vit_fold_2_v3

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: 1.1369
• Accuracy: 0.9551
• F1 Score: 0.9563
• Recall: 0.9560

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
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.8711	1.0	20	2.9097	0.2532	0.2497	0.2611
2.6539	2.0	40	2.6799	0.3910	0.3842	0.3928
2.2936	3.0	60	2.3974	0.5609	0.5597	0.5774
1.8549	4.0	80	2.0469	0.7115	0.7136	0.7281
1.5368	5.0	100	1.6885	0.8141	0.8169	0.8230
1.3205	6.0	120	1.4580	0.8814	0.8823	0.8864
1.1521	7.0	140	1.3361	0.9006	0.9020	0.9004
1.1270	8.0	160	1.2874	0.9103	0.9118	0.9105
1.0616	9.0	180	1.2559	0.9231	0.9242	0.9269
1.0597	10.0	200	1.2492	0.9071	0.9075	0.9049
1.0621	11.0	220	1.2381	0.9103	0.9096	0.9073
1.0121	12.0	240	1.2098	0.9231	0.9238	0.9224
1.0036	13.0	260	1.1897	0.9391	0.9392	0.9408
1.0230	14.0	280	1.1786	0.9327	0.9335	0.9318
0.9965	15.0	300	1.1717	0.9455	0.9465	0.9481
1.0003	16.0	320	1.1839	0.9327	0.9323	0.9313
0.9968	17.0	340	1.1635	0.9423	0.9438	0.9434
0.9940	18.0	360	1.1561	0.9359	0.9375	0.9395
0.9948	19.0	380	1.1597	0.9423	0.9438	0.9434
0.9795	20.0	400	1.1576	0.9455	0.9467	0.9459
0.9752	21.0	420	1.1580	0.9391	0.9403	0.9417
0.9768	22.0	440	1.1596	0.9423	0.9435	0.9422
0.9644	23.0	460	1.1587	0.9359	0.9372	0.9370
0.9746	24.0	480	1.1708	0.9455	0.9460	0.9434
0.9711	25.0	500	1.1564	0.9391	0.9406	0.9398
0.9689	26.0	520	1.1522	0.9391	0.9406	0.9398
0.9710	27.0	540	1.1525	0.9455	0.9466	0.9447
0.9754	28.0	560	1.1468	0.9455	0.9467	0.9459
0.9686	29.0	580	1.1488	0.9455	0.9466	0.9451
0.9678	30.0	600	1.1476	0.9487	0.9496	0.9483
0.9690	31.0	620	1.1462	0.9455	0.9467	0.9459
0.9630	32.0	640	1.1533	0.9487	0.9494	0.9471
0.9621	33.0	660	1.1516	0.9551	0.9555	0.9532
0.9670	34.0	680	1.1439	0.9487	0.9502	0.9499
0.9765	35.0	700	1.1464	0.9487	0.9502	0.9499
0.9769	36.0	720	1.1411	0.9455	0.9467	0.9459
0.9649	37.0	740	1.1353	0.9487	0.9496	0.9483
0.9563	38.0	760	1.1369	0.9551	0.9563	0.9560
0.9635	39.0	780	1.1267	0.9519	0.9533	0.9535
0.9667	40.0	800	1.1389	0.9455	0.9464	0.9447
0.9635	41.0	820	1.1282	0.9487	0.9497	0.9496
0.9552	42.0	840	1.1330	0.9551	0.9555	0.9532
0.9688	43.0	860	1.1279	0.9519	0.9525	0.9508
0.9655	44.0	880	1.1347	0.9551	0.9563	0.9560
0.9648	45.0	900	1.1343	0.9519	0.9531	0.9523
0.9791	46.0	920	1.1304	0.9551	0.9557	0.9545
0.9625	47.0	940	1.1320	0.9487	0.9499	0.9508
0.9636	48.0	960	1.1402	0.9519	0.9525	0.9508
0.9667	49.0	980	1.1401	0.9551	0.9561	0.9548

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2