0.9681	0.9681

Framework versions

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