smids_1x_beit_base_sgd_001_fold4

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

• Loss: 0.3889
• Accuracy: 0.8533

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: 0.001
• 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
1.0889	1.0	75	1.0669	0.4483
0.8655	2.0	150	0.8559	0.645
0.7785	3.0	225	0.7294	0.7017
0.7379	4.0	300	0.6722	0.7167
0.7102	5.0	375	0.6344	0.7367
0.679	6.0	450	0.6029	0.7583
0.6616	7.0	525	0.5757	0.7667
0.6078	8.0	600	0.5519	0.7983
0.6427	9.0	675	0.5357	0.7867
0.5452	10.0	750	0.5237	0.7983
0.5678	11.0	825	0.5091	0.7933
0.5374	12.0	900	0.4984	0.8083
0.5811	13.0	975	0.4866	0.8033
0.5027	14.0	1050	0.4734	0.81
0.4787	15.0	1125	0.4709	0.805
0.4828	16.0	1200	0.4644	0.8117
0.4631	17.0	1275	0.4587	0.81
0.4343	18.0	1350	0.4468	0.82
0.4904	19.0	1425	0.4435	0.82
0.4433	20.0	1500	0.4398	0.8167
0.4461	21.0	1575	0.4318	0.8283
0.3894	22.0	1650	0.4361	0.8217
0.4398	23.0	1725	0.4269	0.8333
0.4433	24.0	1800	0.4213	0.835
0.4253	25.0	1875	0.4182	0.8333
0.4141	26.0	1950	0.4208	0.835
0.4307	27.0	2025	0.4086	0.8433
0.4132	28.0	2100	0.4078	0.8417
0.41	29.0	2175	0.4074	0.8467
0.4398	30.0	2250	0.4048	0.8383
0.3394	31.0	2325	0.4047	0.8383
0.4174	32.0	2400	0.4015	0.8433
0.4379	33.0	2475	0.3992	0.8433
0.3836	34.0	2550	0.3988	0.845
0.3824	35.0	2625	0.3986	0.845
0.3571	36.0	2700	0.3978	0.8433
0.348	37.0	2775	0.3932	0.8483
0.3852	38.0	2850	0.3961	0.8433
0.4099	39.0	2925	0.3932	0.8483
0.4003	40.0	3000	0.3917	0.8533
0.3921	41.0	3075	0.3931	0.8483
0.3545	42.0	3150	0.3904	0.8533
0.3816	43.0	3225	0.3916	0.8467
0.3789	44.0	3300	0.3906	0.8533
0.373	45.0	3375	0.3905	0.85
0.3767	46.0	3450	0.3894	0.8533
0.3814	47.0	3525	0.3896	0.8517
0.3984	48.0	3600	0.3899	0.85
0.3749	49.0	3675	0.3891	0.8533
0.3605	50.0	3750	0.3889	0.8533

Framework versions

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