Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	250	0.7862	0.115	0.9306	3.3236	0.115	0.0472	0.0925	0.7696
0.872	2.0	500	0.7072	0.187	0.9137	4.4054	0.187	0.1401	0.0987	0.7052
0.872	3.0	750	0.6647	0.2918	0.8192	7.0505	0.2918	0.2588	0.0612	0.5348
0.6505	4.0	1000	0.6113	0.4565	0.6865	5.3222	0.4565	0.4485	0.0605	0.3382
0.6505	5.0	1250	0.5561	0.5797	0.5560	4.2347	0.5797	0.5876	0.0597	0.2014
0.5417	6.0	1500	0.4978	0.657	0.4720	3.3410	0.657	0.6557	0.0649	0.1412
0.5417	7.0	1750	0.4849	0.6703	0.4677	3.2428	0.6703	0.6748	0.0876	0.1380
0.4664	8.0	2000	0.4561	0.709	0.4076	2.7423	0.709	0.7105	0.0585	0.1065
0.4664	9.0	2250	0.4467	0.7225	0.3868	2.6295	0.7225	0.7199	0.0566	0.0974
0.4241	10.0	2500	0.4380	0.7242	0.3822	2.6630	0.7242	0.7254	0.0618	0.0928
0.4241	11.0	2750	0.4317	0.7478	0.3579	2.4077	0.7478	0.7491	0.0541	0.0833
0.3956	12.0	3000	0.4238	0.7512	0.3543	2.4087	0.7513	0.7513	0.0589	0.0813
0.3956	13.0	3250	0.4403	0.734	0.3663	2.5318	0.734	0.7327	0.0609	0.0873
0.3718	14.0	3500	0.4285	0.7462	0.3559	2.3344	0.7462	0.7457	0.0505	0.0808
0.3718	15.0	3750	0.4228	0.7518	0.3460	2.4046	0.7518	0.7519	0.0466	0.0772
0.3526	16.0	4000	0.4276	0.7565	0.3472	2.3786	0.7565	0.7568	0.0446	0.0792
0.3526	17.0	4250	0.4158	0.7588	0.3393	2.2729	0.7588	0.7573	0.0549	0.0743
0.3366	18.0	4500	0.4108	0.7702	0.3278	2.1763	0.7702	0.7710	0.0503	0.0694
0.3366	19.0	4750	0.4132	0.762	0.3333	2.2475	0.762	0.7625	0.0435	0.0720
0.3236	20.0	5000	0.4083	0.7608	0.3318	2.1875	0.7608	0.7625	0.0391	0.0718
0.3236	21.0	5250	0.4291	0.7525	0.3419	2.3114	0.7525	0.7530	0.0481	0.0762
0.3124	22.0	5500	0.4160	0.7648	0.3315	2.2708	0.7648	0.7649	0.0455	0.0714
0.3124	23.0	5750	0.4144	0.759	0.3337	2.2574	0.7590	0.7593	0.0408	0.0747
0.3029	24.0	6000	0.4066	0.7665	0.3267	2.1274	0.7665	0.7671	0.0424	0.0706
0.3029	25.0	6250	0.4159	0.7612	0.3359	2.3038	0.7612	0.7620	0.0450	0.0733
0.295	26.0	6500	0.4049	0.7722	0.3241	2.1874	0.7722	0.7739	0.0425	0.0691
0.295	27.0	6750	0.4047	0.7735	0.3236	2.1528	0.7735	0.7733	0.0474	0.0693
0.2873	28.0	7000	0.4052	0.773	0.3239	2.1392	0.7730	0.7740	0.0439	0.0689
0.2873	29.0	7250	0.4126	0.7675	0.3301	2.2627	0.7675	0.7661	0.0355	0.0716
0.2811	30.0	7500	0.4064	0.7708	0.3265	2.1093	0.7707	0.7734	0.0433	0.0708
0.2811	31.0	7750	0.4030	0.7742	0.3214	2.1570	0.7742	0.7748	0.0426	0.0683
0.2747	32.0	8000	0.4041	0.7762	0.3172	2.0937	0.7762	0.7766	0.0430	0.0661
0.2747	33.0	8250	0.4031	0.7795	0.3176	2.1283	0.7795	0.7788	0.0433	0.0661
0.2698	34.0	8500	0.4024	0.7758	0.3209	2.1024	0.7758	0.7762	0.0442	0.0679
0.2698	35.0	8750	0.4024	0.7725	0.3205	2.1210	0.7725	0.7736	0.0424	0.0673
0.2652	36.0	9000	0.4064	0.769	0.3251	2.1974	0.769	0.7680	0.0479	0.0698
0.2652	37.0	9250	0.4016	0.779	0.3191	2.0764	0.779	0.7796	0.0431	0.0678
0.2612	38.0	9500	0.4028	0.7735	0.3185	2.1391	0.7735	0.7733	0.0411	0.0668
0.2612	39.0	9750	0.4031	0.7782	0.3164	2.0988	0.7782	0.7787	0.0349	0.0657
0.2572	40.0	10000	0.4039	0.778	0.3178	2.1040	0.778	0.7782	0.0399	0.0664
0.2572	41.0	10250	0.4025	0.7745	0.3184	2.1274	0.7745	0.7747	0.0389	0.0668
0.2546	42.0	10500	0.4040	0.7752	0.3184	2.1421	0.7752	0.7754	0.0386	0.0660
0.2546	43.0	10750	0.4022	0.7775	0.3171	2.0920	0.7775	0.7778	0.0450	0.0663
0.2516	44.0	11000	0.4023	0.776	0.3164	2.1088	0.776	0.7771	0.0418	0.0658
0.2516	45.0	11250	0.4037	0.773	0.3187	2.0976	0.7730	0.7738	0.0448	0.0669
0.2493	46.0	11500	0.4027	0.7758	0.3174	2.1363	0.7758	0.7761	0.0420	0.0660
0.2493	47.0	11750	0.4028	0.7742	0.3186	2.1218	0.7742	0.7744	0.0408	0.0666
0.2473	48.0	12000	0.4028	0.7762	0.3162	2.1082	0.7762	0.7764	0.0472	0.0658
0.2473	49.0	12250	0.4028	0.7755	0.3172	2.0818	0.7755	0.7759	0.0437	0.0662
0.2462	50.0	12500	0.4025	0.7765	0.3168	2.1020	0.7765	0.7763	0.0442	0.0661

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2

jordyvl/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd_rand

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd_rand

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jordyvl/
resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd_rand