gockle_v2

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

• Loss: 0.9618
• Accuracy: 0.7844

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-06
• train_batch_size: 32
• eval_batch_size: 8
• seed: 11
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.7231	0.64	100	2.6467	0.2279
2.3217	1.28	200	2.4386	0.2288
2.0819	1.92	300	2.2887	0.2815
1.9583	2.56	400	2.1686	0.4501
1.8098	3.21	500	2.0731	0.5085
1.7511	3.85	600	1.9978	0.5320
1.6581	4.49	700	1.9233	0.5584
1.6094	5.13	800	1.8703	0.5706
1.5241	5.77	900	1.8192	0.6017
1.501	6.41	1000	1.7757	0.6111
1.4308	7.05	1100	1.7415	0.6281
1.3985	7.69	1200	1.7015	0.6375
1.3559	8.33	1300	1.6652	0.6403
1.3092	8.97	1400	1.6290	0.6488
1.3059	9.62	1500	1.6142	0.6620
1.2597	10.26	1600	1.5771	0.6704
1.2147	10.9	1700	1.5501	0.6902
1.1942	11.54	1800	1.5288	0.6911
1.1668	12.18	1900	1.5081	0.6902
1.1371	12.82	2000	1.4883	0.6949
1.1256	13.46	2100	1.4770	0.6930
1.0922	14.1	2200	1.4500	0.7081
1.0559	14.74	2300	1.4369	0.7072
1.054	15.38	2400	1.4157	0.7128
1.0465	16.03	2500	1.3899	0.7279
0.9965	16.67	2600	1.3734	0.7194
0.9876	17.31	2700	1.3603	0.7298
0.9791	17.95	2800	1.3422	0.7298
0.9551	18.59	2900	1.3309	0.7373
0.9313	19.23	3000	1.3223	0.7335
0.9211	19.87	3100	1.3052	0.7345
0.9071	20.51	3200	1.2897	0.7420
0.875	21.15	3300	1.2762	0.7561
0.8676	21.79	3400	1.2657	0.7542
0.8498	22.44	3500	1.2575	0.7580
0.8529	23.08	3600	1.2435	0.7542
0.8341	23.72	3700	1.2369	0.7561
0.8056	24.36	3800	1.2306	0.7533
0.8038	25.0	3900	1.2181	0.7665
0.7733	25.64	4000	1.2031	0.7655
0.7834	26.28	4100	1.2015	0.7637
0.7697	26.92	4200	1.1887	0.7637
0.7438	27.56	4300	1.1788	0.7674
0.733	28.21	4400	1.1740	0.7637
0.7244	28.85	4500	1.1671	0.7674
0.7091	29.49	4600	1.1563	0.7693
0.7138	30.13	4700	1.1543	0.7665
0.693	30.77	4800	1.1445	0.7665
0.6837	31.41	4900	1.1348	0.7731
0.6706	32.05	5000	1.1282	0.7702
0.6514	32.69	5100	1.1222	0.7712
0.6513	33.33	5200	1.1323	0.7665
0.6517	33.97	5300	1.1138	0.7693
0.637	34.62	5400	1.1014	0.7712
0.6277	35.26	5500	1.0949	0.7759
0.6103	35.9	5600	1.0882	0.7759
0.5916	36.54	5700	1.0888	0.7693
0.6101	37.18	5800	1.0890	0.7721
0.6042	37.82	5900	1.0779	0.7750
0.5618	38.46	6000	1.0769	0.7750
0.5878	39.1	6100	1.0638	0.7787
0.5522	39.74	6200	1.0611	0.7731
0.557	40.38	6300	1.0639	0.7768
0.5665	41.03	6400	1.0668	0.7740
0.5269	41.67	6500	1.0531	0.7759
0.5672	42.31	6600	1.0493	0.7759
0.5197	42.95	6700	1.0469	0.7759
0.5273	43.59	6800	1.0481	0.7740
0.5149	44.23	6900	1.0434	0.7712
0.5146	44.87	7000	1.0462	0.7787
0.5033	45.51	7100	1.0358	0.7759
0.5073	46.15	7200	1.0322	0.7806
0.4964	46.79	7300	1.0313	0.7815
0.4832	47.44	7400	1.0238	0.7797
0.484	48.08	7500	1.0355	0.7768
0.4856	48.72	7600	1.0263	0.7834
0.4688	49.36	7700	1.0178	0.7815
0.4628	50.0	7800	1.0161	0.7787
0.457	50.64	7900	1.0195	0.7768
0.4547	51.28	8000	1.0064	0.7825
0.4551	51.92	8100	1.0108	0.7806
0.4408	52.56	8200	1.0136	0.7768
0.4471	53.21	8300	1.0016	0.7834
0.4431	53.85	8400	1.0038	0.7863
0.4393	54.49	8500	1.0057	0.7815
0.4246	55.13	8600	0.9961	0.7797
0.4237	55.77	8700	1.0019	0.7806
0.4128	56.41	8800	0.9941	0.7806
0.4285	57.05	8900	0.9946	0.7815
0.4121	57.69	9000	0.9932	0.7806
0.4167	58.33	9100	0.9916	0.7825
0.4001	58.97	9200	0.9915	0.7825
0.4053	59.62	9300	0.9886	0.7815
0.3993	60.26	9400	0.9910	0.7844
0.3881	60.9	9500	0.9856	0.7863
0.3846	61.54	9600	0.9917	0.7806
0.3913	62.18	9700	0.9820	0.7834
0.3897	62.82	9800	0.9806	0.7844
0.3821	63.46	9900	0.9804	0.7825
0.3742	64.1	10000	0.9873	0.7844
0.3835	64.74	10100	0.9807	0.7834
0.3571	65.38	10200	0.9792	0.7844
0.38	66.03	10300	0.9786	0.7844
0.3612	66.67	10400	0.9769	0.7844
0.3628	67.31	10500	0.9991	0.7740
0.3655	67.95	10600	0.9737	0.7806
0.3489	68.59	10700	0.9745	0.7853
0.371	69.23	10800	0.9853	0.7787
0.3454	69.87	10900	0.9676	0.7825
0.3457	70.51	11000	0.9708	0.7853
0.3559	71.15	11100	0.9691	0.7863
0.3523	71.79	11200	0.9690	0.7872
0.3357	72.44	11300	0.9707	0.7815
0.344	73.08	11400	0.9690	0.7863
0.3527	73.72	11500	0.9788	0.7825
0.327	74.36	11600	0.9703	0.7825
0.3376	75.0	11700	0.9770	0.7787
0.3518	75.64	11800	0.9718	0.7834
0.3031	76.28	11900	0.9736	0.7863
0.3404	76.92	12000	0.9661	0.7825
0.3243	77.56	12100	0.9731	0.7853
0.3381	78.21	12200	0.9685	0.7900
0.3258	78.85	12300	0.9691	0.7844
0.3149	79.49	12400	0.9615	0.7844
0.3234	80.13	12500	0.9661	0.7853
0.3296	80.77	12600	0.9722	0.7815
0.3215	81.41	12700	0.9672	0.7834
0.3121	82.05	12800	0.9641	0.7834
0.3163	82.69	12900	0.9636	0.7834
0.3225	83.33	13000	0.9649	0.7853
0.3136	83.97	13100	0.9652	0.7825
0.3172	84.62	13200	0.9639	0.7853
0.3098	85.26	13300	0.9671	0.7834
0.3081	85.9	13400	0.9627	0.7806
0.3099	86.54	13500	0.9626	0.7815
0.3144	87.18	13600	0.9612	0.7815
0.2952	87.82	13700	0.9645	0.7863
0.3092	88.46	13800	0.9604	0.7853
0.3193	89.1	13900	0.9630	0.7844
0.3005	89.74	14000	0.9667	0.7815
0.2928	90.38	14100	0.9638	0.7844
0.315	91.03	14200	0.9644	0.7844
0.3095	91.67	14300	0.9637	0.7834
0.3036	92.31	14400	0.9615	0.7834
0.298	92.95	14500	0.9617	0.7844
0.2944	93.59	14600	0.9658	0.7834
0.3065	94.23	14700	0.9625	0.7834
0.2983	94.87	14800	0.9622	0.7844
0.2953	95.51	14900	0.9626	0.7834
0.3063	96.15	15000	0.9608	0.7853
0.3058	96.79	15100	0.9631	0.7853
0.2974	97.44	15200	0.9614	0.7844
0.3004	98.08	15300	0.9608	0.7844
0.3001	98.72	15400	0.9613	0.7853
0.2968	99.36	15500	0.9623	0.7853
0.2985	100.0	15600	0.9618	0.7844

Framework versions

• Transformers 4.34.1
• Pytorch 2.0.1+cu117
• Datasets 2.14.6
• Tokenizers 0.14.1