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

Loss: 0.6378
Accuracy: 0.8696

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: 5.5e-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: 0.05
num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	6	1.3853	0.3261
1.3854	2.0	13	1.3764	0.6087
1.3854	2.92	19	1.3484	0.5870
1.3679	4.0	26	1.2873	0.5
1.2945	4.92	32	1.2122	0.4130
1.2945	6.0	39	1.1105	0.4130
1.1527	6.92	45	1.0386	0.5652
0.9999	8.0	52	0.9454	0.7174
0.9999	8.92	58	0.8886	0.7174
0.8606	10.0	65	0.7935	0.8261
0.7153	10.92	71	0.7424	0.7826
0.7153	12.0	78	0.6803	0.8043
0.5691	12.92	84	0.6104	0.8261
0.4187	14.0	91	0.5848	0.8043
0.4187	14.92	97	0.5254	0.8478
0.3203	16.0	104	0.5790	0.8261
0.2248	16.92	110	0.6315	0.7826
0.2248	18.0	117	0.7864	0.7391
0.2384	18.92	123	0.6028	0.8043
0.2437	20.0	130	0.6135	0.8043
0.2437	20.92	136	0.6210	0.7826
0.2309	22.0	143	0.6329	0.8043
0.2309	22.92	149	0.6236	0.8261
0.1367	24.0	156	0.6919	0.7826
0.1318	24.92	162	0.7770	0.7391
0.1318	26.0	169	0.7394	0.7609
0.1228	26.92	175	0.5662	0.8261
0.1173	28.0	182	0.8995	0.7391
0.1173	28.92	188	0.6780	0.7826
0.129	30.0	195	0.7868	0.7826
0.1043	30.92	201	0.7302	0.8261
0.1043	32.0	208	0.7549	0.7826
0.0917	32.92	214	0.6124	0.7826
0.0843	34.0	221	0.6607	0.8261
0.0843	34.92	227	0.6816	0.8261
0.1054	36.0	234	0.6349	0.7826
0.0923	36.92	240	0.7346	0.8261
0.0923	38.0	247	0.7571	0.8043
0.0879	38.92	253	0.7625	0.7826
0.0632	40.0	260	0.7908	0.7826
0.0632	40.92	266	0.8490	0.7826
0.0533	42.0	273	0.8177	0.8043
0.0533	42.92	279	0.8878	0.7826
0.0633	44.0	286	0.6725	0.8043
0.0526	44.92	292	0.7090	0.8261
0.0526	46.0	299	0.7725	0.8043
0.0716	46.92	305	0.7965	0.8043
0.0783	48.0	312	0.9016	0.8043
0.0783	48.92	318	0.9555	0.7826
0.0789	50.0	325	0.9379	0.7609
0.0418	50.92	331	0.7863	0.8043
0.0418	52.0	338	0.7688	0.8261
0.0483	52.92	344	0.7040	0.8261
0.0493	54.0	351	0.7560	0.8043
0.0493	54.92	357	0.9141	0.7609
0.0554	56.0	364	0.7642	0.8043
0.0612	56.92	370	0.7923	0.8478
0.0612	58.0	377	0.8156	0.8478
0.0468	58.92	383	0.6847	0.8043
0.0419	60.0	390	0.6378	0.8696
0.0419	60.92	396	0.8031	0.8261
0.0436	62.0	403	0.7883	0.8478
0.0436	62.92	409	0.8270	0.8478
0.0429	64.0	416	0.8654	0.8261
0.0438	64.92	422	0.7054	0.8478
0.0438	66.0	429	0.6511	0.8696
0.0378	66.92	435	0.7341	0.8478
0.0294	68.0	442	0.8695	0.8478
0.0294	68.92	448	0.8984	0.8043
0.0362	70.0	455	0.9207	0.8261
0.0367	70.92	461	0.9426	0.7826
0.0367	72.0	468	0.9156	0.8261
0.0332	72.92	474	0.9034	0.8043
0.0294	74.0	481	0.9086	0.7826
0.0294	74.92	487	0.8890	0.8043
0.0285	76.0	494	0.8999	0.8261
0.0232	76.92	500	0.9546	0.7826
0.0232	78.0	507	0.9126	0.8043
0.0349	78.92	513	0.9537	0.8043
0.0393	80.0	520	0.9870	0.8043
0.0393	80.92	526	0.9763	0.8043
0.0225	82.0	533	0.9384	0.8043
0.0225	82.92	539	0.8600	0.8478
0.0304	84.0	546	0.8530	0.8478
0.0263	84.92	552	0.8588	0.8043
0.0263	86.0	559	0.8635	0.8043
0.0186	86.92	565	0.8602	0.8261
0.0258	88.0	572	0.8514	0.8261
0.0258	88.92	578	0.8431	0.8261
0.0161	90.0	585	0.8046	0.8261
0.0208	90.92	591	0.8082	0.8261
0.0208	92.0	598	0.8276	0.8043
0.0331	92.92	604	0.7698	0.8261
0.0322	94.0	611	0.8191	0.8261
0.0322	94.92	617	0.9046	0.8043
0.0284	96.0	624	0.9535	0.8043
0.0187	96.92	630	0.9304	0.8043
0.0187	98.0	637	0.8834	0.8043
0.0209	98.92	643	0.8519	0.8043
0.027	100.0	650	0.8522	0.8261
0.027	100.92	656	0.8978	0.8261
0.0218	102.0	663	0.9194	0.8261
0.0218	102.92	669	0.9140	0.8261
0.021	104.0	676	0.9173	0.8261
0.0179	104.92	682	0.9279	0.8261
0.0179	106.0	689	0.9263	0.8261
0.0167	106.92	695	0.9158	0.8261
0.0229	108.0	702	0.9109	0.8261
0.0229	108.92	708	0.9065	0.8261
0.0219	110.0	715	0.9011	0.8261
0.0271	110.77	720	0.9002	0.8261

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Augusto777/vit-base-patch16-224-ve-U13-b-120

vit-base-patch16-224-ve-U13-b-120

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Augusto777/
vit-base-patch16-224-ve-U13-b-120