resnet101-base_tobacco

This model is a fine-tuned version of microsoft/resnet-101 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6332
Accuracy: 0.435
Brier Loss: 0.6886
Nll: 4.4967
F1 Micro: 0.435
F1 Macro: 0.2876
Ece: 0.2482
Aurc: 0.3432

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-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	13	2.3065	0.06	0.9008	7.5257	0.06	0.0563	0.1444	0.9505
No log	2.0	26	2.3098	0.075	0.9014	8.6176	0.075	0.0468	0.1535	0.9485
No log	3.0	39	2.3082	0.09	0.9011	7.8490	0.09	0.0647	0.1662	0.9336
No log	4.0	52	2.3056	0.12	0.9006	7.6932	0.12	0.0809	0.1814	0.8887
No log	5.0	65	2.3004	0.125	0.8995	7.1356	0.125	0.0750	0.1841	0.8198
No log	6.0	78	2.2921	0.155	0.8979	5.9637	0.155	0.0706	0.2036	0.7930
No log	7.0	91	2.2917	0.165	0.8978	5.7926	0.165	0.0785	0.2139	0.8056
No log	8.0	104	2.2842	0.185	0.8963	4.7947	0.185	0.0595	0.2244	0.8344
No log	9.0	117	2.2742	0.215	0.8942	4.4573	0.2150	0.0830	0.2424	0.7961
No log	10.0	130	2.2638	0.2	0.8921	4.8564	0.2000	0.0554	0.2376	0.7663
No log	11.0	143	2.2530	0.215	0.8898	5.0772	0.2150	0.0740	0.2467	0.7908
No log	12.0	156	2.2479	0.19	0.8888	5.3276	0.19	0.0421	0.2220	0.7856
No log	13.0	169	2.2406	0.18	0.8873	5.2973	0.18	0.0308	0.2248	0.8007
No log	14.0	182	2.2202	0.285	0.8826	5.4657	0.285	0.1167	0.2855	0.6743
No log	15.0	195	2.2085	0.29	0.8801	5.7797	0.29	0.1154	0.2909	0.6660
No log	16.0	208	2.1850	0.305	0.8742	5.7600	0.305	0.1194	0.3063	0.4897
No log	17.0	221	2.2017	0.18	0.8789	5.7405	0.18	0.0306	0.2309	0.7654
No log	18.0	234	2.1998	0.18	0.8784	5.8985	0.18	0.0305	0.2377	0.7525
No log	19.0	247	2.1429	0.285	0.8640	5.9614	0.285	0.1117	0.2970	0.5007
No log	20.0	260	2.1240	0.315	0.8587	5.9916	0.315	0.1232	0.3057	0.4288
No log	21.0	273	2.0986	0.305	0.8513	5.9764	0.305	0.1166	0.3001	0.4526
No log	22.0	286	2.0909	0.315	0.8494	5.9914	0.315	0.1234	0.3062	0.4385
No log	23.0	299	2.0451	0.295	0.8313	6.1078	0.295	0.1115	0.2901	0.4619
No log	24.0	312	2.0662	0.3	0.8413	6.1029	0.3	0.1168	0.3014	0.4544
No log	25.0	325	2.0235	0.3	0.8238	6.1798	0.3	0.1156	0.2885	0.4553
No log	26.0	338	2.0669	0.305	0.8439	6.2056	0.305	0.1207	0.3046	0.4579
No log	27.0	351	2.0223	0.315	0.8256	6.1083	0.315	0.1232	0.2860	0.4308
No log	28.0	364	2.1075	0.185	0.8574	6.0867	0.185	0.0370	0.2317	0.7416
No log	29.0	377	1.9127	0.295	0.7709	6.1567	0.295	0.1155	0.2464	0.4630
No log	30.0	390	1.9407	0.315	0.7889	6.1398	0.315	0.1283	0.2696	0.4244
No log	31.0	403	1.9099	0.305	0.7737	6.1311	0.305	0.1216	0.2626	0.4441
No log	32.0	416	1.9071	0.31	0.7731	6.1004	0.31	0.1237	0.2803	0.4387
No log	33.0	429	1.9097	0.31	0.7774	6.1658	0.31	0.1212	0.2701	0.4328
No log	34.0	442	1.9008	0.3	0.7724	6.2049	0.3	0.1180	0.2415	0.4452
No log	35.0	455	2.0340	0.275	0.8382	5.8659	0.275	0.1095	0.2873	0.6352
No log	36.0	468	1.9324	0.315	0.7937	6.0328	0.315	0.1248	0.2865	0.4177
No log	37.0	481	2.0698	0.18	0.8483	6.1172	0.18	0.0306	0.2448	0.7024
No log	38.0	494	1.8436	0.3	0.7492	6.1508	0.3	0.1192	0.2461	0.4406
2.0752	39.0	507	1.8504	0.31	0.7556	6.0528	0.31	0.1222	0.2696	0.4355
2.0752	40.0	520	1.8523	0.315	0.7582	6.0492	0.315	0.1245	0.2522	0.4341
2.0752	41.0	533	1.8858	0.305	0.7785	6.1136	0.305	0.1244	0.2756	0.4559
2.0752	42.0	546	1.8466	0.305	0.7594	5.9124	0.305	0.1205	0.2739	0.4469
2.0752	43.0	559	1.9921	0.195	0.8300	5.6106	0.195	0.0490	0.2368	0.7141
2.0752	44.0	572	1.8133	0.31	0.7447	5.6505	0.31	0.1242	0.2708	0.4189
2.0752	45.0	585	1.8022	0.32	0.7397	5.6263	0.32	0.1324	0.2557	0.4213
2.0752	46.0	598	1.8361	0.32	0.7599	5.6068	0.32	0.1281	0.2719	0.4239
2.0752	47.0	611	1.7972	0.32	0.7376	5.8954	0.32	0.1306	0.2418	0.4311
2.0752	48.0	624	1.7850	0.325	0.7357	5.8208	0.325	0.1397	0.2528	0.3984
2.0752	49.0	637	1.7808	0.315	0.7332	5.5883	0.315	0.1325	0.2551	0.4255
2.0752	50.0	650	1.7838	0.31	0.7338	5.6850	0.31	0.1314	0.2530	0.4247
2.0752	51.0	663	1.7767	0.305	0.7316	5.4974	0.305	0.1241	0.2515	0.4253
2.0752	52.0	676	1.7607	0.32	0.7263	5.3077	0.32	0.1321	0.2458	0.4148
2.0752	53.0	689	1.7486	0.32	0.7224	5.1734	0.32	0.1355	0.2510	0.4190
2.0752	54.0	702	1.7693	0.33	0.7323	5.1578	0.33	0.1446	0.2638	0.3970
2.0752	55.0	715	1.7476	0.325	0.7235	5.1481	0.325	0.1602	0.2285	0.4140
2.0752	56.0	728	1.7384	0.31	0.7189	5.3248	0.31	0.1507	0.2295	0.4202
2.0752	57.0	741	1.7454	0.32	0.7228	5.2669	0.32	0.1575	0.2602	0.4218
2.0752	58.0	754	1.8063	0.33	0.7551	5.0652	0.33	0.1574	0.2835	0.4092
2.0752	59.0	767	1.7466	0.34	0.7237	4.9430	0.34	0.1783	0.2729	0.4124
2.0752	60.0	780	1.7240	0.345	0.7166	5.0165	0.345	0.1776	0.2397	0.4118
2.0752	61.0	793	1.7105	0.325	0.7126	5.0261	0.325	0.1647	0.2564	0.4149
2.0752	62.0	806	1.7078	0.345	0.7157	5.0160	0.345	0.1797	0.2612	0.4013
2.0752	63.0	819	1.7982	0.305	0.7575	4.9876	0.305	0.1614	0.2733	0.4650
2.0752	64.0	832	1.8072	0.33	0.7635	5.0080	0.33	0.1954	0.2928	0.4487
2.0752	65.0	845	1.7201	0.35	0.7180	4.8708	0.35	0.2071	0.2445	0.4114
2.0752	66.0	858	1.7131	0.335	0.7167	4.9248	0.335	0.1936	0.2531	0.4223
2.0752	67.0	871	1.7071	0.345	0.7138	4.8657	0.345	0.1948	0.2664	0.4128
2.0752	68.0	884	1.7022	0.36	0.7128	4.7996	0.36	0.2147	0.2443	0.4023
2.0752	69.0	897	1.6859	0.37	0.7055	4.7318	0.37	0.2296	0.2577	0.3909
2.0752	70.0	910	1.6860	0.37	0.7038	4.8293	0.37	0.2314	0.2594	0.3894
2.0752	71.0	923	1.6823	0.36	0.7038	4.7070	0.36	0.2170	0.2485	0.3934
2.0752	72.0	936	1.7656	0.335	0.7457	4.8009	0.335	0.2035	0.2760	0.4503
2.0752	73.0	949	1.8235	0.32	0.7754	4.7280	0.32	0.2028	0.2752	0.5244
2.0752	74.0	962	1.6878	0.37	0.7073	4.7660	0.37	0.2290	0.2455	0.3996
2.0752	75.0	975	1.6717	0.365	0.7003	4.7709	0.3650	0.2209	0.2404	0.3906
2.0752	76.0	988	1.6610	0.365	0.6972	4.6921	0.3650	0.2223	0.2640	0.3910
1.6288	77.0	1001	1.6740	0.4	0.7016	4.6791	0.4000	0.2519	0.2794	0.3693
1.6288	78.0	1014	1.6792	0.385	0.7048	4.7411	0.3850	0.2434	0.2594	0.3913
1.6288	79.0	1027	1.6752	0.395	0.7030	4.5595	0.395	0.2608	0.2906	0.3887
1.6288	80.0	1040	1.6554	0.395	0.6951	4.5213	0.395	0.2653	0.2696	0.3821
1.6288	81.0	1053	1.6688	0.385	0.7013	4.5993	0.3850	0.2441	0.2614	0.3886
1.6288	82.0	1066	1.6892	0.35	0.7121	4.6296	0.35	0.2187	0.2701	0.4067
1.6288	83.0	1079	1.6691	0.4	0.7031	4.5448	0.4000	0.2570	0.2845	0.3756
1.6288	84.0	1092	1.6544	0.39	0.6946	4.6295	0.39	0.2357	0.2522	0.3806
1.6288	85.0	1105	1.6592	0.395	0.6983	4.4632	0.395	0.2515	0.2793	0.3815
1.6288	86.0	1118	1.6526	0.4	0.6945	4.5685	0.4000	0.2579	0.2527	0.3781
1.6288	87.0	1131	1.6558	0.4	0.6968	4.5767	0.4000	0.2623	0.2435	0.3804
1.6288	88.0	1144	1.6507	0.395	0.6961	4.5355	0.395	0.2390	0.2554	0.3710
1.6288	89.0	1157	1.6462	0.4	0.6941	4.5278	0.4000	0.2525	0.2406	0.3704
1.6288	90.0	1170	1.6490	0.39	0.6954	4.5513	0.39	0.2430	0.2497	0.3700
1.6288	91.0	1183	1.6568	0.405	0.6980	4.5792	0.405	0.2545	0.2584	0.3675
1.6288	92.0	1196	1.6421	0.41	0.6909	4.5731	0.41	0.2666	0.2527	0.3609
1.6288	93.0	1209	1.6489	0.405	0.6952	4.3408	0.405	0.2695	0.2738	0.3716
1.6288	94.0	1222	1.6440	0.41	0.6933	4.3845	0.41	0.2713	0.2629	0.3619
1.6288	95.0	1235	1.6411	0.435	0.6919	4.4244	0.435	0.2878	0.2634	0.3516
1.6288	96.0	1248	1.6391	0.41	0.6918	4.4251	0.41	0.2628	0.2655	0.3743
1.6288	97.0	1261	1.6341	0.42	0.6893	4.4415	0.4200	0.2761	0.2549	0.3598
1.6288	98.0	1274	1.6476	0.415	0.6952	4.5149	0.415	0.2778	0.2385	0.3639
1.6288	99.0	1287	1.6463	0.42	0.6939	4.5027	0.4200	0.2792	0.2806	0.3593
1.6288	100.0	1300	1.6332	0.435	0.6886	4.4967	0.435	0.2876	0.2482	0.3432

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3

bdpc/resnet101-base_tobacco

resnet101-base_tobacco

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种