hf_train_output

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

• Loss: 0.3894
• Accuracy: 0.9258

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: 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: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5619	0.55	50	0.5432	0.7692
0.4582	1.1	100	0.4435	0.8352
0.3548	1.65	150	0.3739	0.8599
0.217	2.2	200	0.2913	0.9093
0.1709	2.75	250	0.2619	0.9148
0.0919	3.3	300	0.2475	0.9148
0.0652	3.85	350	0.3275	0.8901
0.0495	4.4	400	0.2515	0.9093
0.0321	4.95	450	0.2878	0.9066
0.0247	5.49	500	0.2612	0.9148
0.017	6.04	550	0.2687	0.9176
0.0131	6.59	600	0.3062	0.9093
0.0113	7.14	650	0.2587	0.9231
0.0099	7.69	700	0.2815	0.9203
0.009	8.24	750	0.2675	0.9286
0.0084	8.79	800	0.2711	0.9286
0.0077	9.34	850	0.2663	0.9313
0.0073	9.89	900	0.3003	0.9258
0.0069	10.44	950	0.2758	0.9313
0.0064	10.99	1000	0.2999	0.9258
0.0061	11.54	1050	0.2931	0.9313
0.0057	12.09	1100	0.2989	0.9313
0.0056	12.64	1150	0.2974	0.9313
0.0053	13.19	1200	0.3099	0.9258
0.005	13.74	1250	0.3131	0.9313
0.0049	14.29	1300	0.3201	0.9258
0.0046	14.84	1350	0.3109	0.9313
0.0045	15.38	1400	0.3168	0.9313
0.0043	15.93	1450	0.3226	0.9231
0.0042	16.48	1500	0.3234	0.9231
0.0041	17.03	1550	0.3283	0.9258
0.0039	17.58	1600	0.3304	0.9258
0.0038	18.13	1650	0.3321	0.9231
0.0037	18.68	1700	0.3362	0.9231
0.0036	19.23	1750	0.3307	0.9286
0.0035	19.78	1800	0.3357	0.9231
0.0034	20.33	1850	0.3244	0.9313
0.0033	20.88	1900	0.3497	0.9231
0.0032	21.43	1950	0.3443	0.9231
0.0031	21.98	2000	0.3398	0.9286
0.003	22.53	2050	0.3388	0.9286
0.003	23.08	2100	0.3399	0.9286
0.0029	23.63	2150	0.3548	0.9231
0.0028	24.18	2200	0.3475	0.9286
0.0028	24.73	2250	0.3480	0.9286
0.0027	25.27	2300	0.3542	0.9231
0.0026	25.82	2350	0.3589	0.9231
0.0026	26.37	2400	0.3449	0.9286
0.0025	26.92	2450	0.3604	0.9231
0.0025	27.47	2500	0.3493	0.9286
0.0024	28.02	2550	0.3631	0.9258
0.0024	28.57	2600	0.3590	0.9258
0.0023	29.12	2650	0.3604	0.9258
0.0023	29.67	2700	0.3667	0.9258
0.0022	30.22	2750	0.3571	0.9286
0.0022	30.77	2800	0.3660	0.9258
0.0021	31.32	2850	0.3638	0.9286
0.0021	31.87	2900	0.3729	0.9258
0.0021	32.42	2950	0.3706	0.9258
0.002	32.97	3000	0.3669	0.9286
0.002	33.52	3050	0.3740	0.9258
0.002	34.07	3100	0.3693	0.9286
0.002	34.62	3150	0.3700	0.9286
0.0019	35.16	3200	0.3752	0.9258
0.0019	35.71	3250	0.3753	0.9258
0.0019	36.26	3300	0.3721	0.9286
0.0018	36.81	3350	0.3764	0.9258
0.0018	37.36	3400	0.3758	0.9258
0.0018	37.91	3450	0.3775	0.9258
0.0018	38.46	3500	0.3812	0.9258
0.0018	39.01	3550	0.3817	0.9258
0.0017	39.56	3600	0.3815	0.9258
0.0017	40.11	3650	0.3825	0.9258
0.0017	40.66	3700	0.3852	0.9258
0.0017	41.21	3750	0.3854	0.9258
0.0017	41.76	3800	0.3823	0.9258
0.0016	42.31	3850	0.3829	0.9258
0.0016	42.86	3900	0.3873	0.9258
0.0016	43.41	3950	0.3842	0.9258
0.0016	43.96	4000	0.3857	0.9258
0.0016	44.51	4050	0.3873	0.9258
0.0016	45.05	4100	0.3878	0.9258
0.0016	45.6	4150	0.3881	0.9258
0.0016	46.15	4200	0.3888	0.9258
0.0016	46.7	4250	0.3891	0.9258
0.0016	47.25	4300	0.3878	0.9258
0.0016	47.8	4350	0.3890	0.9258
0.0016	48.35	4400	0.3890	0.9258
0.0015	48.9	4450	0.3895	0.9258
0.0015	49.45	4500	0.3896	0.9258
0.0015	50.0	4550	0.3894	0.9258

Framework versions

• Transformers 4.24.0
• Pytorch 1.12.1+cu113
• Datasets 2.7.0
• Tokenizers 0.13.2