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

Loss: 0.2238
Ordinal Mae: 0.4441
Ordinal Accuracy: 0.6446
Na Accuracy: 0.7992

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: 0.0001
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	Ordinal Mae	Ordinal Accuracy	Na Accuracy
0.3421	0.04	100	0.3331	0.8749	0.3817	0.6911
0.2813	0.09	200	0.3000	0.7492	0.5117	0.7954
0.2619	0.13	300	0.3019	0.6841	0.5273	0.7046
0.2863	0.17	400	0.2960	0.6538	0.5097	0.7336
0.2159	0.22	500	0.2602	0.5404	0.5660	0.8243
0.2235	0.26	600	0.2557	0.5015	0.5874	0.7780
0.285	0.31	700	0.2564	0.5000	0.6180	0.6853
0.2028	0.35	800	0.2862	0.6338	0.5068	0.7220
0.2006	0.39	900	0.2495	0.4830	0.6299	0.7587
0.2663	0.44	1000	0.2660	0.4893	0.6021	0.8610
0.2062	0.48	1100	0.2481	0.4713	0.6267	0.8436
0.1749	0.52	1200	0.2586	0.4959	0.6423	0.6737
0.2197	0.57	1300	0.2349	0.4841	0.5981	0.8031
0.2073	0.61	1400	0.2587	0.4878	0.6013	0.6950
0.1915	0.66	1500	0.2393	0.4771	0.6322	0.7683
0.2374	0.7	1600	0.2238	0.4441	0.6446	0.7992
0.2278	0.74	1700	0.2453	0.4410	0.6539	0.7278
0.2033	0.79	1800	0.2251	0.4584	0.6299	0.8185
0.1843	0.83	1900	0.2280	0.4446	0.6513	0.8127
0.1878	0.87	2000	0.2277	0.4454	0.6492	0.8127
0.2608	0.92	2100	0.2309	0.4517	0.6192	0.8494
0.201	0.96	2200	0.2459	0.4654	0.6406	0.7278
0.1736	1.0	2300	0.2438	0.4474	0.6475	0.7201
0.1374	1.05	2400	0.2368	0.4145	0.6622	0.7799
0.1334	1.09	2500	0.2424	0.4105	0.6732	0.7510
0.1319	1.14	2600	0.2336	0.4155	0.6712	0.7741
0.1549	1.18	2700	0.2525	0.4040	0.6625	0.7587
0.116	1.22	2800	0.2501	0.4425	0.6371	0.7664
0.1358	1.27	2900	0.2324	0.4136	0.6498	0.8185
0.1614	1.31	3000	0.2637	0.4353	0.6316	0.7915
0.1395	1.35	3100	0.2446	0.4020	0.6726	0.8012
0.1208	1.4	3200	0.2465	0.3946	0.6764	0.8243
0.1432	1.44	3300	0.2552	0.3919	0.6576	0.8900
0.1358	1.48	3400	0.2561	0.3984	0.6796	0.7896
0.0877	1.53	3500	0.2381	0.3901	0.6822	0.7876
0.1212	1.57	3600	0.2600	0.4001	0.6949	0.7259
0.1917	1.62	3700	0.2459	0.3889	0.6894	0.7819
0.1175	1.66	3800	0.2444	0.3937	0.6819	0.7741
0.1522	1.7	3900	0.2473	0.4010	0.6608	0.8050
0.1027	1.75	4000	0.2354	0.4208	0.6478	0.7838
0.1343	1.79	4100	0.2284	0.3977	0.6744	0.7992
0.1552	1.83	4200	0.2607	0.4045	0.6715	0.7780
0.1172	1.88	4300	0.2421	0.3971	0.6666	0.8282
0.1381	1.92	4400	0.2253	0.3813	0.6793	0.7857
0.1282	1.97	4500	0.2335	0.4146	0.6510	0.8436
0.0734	2.01	4600	0.2382	0.3802	0.6897	0.7896
0.1046	2.05	4700	0.2358	0.3695	0.6874	0.8012
0.0529	2.1	4800	0.2463	0.3596	0.7096	0.7934
0.0687	2.14	4900	0.2615	0.3921	0.6738	0.7857
0.0613	2.18	5000	0.2543	0.3651	0.6877	0.8108
0.0591	2.23	5100	0.2539	0.3693	0.6885	0.7915
0.0474	2.27	5200	0.2650	0.3722	0.6836	0.7992
0.0511	2.31	5300	0.2631	0.3681	0.6868	0.8127
0.0683	2.36	5400	0.2714	0.3630	0.6955	0.7838
0.0654	2.4	5500	0.2769	0.3673	0.6787	0.7992
0.0581	2.45	5600	0.2777	0.3628	0.6952	0.7992
0.072	2.49	5700	0.2919	0.3610	0.6888	0.7683
0.0737	2.53	5800	0.2807	0.3612	0.6984	0.7838
0.0667	2.58	5900	0.2926	0.3607	0.7001	0.7510
0.0669	2.62	6000	0.2875	0.3616	0.6891	0.7992
0.0535	2.66	6100	0.2854	0.3565	0.6960	0.7683
0.06	2.71	6200	0.2847	0.3501	0.7015	0.7741
0.0534	2.75	6300	0.2821	0.3495	0.7007	0.7625
0.0526	2.79	6400	0.2834	0.3853	0.6700	0.7625
0.0841	2.84	6500	0.2839	0.3504	0.7044	0.7490
0.0529	2.88	6600	0.2858	0.3595	0.6897	0.7819
0.0811	2.93	6700	0.2843	0.3480	0.7047	0.7799
0.0502	2.97	6800	0.2892	0.3483	0.7010	0.7819
0.0273	3.01	6900	0.2801	0.3454	0.6958	0.8108
0.0306	3.06	7000	0.2782	0.3444	0.7024	0.8031
0.0257	3.1	7100	0.2797	0.3352	0.7085	0.7934
0.0241	3.14	7200	0.2828	0.3343	0.7059	0.7954
0.0255	3.19	7300	0.2890	0.3364	0.6981	0.8050
0.0245	3.23	7400	0.2906	0.3392	0.7044	0.7992
0.0232	3.28	7500	0.2891	0.3338	0.7036	0.7857
0.0352	3.32	7600	0.2908	0.3443	0.6926	0.7896
0.0376	3.36	7700	0.2877	0.3315	0.7050	0.7915
0.025	3.41	7800	0.2889	0.3316	0.7076	0.7896
0.0225	3.45	7900	0.2902	0.3286	0.7070	0.7819
0.024	3.49	8000	0.2902	0.3270	0.7102	0.7954
0.0404	3.54	8100	0.2950	0.3294	0.7053	0.7896
0.0221	3.58	8200	0.2924	0.3271	0.7093	0.7934
0.0182	3.62	8300	0.2921	0.3237	0.7105	0.7934
0.0304	3.67	8400	0.2911	0.3231	0.7134	0.7857
0.0193	3.71	8500	0.2915	0.3221	0.7166	0.7838
0.0223	3.76	8600	0.2931	0.3235	0.7122	0.7896
0.0254	3.8	8700	0.2947	0.3214	0.7174	0.7876
0.0215	3.84	8800	0.2936	0.3202	0.7128	0.7857
0.0312	3.89	8900	0.2956	0.3210	0.7134	0.7857
0.0189	3.93	9000	0.2946	0.3210	0.7125	0.7876
0.021	3.97	9100	0.2949	0.3194	0.7145	0.7876

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

rshrott/ryan03282024

ryan03282024

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rshrott/
ryan03282024