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

Loss: 0.3747
Accuracy: 0.9

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-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	2.4997	0.0667
No log	2.0	2	2.6037	0.1
No log	3.0	3	2.3924	0.0667
No log	4.0	4	2.3152	0.1
No log	5.0	5	2.1146	0.1667
No log	6.0	6	2.1610	0.2333
No log	7.0	7	2.1346	0.1333
No log	8.0	8	2.1400	0.1
No log	9.0	9	2.1422	0.0667
2.3217	10.0	10	2.0948	0.1333
2.3217	11.0	11	2.0994	0.2
2.3217	12.0	12	1.8570	0.3333
2.3217	13.0	13	1.9750	0.2667
2.3217	14.0	14	1.8089	0.3
2.3217	15.0	15	1.8738	0.3
2.3217	16.0	16	1.7751	0.3333
2.3217	17.0	17	1.7744	0.2
2.3217	18.0	18	1.7998	0.3333
2.3217	19.0	19	1.7048	0.2667
1.798	20.0	20	1.6367	0.4
1.798	21.0	21	1.6092	0.3
1.798	22.0	22	1.5605	0.3667
1.798	23.0	23	1.4219	0.5
1.798	24.0	24	1.5037	0.4
1.798	25.0	25	1.3966	0.4333
1.798	26.0	26	1.4327	0.4
1.798	27.0	27	1.3484	0.4
1.798	28.0	28	1.3958	0.4
1.798	29.0	29	1.2789	0.4667
1.1133	30.0	30	1.2002	0.4333
1.1133	31.0	31	1.1080	0.4667
1.1133	32.0	32	0.9814	0.6
1.1133	33.0	33	1.0498	0.5667
1.1133	34.0	34	0.9709	0.6333
1.1133	35.0	35	0.9985	0.5333
1.1133	36.0	36	0.8779	0.6667
1.1133	37.0	37	0.7959	0.7
1.1133	38.0	38	0.7583	0.7
1.1133	39.0	39	1.0074	0.5667
0.5945	40.0	40	0.6441	0.6667
0.5945	41.0	41	0.7701	0.6667
0.5945	42.0	42	0.8433	0.6667
0.5945	43.0	43	0.7998	0.6667
0.5945	44.0	44	0.7087	0.7
0.5945	45.0	45	0.5793	0.8333
0.5945	46.0	46	0.5024	0.8
0.5945	47.0	47	0.8088	0.7
0.5945	48.0	48	0.7690	0.7
0.5945	49.0	49	0.8561	0.6667
0.3008	50.0	50	0.4728	0.8667
0.3008	51.0	51	0.5935	0.6667
0.3008	52.0	52	0.3772	0.9
0.3008	53.0	53	0.6337	0.6333
0.3008	54.0	54	0.6097	0.7
0.3008	55.0	55	0.4838	0.8333
0.3008	56.0	56	0.5487	0.8333
0.3008	57.0	57	0.5395	0.8
0.3008	58.0	58	0.5078	0.7667
0.3008	59.0	59	0.4211	0.8
0.1792	60.0	60	0.4578	0.8333
0.1792	61.0	61	0.4603	0.8333
0.1792	62.0	62	0.2765	0.9
0.1792	63.0	63	0.6634	0.7333
0.1792	64.0	64	0.3247	0.9
0.1792	65.0	65	0.6290	0.6667
0.1792	66.0	66	0.5741	0.8
0.1792	67.0	67	0.3994	0.8333
0.1792	68.0	68	0.4273	0.8333
0.1792	69.0	69	0.4240	0.7333
0.1158	70.0	70	0.4269	0.8333
0.1158	71.0	71	0.4764	0.8333
0.1158	72.0	72	0.3892	0.8667
0.1158	73.0	73	0.5258	0.8
0.1158	74.0	74	0.3253	0.8333
0.1158	75.0	75	0.5055	0.7667
0.1158	76.0	76	0.6183	0.7667
0.1158	77.0	77	0.3801	0.9
0.1158	78.0	78	0.5568	0.7333
0.1158	79.0	79	0.3794	0.8333
0.0936	80.0	80	0.2896	0.9
0.0936	81.0	81	0.5924	0.7667
0.0936	82.0	82	0.5123	0.8333
0.0936	83.0	83	0.6333	0.8
0.0936	84.0	84	0.4452	0.7333
0.0936	85.0	85	0.4296	0.8333
0.0936	86.0	86	0.3000	0.8667
0.0936	87.0	87	0.3882	0.8667
0.0936	88.0	88	0.5478	0.7333
0.0936	89.0	89	0.3075	0.8667
0.0473	90.0	90	0.5298	0.8
0.0473	91.0	91	0.6640	0.7333
0.0473	92.0	92	0.4580	0.8333
0.0473	93.0	93	0.5458	0.7333
0.0473	94.0	94	0.4686	0.8333
0.0473	95.0	95	0.2982	0.8333
0.0473	96.0	96	0.4537	0.8333
0.0473	97.0	97	0.3308	0.8667
0.0473	98.0	98	0.4839	0.8
0.0473	99.0	99	0.4554	0.8
0.0443	100.0	100	0.2150	0.9667
0.0443	101.0	101	0.3185	0.9333
0.0443	102.0	102	0.2575	0.9
0.0443	103.0	103	0.3313	0.8667
0.0443	104.0	104	0.4836	0.8333
0.0443	105.0	105	0.3910	0.8667
0.0443	106.0	106	0.5569	0.8333
0.0443	107.0	107	0.4688	0.8667
0.0443	108.0	108	0.2292	0.9333
0.0443	109.0	109	0.4958	0.8
0.0353	110.0	110	0.3628	0.9
0.0353	111.0	111	0.6191	0.7333
0.0353	112.0	112	0.5096	0.8
0.0353	113.0	113	0.3478	0.9
0.0353	114.0	114	0.3585	0.8667
0.0353	115.0	115	0.3859	0.8
0.0353	116.0	116	0.3952	0.8333
0.0353	117.0	117	0.4491	0.8333
0.0353	118.0	118	0.4710	0.8
0.0353	119.0	119	0.5375	0.8
0.0292	120.0	120	0.6853	0.8333
0.0292	121.0	121	0.4836	0.8
0.0292	122.0	122	0.5246	0.8
0.0292	123.0	123	0.4446	0.8667
0.0292	124.0	124	0.4238	0.8
0.0292	125.0	125	0.3543	0.8333
0.0292	126.0	126	0.2007	0.9333
0.0292	127.0	127	0.2274	0.9333
0.0292	128.0	128	0.3778	0.8333
0.0292	129.0	129	0.4544	0.8333
0.0296	130.0	130	0.2613	0.8667
0.0296	131.0	131	0.3248	0.9
0.0296	132.0	132	0.4552	0.8
0.0296	133.0	133	0.4356	0.8333
0.0296	134.0	134	0.3427	0.9
0.0296	135.0	135	0.1513	1.0
0.0296	136.0	136	0.3139	0.8333
0.0296	137.0	137	0.3094	0.9
0.0296	138.0	138	0.3401	0.8667
0.0296	139.0	139	0.4339	0.9333
0.0178	140.0	140	0.2465	0.9
0.0178	141.0	141	0.4604	0.8667
0.0178	142.0	142	0.4860	0.8
0.0178	143.0	143	0.3710	0.8333
0.0178	144.0	144	0.4719	0.8333
0.0178	145.0	145	0.3030	0.9333
0.0178	146.0	146	0.6212	0.7667
0.0178	147.0	147	0.2716	0.9
0.0178	148.0	148	0.4297	0.8333
0.0178	149.0	149	0.3456	0.8333
0.0103	150.0	150	0.4718	0.8667
0.0103	151.0	151	0.3841	0.8333
0.0103	152.0	152	0.4124	0.9333
0.0103	153.0	153	0.2595	0.9333
0.0103	154.0	154	0.2666	0.8667
0.0103	155.0	155	0.4872	0.7333
0.0103	156.0	156	0.4039	0.8333
0.0103	157.0	157	0.3004	0.8667
0.0103	158.0	158	0.3021	0.9
0.0103	159.0	159	0.4477	0.9
0.0075	160.0	160	0.3548	0.9333
0.0075	161.0	161	0.2648	0.9333
0.0075	162.0	162	0.3269	0.9333
0.0075	163.0	163	0.5231	0.8
0.0075	164.0	164	0.2841	0.8667
0.0075	165.0	165	0.3145	0.9
0.0075	166.0	166	0.4291	0.8667
0.0075	167.0	167	0.5396	0.8333
0.0075	168.0	168	0.3873	0.9
0.0075	169.0	169	0.3150	0.9333
0.0062	170.0	170	0.3809	0.9
0.0062	171.0	171	0.2062	0.9
0.0062	172.0	172	0.3242	0.8667
0.0062	173.0	173	0.3500	0.9
0.0062	174.0	174	0.2784	0.9
0.0062	175.0	175	0.2553	0.8667
0.0062	176.0	176	0.4475	0.9
0.0062	177.0	177	0.3598	0.9333
0.0062	178.0	178	0.3488	0.8333
0.0062	179.0	179	0.2966	0.8333
0.0056	180.0	180	0.4635	0.8
0.0056	181.0	181	0.2402	0.9
0.0056	182.0	182	0.3984	0.8667
0.0056	183.0	183	0.2032	0.9
0.0056	184.0	184	0.2633	0.8333
0.0056	185.0	185	0.3015	0.9333
0.0056	186.0	186	0.3774	0.9
0.0056	187.0	187	0.5716	0.8333
0.0056	188.0	188	0.3961	0.8667
0.0056	189.0	189	0.3915	0.9
0.0048	190.0	190	0.3788	0.8333
0.0048	191.0	191	0.4823	0.8667
0.0048	192.0	192	0.3158	0.8667
0.0048	193.0	193	0.2184	0.8667
0.0048	194.0	194	0.3363	0.8667
0.0048	195.0	195	0.3996	0.9
0.0048	196.0	196	0.2263	0.8333
0.0048	197.0	197	0.4634	0.8333
0.0048	198.0	198	0.3492	0.8667
0.0048	199.0	199	0.3086	0.9
0.0034	200.0	200	0.3747	0.9

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

ricardoSLabs/jaffe_V2_200_1

jaffe_V2_200_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ricardoSLabs/
jaffe_V2_200_1