BilalMuftuoglu/beit-base-patch16-224-55-fold1

beit-base-patch16-224-fold1

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: 0.7241
• Accuracy: 0.8481

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8571	3	0.8050	0.4557
No log	2.0	7	0.7151	0.5696
0.8103	2.8571	10	0.6822	0.5570
0.8103	4.0	14	0.6408	0.5696
0.8103	4.8571	17	0.6244	0.6709
0.6583	6.0	21	0.5893	0.6709
0.6583	6.8571	24	0.5877	0.6329
0.6583	8.0	28	0.5752	0.6835
0.5912	8.8571	31	0.5826	0.6456
0.5912	10.0	35	0.5469	0.6835
0.5912	10.8571	38	0.6173	0.6582
0.5301	12.0	42	0.5151	0.6962
0.5301	12.8571	45	0.5105	0.6962
0.5301	14.0	49	0.5489	0.7089
0.4703	14.8571	52	0.5725	0.6835
0.4703	16.0	56	0.5560	0.6962
0.4703	16.8571	59	0.5824	0.6709
0.4189	18.0	63	0.5401	0.7468
0.4189	18.8571	66	0.5147	0.7722
0.3741	20.0	70	0.4864	0.7595
0.3741	20.8571	73	0.5272	0.7342
0.3741	22.0	77	0.4914	0.7468
0.387	22.8571	80	0.5658	0.7468
0.387	24.0	84	0.4662	0.7722
0.387	24.8571	87	0.4376	0.7848
0.3502	26.0	91	0.5367	0.7722
0.3502	26.8571	94	0.5490	0.7342
0.3502	28.0	98	0.7163	0.7722
0.3148	28.8571	101	0.6005	0.7468
0.3148	30.0	105	0.6501	0.7722
0.3148	30.8571	108	0.5313	0.7975
0.2973	32.0	112	0.5466	0.7722
0.2973	32.8571	115	0.5731	0.8101
0.2973	34.0	119	0.6544	0.8101
0.2474	34.8571	122	0.6061	0.7848
0.2474	36.0	126	0.5816	0.7722
0.2474	36.8571	129	0.7161	0.7595
0.2033	38.0	133	0.6235	0.7848
0.2033	38.8571	136	0.7889	0.7595
0.2338	40.0	140	0.5943	0.7595
0.2338	40.8571	143	0.6170	0.7342
0.2338	42.0	147	0.6964	0.6962
0.2067	42.8571	150	0.7154	0.7468
0.2067	44.0	154	0.7675	0.7722
0.2067	44.8571	157	0.7766	0.7468
0.2133	46.0	161	0.9330	0.7848
0.2133	46.8571	164	0.6494	0.7975
0.2133	48.0	168	0.5709	0.7722
0.2004	48.8571	171	0.6462	0.8101
0.2004	50.0	175	0.6668	0.7722
0.2004	50.8571	178	0.6305	0.8101
0.188	52.0	182	0.7189	0.8228
0.188	52.8571	185	0.6853	0.7848
0.188	54.0	189	0.8040	0.8228
0.1623	54.8571	192	0.6958	0.8101
0.1623	56.0	196	0.6907	0.8101
0.1623	56.8571	199	0.6821	0.8101
0.1588	58.0	203	0.6534	0.8101
0.1588	58.8571	206	0.7192	0.8101
0.1607	60.0	210	0.7753	0.8228
0.1607	60.8571	213	0.8950	0.8101
0.1607	62.0	217	0.7904	0.8101
0.1767	62.8571	220	0.6973	0.8101
0.1767	64.0	224	0.6694	0.7975
0.1767	64.8571	227	0.6339	0.8101
0.1463	66.0	231	0.6530	0.8101
0.1463	66.8571	234	0.6142	0.8101
0.1463	68.0	238	0.6290	0.8228
0.1287	68.8571	241	0.6334	0.8354
0.1287	70.0	245	0.8059	0.8101
0.1287	70.8571	248	0.7241	0.8481
0.1323	72.0	252	0.6836	0.8481
0.1323	72.8571	255	0.6588	0.8228
0.1323	74.0	259	0.6598	0.8481
0.1042	74.8571	262	0.7139	0.8354
0.1042	76.0	266	0.7236	0.8354
0.1042	76.8571	269	0.6919	0.8354
0.1106	78.0	273	0.6568	0.8354
0.1106	78.8571	276	0.6556	0.8481
0.1348	80.0	280	0.6612	0.8354
0.1348	80.8571	283	0.6686	0.8228
0.1348	82.0	287	0.6705	0.8481
0.1352	82.8571	290	0.6776	0.8354
0.1352	84.0	294	0.6873	0.8354
0.1352	84.8571	297	0.6888	0.8354
0.1226	85.7143	300	0.6880	0.8354

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1