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: 0.1916
Ordinal Mae: 0.4221
Ordinal Accuracy: 0.6828
Na Accuracy: 0.8591

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: 2e-05
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: 1.5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Ordinal Mae	Ordinal Accuracy	Na Accuracy
0.4436	0.04	100	0.3698	0.8706	0.3332	0.7990
0.3143	0.07	200	0.3215	0.8555	0.4017	0.8093
0.3385	0.11	300	0.2997	0.8303	0.4485	0.8591
0.3127	0.14	400	0.2889	0.8013	0.4881	0.8746
0.3054	0.18	500	0.2804	0.7619	0.5325	0.8780
0.3051	0.22	600	0.2752	0.7215	0.5235	0.9158
0.2833	0.25	700	0.2653	0.6807	0.5487	0.8969
0.2907	0.29	800	0.2550	0.6432	0.5618	0.8351
0.2468	0.32	900	0.2522	0.6119	0.5972	0.8058
0.2199	0.36	1000	0.2437	0.6023	0.6062	0.8127
0.2219	0.4	1100	0.2361	0.5574	0.5959	0.9038
0.2071	0.43	1200	0.2387	0.5439	0.6175	0.7715
0.2214	0.47	1300	0.2341	0.5257	0.6232	0.7955
0.2627	0.5	1400	0.2315	0.5152	0.6124	0.7990
0.2067	0.54	1500	0.2247	0.5026	0.6396	0.8110
0.2086	0.58	1600	0.2192	0.4955	0.6589	0.8041
0.1993	0.61	1700	0.2182	0.4738	0.6522	0.8127
0.1962	0.65	1800	0.2211	0.4858	0.6232	0.9141
0.1882	0.69	1900	0.2045	0.4669	0.6632	0.8625
0.1895	0.72	2000	0.2082	0.4696	0.6316	0.8608
0.1979	0.76	2100	0.2270	0.4791	0.6373	0.9003
0.2643	0.79	2200	0.2069	0.4663	0.6414	0.8557
0.2279	0.83	2300	0.2030	0.4581	0.6543	0.8694
0.1965	0.87	2400	0.2109	0.4446	0.6820	0.8007
0.1637	0.9	2500	0.2005	0.4439	0.6763	0.8557
0.1705	0.94	2600	0.1964	0.4321	0.6748	0.8540
0.2412	0.97	2700	0.1958	0.4345	0.6730	0.8780
0.1438	1.01	2800	0.1972	0.4301	0.6784	0.8471
0.123	1.05	2900	0.1995	0.4231	0.6753	0.8419
0.1411	1.08	3000	0.1946	0.4220	0.6817	0.8454
0.1443	1.12	3100	0.1916	0.4221	0.6828	0.8591
0.208	1.15	3200	0.1942	0.4163	0.6740	0.8677
0.1343	1.19	3300	0.1962	0.4182	0.6889	0.8471
0.1347	1.23	3400	0.1938	0.4161	0.6900	0.8660
0.1076	1.26	3500	0.1970	0.4181	0.6943	0.8471
0.1248	1.3	3600	0.1951	0.4151	0.6959	0.8471
0.1455	1.33	3700	0.1952	0.4147	0.6851	0.8814
0.131	1.37	3800	0.1953	0.4172	0.6948	0.8454
0.1307	1.41	3900	0.1932	0.4127	0.6928	0.8643
0.1198	1.44	4000	0.1947	0.4110	0.6941	0.8574
0.1363	1.48	4100	0.1952	0.4087	0.6887	0.8574

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

rshrott/ryan03312024_lr_2e-5_wd_001

ryan03312024_lr_2e-5_wd_001

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rshrott/
ryan03312024_lr_2e-5_wd_001