smids_10x_deit_small_sgd_00001_fold1

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: 0.9431
• Accuracy: 0.5543

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
1.0703	1.0	751	1.0730	0.4341
1.028	2.0	1502	1.0687	0.4341
1.0258	3.0	2253	1.0640	0.4374
1.0492	4.0	3004	1.0592	0.4391
1.036	5.0	3755	1.0544	0.4407
1.0185	6.0	4506	1.0494	0.4457
1.0044	7.0	5257	1.0445	0.4524
1.0106	8.0	6008	1.0397	0.4524
1.0008	9.0	6759	1.0351	0.4558
0.983	10.0	7510	1.0305	0.4624
0.9888	11.0	8261	1.0261	0.4691
0.98	12.0	9012	1.0217	0.4758
0.9777	13.0	9763	1.0175	0.4775
0.9805	14.0	10514	1.0134	0.4825
0.9554	15.0	11265	1.0095	0.4875
0.9727	16.0	12016	1.0055	0.4942
0.9405	17.0	12767	1.0016	0.4992
0.9669	18.0	13518	0.9980	0.5042
0.9407	19.0	14269	0.9944	0.5042
0.9487	20.0	15020	0.9909	0.5075
0.9336	21.0	15771	0.9876	0.5092
0.9505	22.0	16522	0.9843	0.5109
0.9425	23.0	17273	0.9812	0.5125
0.9422	24.0	18024	0.9782	0.5175
0.9397	25.0	18775	0.9753	0.5209
0.9277	26.0	19526	0.9725	0.5225
0.9248	27.0	20277	0.9699	0.5326
0.915	28.0	21028	0.9674	0.5342
0.9341	29.0	21779	0.9650	0.5376
0.9201	30.0	22530	0.9628	0.5392
0.8994	31.0	23281	0.9606	0.5376
0.9167	32.0	24032	0.9586	0.5392
0.8872	33.0	24783	0.9568	0.5426
0.8983	34.0	25534	0.9550	0.5426
0.8839	35.0	26285	0.9534	0.5442
0.9018	36.0	27036	0.9519	0.5476
0.8955	37.0	27787	0.9506	0.5492
0.8964	38.0	28538	0.9493	0.5492
0.9005	39.0	29289	0.9482	0.5492
0.8988	40.0	30040	0.9472	0.5526
0.8967	41.0	30791	0.9463	0.5543
0.8873	42.0	31542	0.9455	0.5543
0.9048	43.0	32293	0.9449	0.5543
0.8665	44.0	33044	0.9443	0.5543
0.8925	45.0	33795	0.9439	0.5543
0.8934	46.0	34546	0.9435	0.5543
0.8656	47.0	35297	0.9433	0.5543
0.9144	48.0	36048	0.9431	0.5543
0.9081	49.0	36799	0.9431	0.5543
0.8986	50.0	37550	0.9431	0.5543

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2