BilalMuftuoglu/beit-base-patch16-224-55-fold2

beit-base-patch16-224-fold2

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: 0.3405
• Accuracy: 0.8608

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	0.6785	0.6076
No log	2.0	7	0.6621	0.6329
0.7083	2.8571	10	0.6434	0.5823
0.7083	4.0	14	0.6708	0.5696
0.7083	4.8571	17	0.6701	0.6076
0.6009	6.0	21	0.7958	0.5949
0.6009	6.8571	24	0.5952	0.6456
0.6009	8.0	28	0.8008	0.6962
0.5315	8.8571	31	0.8903	0.6329
0.5315	10.0	35	0.7070	0.6709
0.5315	10.8571	38	0.5331	0.7595
0.5756	12.0	42	0.5307	0.7468
0.5756	12.8571	45	0.5070	0.7468
0.5756	14.0	49	0.6117	0.7215
0.4519	14.8571	52	0.4667	0.7468
0.4519	16.0	56	0.4151	0.7848
0.4519	16.8571	59	0.4435	0.7722
0.3821	18.0	63	0.4114	0.7975
0.3821	18.8571	66	0.4067	0.8101
0.328	20.0	70	0.4459	0.8101
0.328	20.8571	73	0.3859	0.8354
0.328	22.0	77	0.3405	0.8608
0.3344	22.8571	80	0.3702	0.8354
0.3344	24.0	84	0.4352	0.7848
0.3344	24.8571	87	0.6777	0.7342
0.2747	26.0	91	0.5708	0.7975
0.2747	26.8571	94	0.4432	0.8101
0.2747	28.0	98	0.3736	0.8101
0.2634	28.8571	101	0.3938	0.8228
0.2634	30.0	105	0.4460	0.8354
0.2634	30.8571	108	0.4382	0.8101
0.2306	32.0	112	0.5574	0.8101
0.2306	32.8571	115	0.3863	0.8354
0.2306	34.0	119	0.4390	0.8481
0.2214	34.8571	122	0.4839	0.8481
0.2214	36.0	126	0.4523	0.8354
0.2214	36.8571	129	0.4022	0.8354
0.1945	38.0	133	0.4408	0.8354
0.1945	38.8571	136	0.3988	0.8354
0.1863	40.0	140	0.4467	0.8481
0.1863	40.8571	143	0.4788	0.8101
0.1863	42.0	147	0.4749	0.8354
0.1718	42.8571	150	0.4727	0.8228
0.1718	44.0	154	0.4632	0.8481
0.1718	44.8571	157	0.4561	0.8354
0.1535	46.0	161	0.5113	0.8101
0.1535	46.8571	164	0.6505	0.8481
0.1535	48.0	168	0.5612	0.8228
0.1454	48.8571	171	0.6825	0.8354
0.1454	50.0	175	0.7960	0.8354
0.1454	50.8571	178	0.5915	0.8228
0.1327	52.0	182	0.6200	0.8354
0.1327	52.8571	185	0.4977	0.8354
0.1327	54.0	189	0.6180	0.8608
0.1491	54.8571	192	0.6474	0.8608
0.1491	56.0	196	0.5886	0.8481
0.1491	56.8571	199	0.6743	0.8481
0.1666	58.0	203	0.6476	0.8354
0.1666	58.8571	206	0.6483	0.8481
0.1219	60.0	210	0.7216	0.8354
0.1219	60.8571	213	0.6541	0.8354
0.1219	62.0	217	0.6636	0.8354
0.1339	62.8571	220	0.6708	0.8354
0.1339	64.0	224	0.6735	0.8481
0.1339	64.8571	227	0.7030	0.8354
0.1227	66.0	231	0.6779	0.8228
0.1227	66.8571	234	0.7091	0.8354
0.1227	68.0	238	0.6858	0.8354
0.1316	68.8571	241	0.6668	0.8354
0.1316	70.0	245	0.6491	0.8354
0.1316	70.8571	248	0.7164	0.8481
0.1124	72.0	252	0.8063	0.8354
0.1124	72.8571	255	0.7437	0.8481
0.1124	74.0	259	0.8528	0.8354
0.1036	74.8571	262	0.9348	0.8101
0.1036	76.0	266	0.8078	0.8354
0.1036	76.8571	269	0.7697	0.8481
0.1057	78.0	273	0.8040	0.8481
0.1057	78.8571	276	0.8197	0.8481
0.099	80.0	280	0.8256	0.8354
0.099	80.8571	283	0.8057	0.8228
0.099	82.0	287	0.7797	0.8354
0.0927	82.8571	290	0.7807	0.8354
0.0927	84.0	294	0.7957	0.8228
0.0927	84.8571	297	0.8031	0.8228
0.0995	85.7143	300	0.8061	0.8228

Framework versions

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