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Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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vit-msn-small-lateralflowivalidationtraintest_7

This model is a fine-tuned version of Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7 on the imagefolder dataset. It achieves the following results on the evaluation set:

—Loss: 0.4160
—Accuracy: 0.8791

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: 1e-05
—trainbatchsize: 64
—evalbatchsize: 64
—seed: 42
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 256
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.2
—num_epochs: 100
—labelsmoothingfactor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9231	3	0.4160	0.8791
No log	1.8462	6	0.4668	0.8388
No log	2.7692	9	0.5433	0.8022
0.3869	4.0	13	0.5052	0.8168
0.3869	4.9231	16	0.4591	0.8571
0.3869	5.8462	19	0.4820	0.8278
0.3658	6.7692	22	0.4953	0.8095
0.3658	8.0	26	0.4497	0.8608
0.3658	8.9231	29	0.4686	0.8315
0.3439	9.8462	32	0.4506	0.8608
0.3439	10.7692	35	0.4859	0.8168
0.3439	12.0	39	0.4929	0.8168
0.3416	12.9231	42	0.4957	0.8059
0.3416	13.8462	45	0.5229	0.7875
0.3416	14.7692	48	0.4473	0.8535
0.324	16.0	52	0.5260	0.8059
0.324	16.9231	55	0.4582	0.8462
0.324	17.8462	58	0.5299	0.7839
0.3273	18.7692	61	0.4947	0.8205
0.3273	20.0	65	0.5393	0.7692
0.3273	20.9231	68	0.4916	0.8278
0.3397	21.8462	71	0.5360	0.7802
0.3397	22.7692	74	0.5661	0.7656
0.3397	24.0	78	0.6354	0.7216
0.3344	24.9231	81	0.6782	0.7033
0.3344	25.8462	84	0.5704	0.7582
0.3344	26.7692	87	0.6537	0.6777
0.3325	28.0	91	0.4798	0.8425
0.3325	28.9231	94	0.5158	0.8059
0.3325	29.8462	97	0.5408	0.7912
0.3283	30.7692	100	0.5964	0.7399
0.3283	32.0	104	0.5069	0.8205
0.3283	32.9231	107	0.5396	0.7875
0.3229	33.8462	110	0.5203	0.7985
0.3229	34.7692	113	0.5464	0.7875
0.3229	36.0	117	0.5890	0.7509
0.3207	36.9231	120	0.5080	0.8132
0.3207	37.8462	123	0.4944	0.8168
0.3207	38.7692	126	0.4968	0.8095
0.3286	40.0	130	0.4874	0.8132
0.3286	40.9231	133	0.5013	0.8059
0.3286	41.8462	136	0.5329	0.7656
0.3286	42.7692	139	0.6199	0.6996
0.3154	44.0	143	0.4854	0.8059
0.3154	44.9231	146	0.5545	0.7509
0.3154	45.8462	149	0.5267	0.7729
0.3119	46.7692	152	0.5214	0.7802
0.3119	48.0	156	0.5265	0.7839
0.3119	48.9231	159	0.5137	0.7985
0.3036	49.8462	162	0.5354	0.7839
0.3036	50.7692	165	0.5269	0.7875
0.3036	52.0	169	0.5797	0.7399
0.2995	52.9231	172	0.6258	0.7179
0.2995	53.8462	175	0.5512	0.7692
0.2995	54.7692	178	0.5517	0.7619
0.306	56.0	182	0.5590	0.7546
0.306	56.9231	185	0.5514	0.7619
0.306	57.8462	188	0.5597	0.7509
0.2989	58.7692	191	0.5957	0.7326
0.2989	60.0	195	0.5366	0.7766
0.2989	60.9231	198	0.5465	0.7729
0.2931	61.8462	201	0.6171	0.7253
0.2931	62.7692	204	0.5768	0.7509
0.2931	64.0	208	0.5706	0.7509
0.299	64.9231	211	0.5962	0.7363
0.299	65.8462	214	0.6220	0.7216
0.299	66.7692	217	0.5929	0.7363
0.2969	68.0	221	0.6136	0.7253
0.2969	68.9231	224	0.6092	0.7289
0.2969	69.8462	227	0.6029	0.7253
0.3015	70.7692	230	0.5356	0.7766
0.3015	72.0	234	0.5376	0.7692
0.3015	72.9231	237	0.5886	0.7436
0.2919	73.8462	240	0.5869	0.7436
0.2919	74.7692	243	0.5846	0.7473
0.2919	76.0	247	0.5507	0.7656
0.288	76.9231	250	0.5801	0.7509
0.288	77.8462	253	0.6077	0.7399
0.288	78.7692	256	0.5848	0.7436
0.2951	80.0	260	0.5435	0.7692
0.2951	80.9231	263	0.5638	0.7656
0.2951	81.8462	266	0.5795	0.7399
0.2951	82.7692	269	0.5774	0.7509
0.2875	84.0	273	0.5703	0.7509
0.2875	84.9231	276	0.5713	0.7509
0.2875	85.8462	279	0.5784	0.7473
0.2855	86.7692	282	0.5904	0.7436
0.2855	88.0	286	0.5917	0.7326
0.2855	88.9231	289	0.5860	0.7473
0.2964	89.8462	292	0.5858	0.7473
0.2964	90.7692	295	0.5823	0.7436
0.2964	92.0	299	0.5817	0.7436
0.291	92.3077	300	0.5816	0.7436

Framework versions

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