This model is a fine-tuned version of facebook/dino-vitb8 on an - BTX24/tekno21-brain-stroke-dataset-binary dataset. It achieves the following results on the evaluation set:

Loss: 0.1127
Accuracy: 0.9597
F1: 0.9595
Precision: 0.9602
Recall: 0.9597

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 36
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.7965	0.6202	100	0.8312	0.5382	0.5058	0.4968	0.5382
0.6839	1.2357	200	0.6796	0.6246	0.5750	0.5991	0.6246
0.5662	1.8558	300	0.5344	0.7318	0.7119	0.7377	0.7318
0.4408	2.4713	400	0.4082	0.8123	0.8082	0.8120	0.8123
0.3611	3.0868	500	0.3335	0.8602	0.8597	0.8596	0.8602
0.3121	3.7070	600	0.2746	0.8860	0.8832	0.8914	0.8860
0.2614	4.3225	700	0.2299	0.9050	0.9040	0.9058	0.9050
0.242	4.9426	800	0.2103	0.9177	0.9178	0.9179	0.9177
0.2239	5.5581	900	0.2298	0.9082	0.9090	0.9136	0.9082
0.1979	6.1736	1000	0.2059	0.9209	0.9197	0.9230	0.9209
0.2082	6.7938	1100	0.1779	0.9263	0.9261	0.9261	0.9263
0.1723	7.4093	1200	0.1693	0.9308	0.9302	0.9315	0.9308
0.1877	8.0248	1300	0.1681	0.9380	0.9382	0.9385	0.9380
0.2	8.6450	1400	0.1482	0.9403	0.9402	0.9402	0.9403
0.1642	9.2605	1500	0.1637	0.9331	0.9322	0.9352	0.9331
0.1525	9.8806	1600	0.1494	0.9421	0.9417	0.9425	0.9421
0.158	10.4961	1700	0.1403	0.9484	0.9480	0.9495	0.9484
0.1327	11.1116	1800	0.1329	0.9498	0.9498	0.9498	0.9498
0.1465	11.7318	1900	0.1233	0.9525	0.9524	0.9525	0.9525
0.1311	12.3473	2000	0.1280	0.9521	0.9520	0.9520	0.9521
0.129	12.9674	2100	0.1173	0.9557	0.9556	0.9556	0.9557
0.1425	13.5829	2200	0.1190	0.9552	0.9552	0.9552	0.9552
0.1256	14.1984	2300	0.1225	0.9566	0.9563	0.9570	0.9566
0.1461	14.8186	2400	0.1171	0.9588	0.9588	0.9588	0.9588
0.133	15.4341	2500	0.1165	0.9548	0.9546	0.9549	0.9548
0.1258	16.0496	2600	0.1302	0.9480	0.9474	0.9500	0.9480
0.115	16.6698	2700	0.1320	0.9534	0.9537	0.9552	0.9534
0.1134	17.2853	2800	0.1171	0.9552	0.9549	0.9562	0.9552
0.1069	17.9054	2900	0.1127	0.9597	0.9595	0.9602	0.9597