Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
4.7829	0.0777	10	4.7747	0.2119	0.1874	0.3919	0.1982
4.7714	0.1553	20	4.7572	0.2038	0.1842	0.4262	0.1836
4.7606	0.2330	30	4.7367	0.3586	0.3433	0.6517	0.3307
4.747	0.3107	40	4.7149	0.4303	0.4272	0.7734	0.4039
4.7253	0.3883	50	4.6846	0.4361	0.4678	0.7906	0.4160
4.7069	0.4660	60	4.6534	0.5330	0.5397	0.8048	0.5093
4.6857	0.5437	70	4.6177	0.5500	0.5511	0.7998	0.5264
4.6569	0.6214	80	4.5764	0.5739	0.5800	0.8208	0.5517
4.6293	0.6990	90	4.5359	0.6142	0.6149	0.8075	0.5926
4.5953	0.7767	100	4.4828	0.6207	0.6233	0.8109	0.6000
4.5651	0.8544	110	4.4257	0.6591	0.6585	0.8148	0.6393
4.5296	0.9320	120	4.3647	0.7063	0.7012	0.8284	0.6882
4.4911	1.0097	130	4.2998	0.7089	0.7074	0.8326	0.6924
4.4442	1.0874	140	4.2288	0.6939	0.6890	0.8302	0.6759
4.3912	1.1650	150	4.1527	0.6873	0.6863	0.8262	0.6703
4.3393	1.2427	160	4.0884	0.7250	0.7127	0.8251	0.7082
4.3019	1.3204	170	3.9946	0.7262	0.7152	0.8234	0.7098
4.2366	1.3981	180	3.9314	0.7301	0.7177	0.8230	0.7143
4.1966	1.4757	190	3.8398	0.7325	0.7196	0.8169	0.7175
4.1402	1.5534	200	3.7587	0.7381	0.7217	0.8149	0.7221
4.0771	1.6311	210	3.6745	0.7310	0.7149	0.8125	0.7160
4.0436	1.7087	220	3.5729	0.7364	0.7189	0.8121	0.7214
3.9697	1.7864	230	3.5030	0.7490	0.7339	0.8172	0.7358
3.9181	1.8641	240	3.4505	0.7541	0.7379	0.8123	0.7408
3.8573	1.9417	250	3.3529	0.7646	0.7453	0.8136	0.7521
3.8077	2.0194	260	3.2566	0.7660	0.7482	0.8093	0.7540
3.7449	2.0971	270	3.1869	0.7709	0.7510	0.8144	0.7588
3.682	2.1748	280	3.0898	0.7668	0.7440	0.8097	0.7548
3.6461	2.2524	290	3.0377	0.7641	0.7381	0.8100	0.7511
3.6004	2.3301	300	2.9001	0.7648	0.7384	0.8061	0.7522
3.5478	2.4078	310	2.8623	0.7653	0.7410	0.8060	0.7529
3.4971	2.4854	320	2.7961	0.7675	0.7447	0.8068	0.7558
3.4446	2.5631	330	2.6960	0.7690	0.7486	0.8128	0.7582
3.4093	2.6408	340	2.6480	0.7821	0.7652	0.8151	0.7718
3.3994	2.7184	350	2.5330	0.7847	0.7676	0.8156	0.7742
3.2963	2.7961	360	2.4866	0.7855	0.7681	0.8154	0.7752
3.2615	2.8738	370	2.4344	0.7891	0.7740	0.8172	0.7792
3.2024	2.9515	380	2.4011	0.7794	0.7638	0.8126	0.7694
3.1641	3.0291	390	2.3039	0.7835	0.7659	0.8100	0.7736
3.0719	3.1068	400	2.2471	0.7796	0.7608	0.8072	0.7691
3.0808	3.1845	410	2.2130	0.7896	0.7717	0.8137	0.7795
2.9916	3.2621	420	2.1387	0.7823	0.7652	0.8104	0.7718
2.9898	3.3398	430	2.0905	0.7981	0.7821	0.8250	0.7886
2.9597	3.4175	440	2.0260	0.7923	0.7769	0.8192	0.7826
2.9068	3.4951	450	1.9944	0.7976	0.7816	0.8233	0.7877
2.8423	3.5728	460	1.9643	0.7976	0.7805	0.8185	0.7876
2.8323	3.6505	470	1.8926	0.7935	0.7754	0.8136	0.7837
2.7814	3.7282	480	1.8676	0.8017	0.7856	0.8208	0.7917
2.7337	3.8058	490	1.8320	0.8052	0.7905	0.8246	0.7957
2.7215	3.8835	500	1.8003	0.7986	0.7834	0.8208	0.7890
2.6456	3.9612	510	1.7754	0.8005	0.7848	0.8230	0.7914
2.6494	4.0388	520	1.7083	0.8054	0.7895	0.8252	0.7967
2.5878	4.1165	530	1.6836	0.8054	0.7878	0.8239	0.7967
2.592	4.1942	540	1.6770	0.8005	0.7826	0.8220	0.7912
2.5698	4.2718	550	1.6184	0.8056	0.7881	0.8268	0.7970
2.52	4.3495	560	1.6368	0.8064	0.7898	0.8267	0.7975
2.5317	4.4272	570	1.5952	0.8059	0.7891	0.8289	0.7972
2.4199	4.5049	580	1.5518	0.8163	0.8002	0.8337	0.8082
2.4357	4.5825	590	1.5375	0.8095	0.7933	0.8263	0.8012
2.4217	4.6602	600	1.4994	0.8127	0.7964	0.8297	0.8042
2.428	4.7379	610	1.4671	0.8156	0.8003	0.8309	0.8074
2.3725	4.8155	620	1.4402	0.8141	0.7973	0.8295	0.8054
2.3594	4.8932	630	1.4566	0.8134	0.7976	0.8287	0.8049
2.3279	4.9709	640	1.4359	0.8183	0.8034	0.8314	0.8100
2.3166	5.0485	650	1.4067	0.8226	0.8086	0.8343	0.8149
2.3062	5.1262	660	1.3913	0.8212	0.8072	0.8340	0.8131
2.3096	5.2039	670	1.3577	0.8241	0.8107	0.8373	0.8159
2.2514	5.2816	680	1.3574	0.8270	0.8136	0.8371	0.8193
2.2053	5.3592	690	1.3450	0.8239	0.8101	0.8370	0.8164
2.2347	5.4369	700	1.3331	0.8270	0.8137	0.8388	0.8194
2.215	5.5146	710	1.2902	0.8294	0.8154	0.8419	0.8219
2.175	5.5922	720	1.2861	0.8256	0.8114	0.8388	0.8181
2.2212	5.6699	730	1.2637	0.8321	0.8180	0.8440	0.8241
2.1459	5.7476	740	1.2827	0.8302	0.8166	0.8396	0.8227
2.1615	5.8252	750	1.2800	0.8311	0.8184	0.8496	0.8239
2.0966	5.9029	760	1.2742	0.8326	0.8195	0.8418	0.8251
2.1314	5.9806	770	1.2464	0.8316	0.8184	0.8407	0.8238
2.0846	6.0583	780	1.2409	0.8326	0.8189	0.8414	0.8250
2.0522	6.1359	790	1.2023	0.8365	0.8233	0.8455	0.8292
2.0724	6.2136	800	1.2252	0.8309	0.8174	0.8396	0.8235
2.0848	6.2913	810	1.2025	0.8321	0.8186	0.8424	0.8248
2.0402	6.3689	820	1.2130	0.8333	0.8189	0.8428	0.8255
2.0778	6.4466	830	1.1809	0.8375	0.8249	0.8532	0.8302
2.0963	6.5243	840	1.1696	0.8365	0.8231	0.8527	0.8289
2.0576	6.6019	850	1.1866	0.8321	0.8181	0.8411	0.8245
2.0386	6.6796	860	1.1882	0.8302	0.8160	0.8389	0.8227
2.0084	6.7573	870	1.1696	0.8372	0.8244	0.8446	0.8301
2.0571	6.8350	880	1.1622	0.8353	0.8217	0.8437	0.8280
2.0264	6.9126	890	1.1640	0.8336	0.8204	0.8429	0.8263
2.0077	6.9903	900	1.1673	0.8367	0.8241	0.8447	0.8295
2.0492	7.0680	910	1.1455	0.8404	0.8269	0.8462	0.8330
1.9973	7.1456	920	1.1538	0.8379	0.8250	0.8455	0.8307
1.9961	7.2233	930	1.1502	0.8367	0.8236	0.8415	0.8295
1.9681	7.3010	940	1.1657	0.8384	0.8254	0.8463	0.8311
2.0188	7.3786	950	1.1309	0.8379	0.8252	0.8445	0.8310
2.0225	7.4563	960	1.1547	0.8367	0.8231	0.8446	0.8294
1.9562	7.5340	970	1.1474	0.8377	0.8243	0.8457	0.8305
2.0247	7.6117	980	1.1251	0.8365	0.8241	0.8449	0.8294
1.9355	7.6893	990	1.1349	0.8397	0.8276	0.8532	0.8329
1.9804	7.7670	1000	1.1545	0.8387	0.8260	0.8457	0.8315

Framework versions

Transformers 4.40.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1

amaye15/microsoft-resnet-50-batch32-lr0.0005-standford-dogs

microsoft-resnet-50-batch32-lr0.0005-standford-dogs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

amaye15/
microsoft-resnet-50-batch32-lr0.0005-standford-dogs