vit-msn-small-lateral_flow_ivalidation_train_test

This model is a fine-tuned version of Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.3718
• Accuracy: 0.9121

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-06
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 100
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4023	0.9231	3	0.4126	0.8938
0.4055	1.8462	6	0.4096	0.8974
0.4112	2.7692	9	0.4275	0.8974
0.4246	4.0	13	0.3958	0.9048
0.376	4.9231	16	0.3980	0.9121
0.4226	5.8462	19	0.4029	0.9084
0.3751	6.7692	22	0.3749	0.9048
0.4135	8.0	26	0.3757	0.9121
0.3673	8.9231	29	0.4174	0.8901
0.3749	9.8462	32	0.4077	0.9048
0.4119	10.7692	35	0.4181	0.8901
0.3946	12.0	39	0.4189	0.8901
0.3335	12.9231	42	0.4029	0.9084
0.3717	13.8462	45	0.3963	0.9011
0.3493	14.7692	48	0.3797	0.9158
0.3686	16.0	52	0.3761	0.9121
0.3999	16.9231	55	0.3774	0.9158
0.3221	17.8462	58	0.3757	0.9158
0.3902	18.7692	61	0.3774	0.9121
0.3649	20.0	65	0.3962	0.9011
0.3553	20.9231	68	0.3718	0.9121
0.3761	21.8462	71	0.3934	0.9121
0.3422	22.7692	74	0.4271	0.8828
0.3247	24.0	78	0.3727	0.9194
0.3417	24.9231	81	0.3793	0.9121
0.3499	25.8462	84	0.4293	0.8791
0.3397	26.7692	87	0.4216	0.8901
0.346	28.0	91	0.4001	0.8901
0.3337	28.9231	94	0.4168	0.8864
0.3268	29.8462	97	0.4123	0.8938
0.3274	30.7692	100	0.4187	0.8828
0.3757	32.0	104	0.4026	0.8974
0.3727	32.9231	107	0.4021	0.8938
0.3431	33.8462	110	0.4024	0.9011
0.3626	34.7692	113	0.4200	0.8901
0.3381	36.0	117	0.4080	0.8938
0.3411	36.9231	120	0.4279	0.8791
0.3229	37.8462	123	0.4422	0.8718
0.3736	38.7692	126	0.4285	0.8791
0.4145	40.0	130	0.4402	0.8718
0.3456	40.9231	133	0.4226	0.8828
0.3567	41.8462	136	0.4113	0.8901
0.339	42.7692	139	0.4445	0.8645
0.3142	44.0	143	0.4204	0.8791
0.3461	44.9231	146	0.4006	0.8974
0.3583	45.8462	149	0.3991	0.9011
0.3651	46.7692	152	0.4293	0.8681
0.3098	48.0	156	0.4082	0.8901
0.375	48.9231	159	0.4095	0.8864
0.3435	49.8462	162	0.4529	0.8498
0.3452	50.7692	165	0.4440	0.8608
0.3316	52.0	169	0.4181	0.8791
0.3344	52.9231	172	0.4609	0.8535
0.3377	53.8462	175	0.4775	0.8278
0.3455	54.7692	178	0.4396	0.8681
0.3202	56.0	182	0.4384	0.8755
0.3119	56.9231	185	0.4573	0.8535
0.3633	57.8462	188	0.4469	0.8645
0.3025	58.7692	191	0.4437	0.8608
0.3094	60.0	195	0.4472	0.8571
0.3306	60.9231	198	0.4396	0.8681
0.3266	61.8462	201	0.4486	0.8681
0.3495	62.7692	204	0.4658	0.8352
0.3066	64.0	208	0.4754	0.8315
0.3384	64.9231	211	0.4518	0.8608
0.3151	65.8462	214	0.4614	0.8535
0.3233	66.7692	217	0.4638	0.8425
0.3416	68.0	221	0.4741	0.8315
0.3326	68.9231	224	0.4679	0.8425
0.331	69.8462	227	0.4754	0.8315
0.3595	70.7692	230	0.4603	0.8498
0.3107	72.0	234	0.4412	0.8571
0.3126	72.9231	237	0.4578	0.8571
0.3205	73.8462	240	0.4820	0.8242
0.3296	74.7692	243	0.5048	0.7985
0.3246	76.0	247	0.4792	0.8278
0.3065	76.9231	250	0.4842	0.8242
0.282	77.8462	253	0.5049	0.7912
0.3272	78.7692	256	0.5088	0.7875
0.325	80.0	260	0.4933	0.8132
0.3524	80.9231	263	0.4893	0.8132
0.3019	81.8462	266	0.4864	0.8132
0.3095	82.7692	269	0.4875	0.8132
0.3254	84.0	273	0.4910	0.8059
0.3158	84.9231	276	0.4918	0.8059
0.3114	85.8462	279	0.4936	0.8059
0.3348	86.7692	282	0.4996	0.7985
0.3078	88.0	286	0.5043	0.7949
0.3096	88.9231	289	0.5047	0.7949
0.2827	89.8462	292	0.5054	0.7949
0.3249	90.7692	295	0.5040	0.7949
0.3277	92.0	299	0.5031	0.7985
0.3522	92.3077	300	0.5030	0.7985

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1