smids_1x_deit_small_sgd_00001_fold2

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: 1.0336
• Accuracy: 0.5042

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
1.0925	1.0	75	1.0713	0.4343
1.0735	2.0	150	1.0692	0.4343
1.0724	3.0	225	1.0673	0.4393
1.0873	4.0	300	1.0654	0.4426
1.1019	5.0	375	1.0637	0.4426
1.0577	6.0	450	1.0620	0.4459
1.0861	7.0	525	1.0604	0.4493
1.0644	8.0	600	1.0588	0.4542
1.0424	9.0	675	1.0573	0.4509
1.0503	10.0	750	1.0559	0.4509
1.0641	11.0	825	1.0545	0.4493
1.0679	12.0	900	1.0532	0.4526
1.0629	13.0	975	1.0520	0.4542
1.0438	14.0	1050	1.0508	0.4542
1.061	15.0	1125	1.0497	0.4509
1.0498	16.0	1200	1.0486	0.4509
1.0521	17.0	1275	1.0475	0.4559
1.0469	18.0	1350	1.0466	0.4576
1.047	19.0	1425	1.0456	0.4609
1.0592	20.0	1500	1.0447	0.4659
1.0668	21.0	1575	1.0439	0.4709
1.0281	22.0	1650	1.0431	0.4725
1.0356	23.0	1725	1.0423	0.4775
1.026	24.0	1800	1.0416	0.4775
1.0466	25.0	1875	1.0409	0.4792
1.0451	26.0	1950	1.0402	0.4809
1.0338	27.0	2025	1.0396	0.4859
1.0199	28.0	2100	1.0390	0.4842
1.0289	29.0	2175	1.0384	0.4875
1.0316	30.0	2250	1.0379	0.4908
1.0446	31.0	2325	1.0374	0.4925
1.0407	32.0	2400	1.0369	0.4925
1.0163	33.0	2475	1.0365	0.4925
1.0508	34.0	2550	1.0361	0.4925
1.024	35.0	2625	1.0358	0.4942
1.0435	36.0	2700	1.0354	0.4958
1.0618	37.0	2775	1.0351	0.4958
1.0365	38.0	2850	1.0349	0.4975
1.0269	39.0	2925	1.0346	0.4992
1.0291	40.0	3000	1.0344	0.5008
1.0505	41.0	3075	1.0342	0.5008
1.0316	42.0	3150	1.0340	0.5008
1.0295	43.0	3225	1.0339	0.5008
1.049	44.0	3300	1.0338	0.5008
1.0556	45.0	3375	1.0337	0.5008
1.0458	46.0	3450	1.0336	0.5008
1.0348	47.0	3525	1.0336	0.5008
1.0496	48.0	3600	1.0336	0.5025
1.0321	49.0	3675	1.0336	0.5042
1.0497	50.0	3750	1.0336	0.5042

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0