Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	250	5.5798	0.1893	0.8757	3.8790	0.1893	0.1519	0.0656	0.6360
5.6792	2.0	500	5.5256	0.2642	0.8415	4.3054	0.2642	0.2235	0.0705	0.5478
5.6792	3.0	750	5.3875	0.3157	0.8213	3.4839	0.3157	0.2920	0.0835	0.5208
4.8939	4.0	1000	4.4870	0.473	0.6754	2.6509	0.473	0.4671	0.0728	0.3199
4.8939	5.0	1250	4.4320	0.5015	0.6345	2.7717	0.5015	0.4779	0.0577	0.2773
4.3006	6.0	1500	4.2399	0.5435	0.5938	2.6489	0.5435	0.5334	0.0527	0.2353
4.3006	7.0	1750	3.8189	0.6155	0.5111	2.4587	0.6155	0.6096	0.0725	0.1646
3.6984	8.0	2000	3.5238	0.6795	0.4476	2.2085	0.6795	0.6810	0.0712	0.1258
3.6984	9.0	2250	3.4654	0.6877	0.4287	2.2234	0.6877	0.6852	0.0511	0.1198
3.32	10.0	2500	3.4769	0.692	0.4253	2.2586	0.692	0.6868	0.0456	0.1164
3.32	11.0	2750	3.3473	0.7235	0.3967	2.1360	0.7235	0.7245	0.0518	0.1063
3.0488	12.0	3000	3.3891	0.712	0.3929	2.1461	0.712	0.7100	0.0491	0.1043
3.0488	13.0	3250	3.3123	0.7208	0.3846	2.1236	0.7208	0.7171	0.0444	0.0958
2.8727	14.0	3500	3.3357	0.7147	0.3877	2.0928	0.7147	0.7170	0.0489	0.1000
2.8727	15.0	3750	3.2576	0.7318	0.3703	2.1052	0.7318	0.7358	0.0473	0.0901
2.7552	16.0	4000	3.2528	0.738	0.3650	2.0968	0.738	0.7396	0.0477	0.0877
2.7552	17.0	4250	3.4241	0.7093	0.4120	2.1652	0.7093	0.7078	0.0540	0.1114
2.6718	18.0	4500	3.2877	0.7358	0.3686	2.0587	0.7358	0.7362	0.0517	0.0899
2.6718	19.0	4750	3.2690	0.7375	0.3650	2.0509	0.7375	0.7409	0.0527	0.0891
2.6158	20.0	5000	3.2650	0.7398	0.3629	2.0803	0.7398	0.7432	0.0491	0.0889
2.6158	21.0	5250	3.2507	0.7452	0.3599	2.0773	0.7452	0.7467	0.0577	0.0859
2.5766	22.0	5500	3.1737	0.7525	0.3481	2.0408	0.7525	0.7539	0.0557	0.0815
2.5766	23.0	5750	3.1625	0.7585	0.3478	2.0313	0.7585	0.7599	0.0587	0.0813
2.5388	24.0	6000	3.2357	0.746	0.3563	2.0384	0.746	0.7445	0.0592	0.0838
2.5388	25.0	6250	3.1653	0.761	0.3441	2.0389	0.761	0.7605	0.0423	0.0783
2.5129	26.0	6500	3.1662	0.7532	0.3468	2.0033	0.7532	0.7556	0.0594	0.0805
2.5129	27.0	6750	3.1224	0.7632	0.3384	1.9745	0.7632	0.7624	0.0537	0.0773
2.4881	28.0	7000	3.2460	0.7458	0.3618	2.0745	0.7458	0.7479	0.0521	0.0868
2.4881	29.0	7250	3.1299	0.7605	0.3414	1.9781	0.7605	0.7626	0.0600	0.0774
2.469	30.0	7500	3.1695	0.7555	0.3481	2.0246	0.7555	0.7563	0.0534	0.0811
2.469	31.0	7750	3.1766	0.7612	0.3474	1.9997	0.7612	0.7633	0.0541	0.0803
2.4524	32.0	8000	3.0937	0.7638	0.3351	1.9420	0.7638	0.7649	0.0592	0.0754
2.4524	33.0	8250	3.1293	0.7625	0.3409	1.9671	0.7625	0.7633	0.0580	0.0781
2.4382	34.0	8500	3.1129	0.7668	0.3370	2.0003	0.7668	0.7672	0.0623	0.0746
2.4382	35.0	8750	3.0795	0.767	0.3324	1.9772	0.767	0.7677	0.0536	0.0759
2.4259	36.0	9000	3.0927	0.7675	0.3332	1.9557	0.7675	0.7690	0.0588	0.0744
2.4259	37.0	9250	3.0856	0.7702	0.3327	1.9465	0.7702	0.7715	0.0554	0.0756
2.4107	38.0	9500	3.0915	0.7678	0.3319	1.9699	0.7678	0.7681	0.0556	0.0749
2.4107	39.0	9750	3.0885	0.763	0.3338	1.9478	0.763	0.7643	0.0575	0.0750
2.4002	40.0	10000	3.0921	0.771	0.3315	1.9563	0.771	0.7729	0.0557	0.0744
2.4002	41.0	10250	3.0727	0.767	0.3310	1.9530	0.767	0.7682	0.0567	0.0748
2.3905	42.0	10500	3.0793	0.7645	0.3320	1.9484	0.7645	0.7657	0.0598	0.0755
2.3905	43.0	10750	3.0771	0.7672	0.3308	1.9548	0.7672	0.7679	0.0566	0.0737
2.3798	44.0	11000	3.0794	0.7685	0.3309	1.9620	0.7685	0.7693	0.0631	0.0736
2.3798	45.0	11250	3.0736	0.7665	0.3320	1.9408	0.7665	0.7677	0.0589	0.0749
2.3731	46.0	11500	3.0746	0.7682	0.3312	1.9635	0.7682	0.7693	0.0576	0.0743
2.3731	47.0	11750	3.0711	0.768	0.3306	1.9576	0.768	0.7689	0.0572	0.0739
2.3671	48.0	12000	3.0785	0.7682	0.3317	1.9516	0.7682	0.7697	0.0574	0.0744
2.3671	49.0	12250	3.0678	0.7692	0.3298	1.9388	0.7692	0.7700	0.0606	0.0738
2.3628	50.0	12500	3.0688	0.768	0.3296	1.9490	0.768	0.7688	0.0595	0.0737

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_kd_NKD_t1.0_g1.5_rand

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5_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_kd_NKD_t1.0_g1.5_rand