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Cem13/
med_complication_classifaction

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vit-base-beans-demo-v5

This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 2.6708
Accuracy: 0.4679

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.9636	0.06	100	5.7983	0.1
5.8053	0.11	200	5.8683	0.1110
5.9476	0.17	300	5.9242	0.1006
5.6866	0.23	400	5.6640	0.1110
5.5886	0.29	500	5.6032	0.1153
5.4108	0.34	600	5.5314	0.1179
5.4427	0.4	700	5.4592	0.1188
5.1333	0.46	800	5.3569	0.1272
5.2427	0.52	900	5.2451	0.1318
5.2185	0.57	1000	5.1948	0.1355
4.777	0.63	1100	5.1379	0.1361
5.2378	0.69	1200	5.1043	0.1347
5.2246	0.74	1300	5.0783	0.1419
4.9846	0.8	1400	5.0425	0.1390
5.2708	0.86	1500	5.0202	0.1387
4.9169	0.92	1600	4.9382	0.1526
4.8091	0.97	1700	4.8691	0.1497
4.8795	1.03	1800	4.8124	0.1546
4.6634	1.09	1900	4.7816	0.1601
4.4967	1.15	2000	4.7105	0.1618
4.8389	1.2	2100	4.7104	0.1671
4.5872	1.26	2200	4.6636	0.1607
4.7063	1.32	2300	4.6506	0.1584
4.5526	1.38	2400	4.5932	0.1743
4.4984	1.43	2500	4.5266	0.1792
4.2266	1.49	2600	4.4860	0.1850
4.5827	1.55	2700	4.4237	0.1844
3.9383	1.6	2800	4.3919	0.1887
4.5361	1.66	2900	4.3408	0.1971
4.5067	1.72	3000	4.2708	0.1965
4.3133	1.78	3100	4.2283	0.1997
4.4104	1.83	3200	4.1830	0.2061
3.965	1.89	3300	4.1360	0.2133
4.3425	1.95	3400	4.0754	0.2237
3.9526	2.01	3500	4.0885	0.2188
3.9037	2.06	3600	3.9629	0.2396
3.6883	2.12	3700	4.0130	0.2289
3.8445	2.18	3800	3.9220	0.2540
3.6093	2.23	3900	3.9453	0.2353
3.7109	2.29	4000	3.8822	0.2402
3.588	2.35	4100	3.7765	0.2679
3.4878	2.41	4200	3.7138	0.2821
3.8276	2.46	4300	3.7137	0.2694
3.7288	2.52	4400	3.6505	0.2821
3.4948	2.58	4500	3.6280	0.2835
3.3436	2.64	4600	3.5212	0.3145
3.3389	2.69	4700	3.5006	0.3208
3.4803	2.75	4800	3.4130	0.3361
3.3953	2.81	4900	3.3506	0.3370
3.3648	2.87	5000	3.3132	0.3462
3.1838	2.92	5100	3.2632	0.3543
3.1927	2.98	5200	3.2335	0.3613
2.8337	3.04	5300	3.1633	0.3760
2.6126	3.09	5400	3.1287	0.3803
2.7718	3.15	5500	3.0715	0.3876
2.7694	3.21	5600	3.0283	0.4040
2.7131	3.27	5700	2.9859	0.4040
2.6204	3.32	5800	2.9461	0.4078
2.4889	3.38	5900	2.9413	0.4081
2.5283	3.44	6000	2.9001	0.4147
2.6986	3.5	6100	2.8428	0.4335
2.8514	3.55	6200	2.8352	0.4399
2.2355	3.61	6300	2.7825	0.4462
2.4485	3.67	6400	2.7580	0.4535
2.3359	3.72	6500	2.7330	0.4549
2.5904	3.78	6600	2.7096	0.4613
2.5366	3.84	6700	2.6906	0.4642
2.3954	3.9	6800	2.6797	0.4691
2.3722	3.95	6900	2.6708	0.4679

Framework versions

Transformers 4.28.0
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.13.3

Cem13/med_complication_classifaction

vit-base-beans-demo-v5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Cem13/
med_complication_classifaction