convnextv2-tiny-1k-224-text

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the davanstrien/zenodo-presentations-open-labels dataset. It achieves the following results on the evaluation set:

Loss: 0.4554
Accuracy: 0.7874

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: 1337
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 200.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5242	1.0	23	0.4961	0.7559
0.459	2.0	46	0.5001	0.7638
0.4429	3.0	69	0.4554	0.7874
0.4308	4.0	92	0.4924	0.7638
0.4319	5.0	115	0.4673	0.7874
0.4047	6.0	138	0.4930	0.7756
0.425	7.0	161	0.4739	0.7795
0.4102	8.0	184	0.5118	0.7598
0.3959	9.0	207	0.5490	0.7480
0.365	10.0	230	0.5261	0.7638
0.4214	11.0	253	0.5089	0.7795
0.3798	12.0	276	0.4711	0.7992
0.3906	13.0	299	0.5035	0.7913
0.3706	14.0	322	0.4933	0.7953
0.3766	15.0	345	0.4973	0.7992
0.3213	16.0	368	0.5221	0.7874
0.329	17.0	391	0.5400	0.7835
0.3427	18.0	414	0.5252	0.7913
0.3472	19.0	437	0.6208	0.7441
0.3424	20.0	460	0.5320	0.7795
0.3016	21.0	483	0.5488	0.7795
0.3033	22.0	506	0.5889	0.7480
0.3083	23.0	529	0.6108	0.7638
0.2772	24.0	552	0.5845	0.7480
0.287	25.0	575	0.5242	0.8071
0.2651	26.0	598	0.6276	0.7598
0.2696	27.0	621	0.5649	0.7835
0.2701	28.0	644	0.6103	0.7756
0.2451	29.0	667	0.6207	0.7638
0.2705	30.0	690	0.5990	0.7756
0.2553	31.0	713	0.5962	0.7835
0.2559	32.0	736	0.6681	0.7717
0.2405	33.0	759	0.5917	0.7638
0.2707	34.0	782	0.5906	0.7638
0.3004	35.0	805	0.5905	0.7874
0.2404	36.0	828	0.5914	0.7677
0.242	37.0	851	0.7637	0.7638
0.2221	38.0	874	0.7117	0.7598
0.2196	39.0	897	0.6442	0.7835
0.23	40.0	920	0.7011	0.7717
0.2045	41.0	943	0.7822	0.7598
0.2043	42.0	966	0.7339	0.7520
0.2413	43.0	989	0.6917	0.7677
0.2135	44.0	1012	0.6954	0.7717
0.2194	45.0	1035	0.6729	0.7795
0.211	46.0	1058	0.6841	0.7835
0.2155	47.0	1081	0.7108	0.7677
0.2231	48.0	1104	0.6758	0.7677
0.2364	49.0	1127	0.7747	0.7520
0.222	50.0	1150	0.7104	0.7638
0.2018	51.0	1173	0.6885	0.7953
0.219	52.0	1196	0.7609	0.7520
0.1916	53.0	1219	0.8394	0.7677
0.1767	54.0	1242	0.7910	0.7717
0.236	55.0	1265	0.7601	0.7756
0.1898	56.0	1288	0.7501	0.7717
0.1876	57.0	1311	0.7492	0.7756
0.1592	58.0	1334	0.7905	0.7638
0.1772	59.0	1357	0.7411	0.7717
0.1787	60.0	1380	0.8145	0.7795
0.1782	61.0	1403	0.7721	0.7795
0.1781	62.0	1426	0.8022	0.7835
0.1884	63.0	1449	0.8630	0.7756
0.1905	64.0	1472	0.7472	0.7953
0.16	65.0	1495	0.7761	0.7874
0.1619	66.0	1518	0.8586	0.7795
0.1768	67.0	1541	0.7700	0.7835
0.1395	68.0	1564	0.8326	0.7717
0.1536	69.0	1587	0.8442	0.7756
0.208	70.0	1610	0.9289	0.7677
0.1783	71.0	1633	0.9022	0.7638
0.1572	72.0	1656	0.8510	0.7677
0.1349	73.0	1679	0.7962	0.7677
0.148	74.0	1702	0.8641	0.7756
0.1768	75.0	1725	0.9277	0.7677
0.1833	76.0	1748	0.8663	0.7638
0.1696	77.0	1771	0.8302	0.7756
0.1577	78.0	1794	0.8576	0.7638
0.1724	79.0	1817	0.8652	0.7598
0.1525	80.0	1840	0.8567	0.7717
0.158	81.0	1863	0.9139	0.7598
0.1639	82.0	1886	0.9689	0.7520
0.1424	83.0	1909	0.9698	0.7638
0.1224	84.0	1932	1.0239	0.7717
0.1765	85.0	1955	0.9072	0.7795
0.1726	86.0	1978	0.9436	0.7520
0.1584	87.0	2001	0.8775	0.7638
0.164	88.0	2024	0.8592	0.7717
0.1682	89.0	2047	0.9051	0.7638
0.1455	90.0	2070	1.0020	0.7717
0.1596	91.0	2093	0.9423	0.7677
0.1667	92.0	2116	0.9586	0.7638
0.132	93.0	2139	0.9890	0.7638
0.1335	94.0	2162	0.9922	0.7717
0.1538	95.0	2185	0.9534	0.7520
0.1288	96.0	2208	1.0714	0.7480
0.1661	97.0	2231	0.9950	0.7598
0.1392	98.0	2254	0.9866	0.7520
0.1413	99.0	2277	1.0638	0.7598
0.1619	100.0	2300	1.0178	0.7598
0.1537	101.0	2323	0.9892	0.7638
0.137	102.0	2346	0.9524	0.7559
0.1416	103.0	2369	1.0539	0.7402
0.1477	104.0	2392	1.0825	0.7283
0.1283	105.0	2415	1.0008	0.7520
0.1498	106.0	2438	0.9702	0.7638
0.1576	107.0	2461	1.0144	0.7677
0.1433	108.0	2484	0.9457	0.7638
0.1377	109.0	2507	0.9770	0.7677
0.1163	110.0	2530	1.1386	0.7559
0.1449	111.0	2553	1.0589	0.7559
0.1475	112.0	2576	1.0110	0.7480
0.1582	113.0	2599	0.9657	0.7677
0.1291	114.0	2622	0.9563	0.7756
0.1106	115.0	2645	1.1004	0.7480
0.1339	116.0	2668	1.0327	0.7520
0.1344	117.0	2691	1.0161	0.7520
0.1433	118.0	2714	1.0312	0.7559
0.1271	119.0	2737	1.0266	0.7598
0.1222	120.0	2760	1.0119	0.7638
0.1235	121.0	2783	1.0808	0.7520
0.1311	122.0	2806	1.0612	0.7520
0.1219	123.0	2829	1.1412	0.7520
0.148	124.0	2852	1.0836	0.7402
0.1076	125.0	2875	1.0629	0.7559
0.1306	126.0	2898	1.0791	0.7362
0.1153	127.0	2921	1.1495	0.7402
0.1239	128.0	2944	1.1446	0.7520
0.1533	129.0	2967	1.0818	0.7441
0.136	130.0	2990	1.0558	0.7520
0.1189	131.0	3013	1.0423	0.7520
0.1247	132.0	3036	1.0581	0.7638
0.1136	133.0	3059	1.0132	0.7717
0.1492	134.0	3082	1.1127	0.7441
0.1184	135.0	3105	1.1450	0.7402
0.1122	136.0	3128	1.1063	0.7520
0.1047	137.0	3151	1.1029	0.7441
0.1285	138.0	3174	1.1563	0.7402
0.1004	139.0	3197	1.1552	0.7362
0.1285	140.0	3220	1.1097	0.7480
0.1257	141.0	3243	1.1602	0.7402
0.1075	142.0	3266	1.1912	0.7559
0.1098	143.0	3289	1.1894	0.7520
0.1148	144.0	3312	1.1551	0.7441
0.1489	145.0	3335	1.1379	0.7441
0.1461	146.0	3358	1.1726	0.7480
0.1171	147.0	3381	1.1191	0.7441
0.1262	148.0	3404	1.1662	0.7441
0.1137	149.0	3427	1.1283	0.7480
0.1118	150.0	3450	1.1388	0.7480
0.1169	151.0	3473	1.1627	0.7520
0.1021	152.0	3496	1.1821	0.7323
0.1392	153.0	3519	1.1672	0.7323
0.1111	154.0	3542	1.2136	0.7402
0.1298	155.0	3565	1.1966	0.7402
0.1114	156.0	3588	1.1382	0.7362
0.09	157.0	3611	1.1460	0.7323
0.1294	158.0	3634	1.1612	0.7441
0.1186	159.0	3657	1.2204	0.7402
0.1096	160.0	3680	1.2096	0.7441
0.1107	161.0	3703	1.1822	0.7480
0.1094	162.0	3726	1.1908	0.7480
0.1112	163.0	3749	1.1647	0.7402
0.1042	164.0	3772	1.2523	0.7441
0.0993	165.0	3795	1.2040	0.7402
0.105	166.0	3818	1.2296	0.7402
0.1071	167.0	3841	1.2863	0.7480
0.108	168.0	3864	1.2372	0.7441
0.1076	169.0	3887	1.1872	0.7480
0.1107	170.0	3910	1.2354	0.7323
0.1012	171.0	3933	1.2105	0.7441
0.0918	172.0	3956	1.2026	0.7441
0.1043	173.0	3979	1.2925	0.7559
0.1035	174.0	4002	1.2314	0.7402
0.1101	175.0	4025	1.1943	0.7441
0.1084	176.0	4048	1.2069	0.7362
0.1247	177.0	4071	1.2303	0.7520
0.1278	178.0	4094	1.2118	0.7480
0.1117	179.0	4117	1.2213	0.7480
0.1123	180.0	4140	1.2403	0.7480
0.0918	181.0	4163	1.1987	0.7441
0.0827	182.0	4186	1.2358	0.7441
0.0814	183.0	4209	1.2608	0.7441
0.0897	184.0	4232	1.2370	0.7441
0.1321	185.0	4255	1.2317	0.7480
0.1194	186.0	4278	1.2289	0.7441
0.1154	187.0	4301	1.1964	0.7441
0.0964	188.0	4324	1.2009	0.7441
0.0903	189.0	4347	1.2123	0.7441
0.1174	190.0	4370	1.2335	0.7441
0.0846	191.0	4393	1.2399	0.7441
0.1073	192.0	4416	1.2432	0.7441
0.0892	193.0	4439	1.2604	0.7480
0.1158	194.0	4462	1.2473	0.7480
0.1153	195.0	4485	1.2267	0.7441
0.1208	196.0	4508	1.2178	0.7441
0.083	197.0	4531	1.2145	0.7480
0.1331	198.0	4554	1.2215	0.7441
0.0943	199.0	4577	1.2238	0.7441
0.0926	200.0	4600	1.2236	0.7441

Framework versions

Transformers 4.46.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05

train_batch_size: 64

eval_batch_size: 64

seed: 1337

optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments

lr_scheduler_type: linear

num_epochs: 200.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5242	1.0	23	0.4961	0.7559
0.459	2.0	46	0.5001	0.7638
0.4429	3.0	69	0.4554	0.7874
0.4308	4.0	92	0.4924	0.7638
0.4319	5.0	115	0.4673	0.7874
0.4047	6.0	138	0.4930	0.7756
0.425	7.0	161	0.4739	0.7795
0.4102	8.0	184	0.5118	0.7598
0.3959	9.0	207	0.5490	0.7480
0.365	10.0	230	0.5261	0.7638
0.4214	11.0	253	0.5089	0.7795
0.3798	12.0	276	0.4711	0.7992
0.3906	13.0	299	0.5035	0.7913
0.3706	14.0	322	0.4933	0.7953
0.3766	15.0	345	0.4973	0.7992
0.3213	16.0	368	0.5221	0.7874
0.329	17.0	391	0.5400	0.7835
0.3427	18.0	414	0.5252	0.7913
0.3472	19.0	437	0.6208	0.7441
0.3424	20.0	460	0.5320	0.7795
0.3016	21.0	483	0.5488	0.7795
0.3033	22.0	506	0.5889	0.7480
0.3083	23.0	529	0.6108	0.7638
0.2772	24.0	552	0.5845	0.7480
0.287	25.0	575	0.5242	0.8071
0.2651	26.0	598	0.6276	0.7598
0.2696	27.0	621	0.5649	0.7835
0.2701	28.0	644	0.6103	0.7756
0.2451	29.0	667	0.6207	0.7638
0.2705	30.0	690	0.5990	0.7756
0.2553	31.0	713	0.5962	0.7835
0.2559	32.0	736	0.6681	0.7717
0.2405	33.0	759	0.5917	0.7638
0.2707	34.0	782	0.5906	0.7638
0.3004	35.0	805	0.5905	0.7874
0.2404	36.0	828	0.5914	0.7677
0.242	37.0	851	0.7637	0.7638
0.2221	38.0	874	0.7117	0.7598
0.2196	39.0	897	0.6442	0.7835
0.23	40.0	920	0.7011	0.7717
0.2045	41.0	943	0.7822	0.7598
0.2043	42.0	966	0.7339	0.7520
0.2413	43.0	989	0.6917	0.7677
0.2135	44.0	1012	0.6954	0.7717
0.2194	45.0	1035	0.6729	0.7795
0.211	46.0	1058	0.6841	0.7835
0.2155	47.0	1081	0.7108	0.7677
0.2231	48.0	1104	0.6758	0.7677
0.2364	49.0	1127	0.7747	0.7520
0.222	50.0	1150	0.7104	0.7638
0.2018	51.0	1173	0.6885	0.7953
0.219	52.0	1196	0.7609	0.7520
0.1916	53.0	1219	0.8394	0.7677
0.1767	54.0	1242	0.7910	0.7717
0.236	55.0	1265	0.7601	0.7756
0.1898	56.0	1288	0.7501	0.7717
0.1876	57.0	1311	0.7492	0.7756
0.1592	58.0	1334	0.7905	0.7638
0.1772	59.0	1357	0.7411	0.7717
0.1787	60.0	1380	0.8145	0.7795
0.1782	61.0	1403	0.7721	0.7795
0.1781	62.0	1426	0.8022	0.7835
0.1884	63.0	1449	0.8630	0.7756
0.1905	64.0	1472	0.7472	0.7953
0.16	65.0	1495	0.7761	0.7874
0.1619	66.0	1518	0.8586	0.7795
0.1768	67.0	1541	0.7700	0.7835
0.1395	68.0	1564	0.8326	0.7717
0.1536	69.0	1587	0.8442	0.7756
0.208	70.0	1610	0.9289	0.7677
0.1783	71.0	1633	0.9022	0.7638
0.1572	72.0	1656	0.8510	0.7677
0.1349	73.0	1679	0.7962	0.7677
0.148	74.0	1702	0.8641	0.7756
0.1768	75.0	1725	0.9277	0.7677
0.1833	76.0	1748	0.8663	0.7638
0.1696	77.0	1771	0.8302	0.7756
0.1577	78.0	1794	0.8576	0.7638
0.1724	79.0	1817	0.8652	0.7598
0.1525	80.0	1840	0.8567	0.7717
0.158	81.0	1863	0.9139	0.7598
0.1639	82.0	1886	0.9689	0.7520
0.1424	83.0	1909	0.9698	0.7638
0.1224	84.0	1932	1.0239	0.7717
0.1765	85.0	1955	0.9072	0.7795
0.1726	86.0	1978	0.9436	0.7520
0.1584	87.0	2001	0.8775	0.7638
0.164	88.0	2024	0.8592	0.7717
0.1682	89.0	2047	0.9051	0.7638
0.1455	90.0	2070	1.0020	0.7717
0.1596	91.0	2093	0.9423	0.7677
0.1667	92.0	2116	0.9586	0.7638
0.132	93.0	2139	0.9890	0.7638
0.1335	94.0	2162	0.9922	0.7717
0.1538	95.0	2185	0.9534	0.7520
0.1288	96.0	2208	1.0714	0.7480
0.1661	97.0	2231	0.9950	0.7598
0.1392	98.0	2254	0.9866	0.7520
0.1413	99.0	2277	1.0638	0.7598
0.1619	100.0	2300	1.0178	0.7598
0.1537	101.0	2323	0.9892	0.7638
0.137	102.0	2346	0.9524	0.7559
0.1416	103.0	2369	1.0539	0.7402
0.1477	104.0	2392	1.0825	0.7283
0.1283	105.0	2415	1.0008	0.7520
0.1498	106.0	2438	0.9702	0.7638
0.1576	107.0	2461	1.0144	0.7677
0.1433	108.0	2484	0.9457	0.7638
0.1377	109.0	2507	0.9770	0.7677
0.1163	110.0	2530	1.1386	0.7559
0.1449	111.0	2553	1.0589	0.7559
0.1475	112.0	2576	1.0110	0.7480
0.1582	113.0	2599	0.9657	0.7677
0.1291	114.0	2622	0.9563	0.7756
0.1106	115.0	2645	1.1004	0.7480
0.1339	116.0	2668	1.0327	0.7520
0.1344	117.0	2691	1.0161	0.7520
0.1433	118.0	2714	1.0312	0.7559
0.1271	119.0	2737	1.0266	0.7598
0.1222	120.0	2760	1.0119	0.7638
0.1235	121.0	2783	1.0808	0.7520
0.1311	122.0	2806	1.0612	0.7520
0.1219	123.0	2829	1.1412	0.7520
0.148	124.0	2852	1.0836	0.7402
0.1076	125.0	2875	1.0629	0.7559
0.1306	126.0	2898	1.0791	0.7362
0.1153	127.0	2921	1.1495	0.7402
0.1239	128.0	2944	1.1446	0.7520
0.1533	129.0	2967	1.0818	0.7441
0.136	130.0	2990	1.0558	0.7520
0.1189	131.0	3013	1.0423	0.7520
0.1247	132.0	3036	1.0581	0.7638
0.1136	133.0	3059	1.0132	0.7717
0.1492	134.0	3082	1.1127	0.7441
0.1184	135.0	3105	1.1450	0.7402
0.1122	136.0	3128	1.1063	0.7520
0.1047	137.0	3151	1.1029	0.7441
0.1285	138.0	3174	1.1563	0.7402
0.1004	139.0	3197	1.1552	0.7362
0.1285	140.0	3220	1.1097	0.7480
0.1257	141.0	3243	1.1602	0.7402
0.1075	142.0	3266	1.1912	0.7559
0.1098	143.0	3289	1.1894	0.7520
0.1148	144.0	3312	1.1551	0.7441
0.1489	145.0	3335	1.1379	0.7441
0.1461	146.0	3358	1.1726	0.7480
0.1171	147.0	3381	1.1191	0.7441
0.1262	148.0	3404	1.1662	0.7441
0.1137	149.0	3427	1.1283	0.7480
0.1118	150.0	3450	1.1388	0.7480
0.1169	151.0	3473	1.1627	0.7520
0.1021	152.0	3496	1.1821	0.7323
0.1392	153.0	3519	1.1672	0.7323
0.1111	154.0	3542	1.2136	0.7402
0.1298	155.0	3565	1.1966	0.7402
0.1114	156.0	3588	1.1382	0.7362
0.09	157.0	3611	1.1460	0.7323
0.1294	158.0	3634	1.1612	0.7441
0.1186	159.0	3657	1.2204	0.7402
0.1096	160.0	3680	1.2096	0.7441
0.1107	161.0	3703	1.1822	0.7480
0.1094	162.0	3726	1.1908	0.7480
0.1112	163.0	3749	1.1647	0.7402
0.1042	164.0	3772	1.2523	0.7441
0.0993	165.0	3795	1.2040	0.7402
0.105	166.0	3818	1.2296	0.7402
0.1071	167.0	3841	1.2863	0.7480
0.108	168.0	3864	1.2372	0.7441
0.1076	169.0	3887	1.1872	0.7480
0.1107	170.0	3910	1.2354	0.7323
0.1012	171.0	3933	1.2105	0.7441
0.0918	172.0	3956	1.2026	0.7441
0.1043	173.0	3979	1.2925	0.7559
0.1035	174.0	4002	1.2314	0.7402
0.1101	175.0	4025	1.1943	0.7441
0.1084	176.0	4048	1.2069	0.7362
0.1247	177.0	4071	1.2303	0.7520
0.1278	178.0	4094	1.2118	0.7480
0.1117	179.0	4117	1.2213	0.7480
0.1123	180.0	4140	1.2403	0.7480
0.0918	181.0	4163	1.1987	0.7441
0.0827	182.0	4186	1.2358	0.7441
0.0814	183.0	4209	1.2608	0.7441
0.0897	184.0	4232	1.2370	0.7441
0.1321	185.0	4255	1.2317	0.7480
0.1194	186.0	4278	1.2289	0.7441
0.1154	187.0	4301	1.1964	0.7441
0.0964	188.0	4324	1.2009	0.7441
0.0903	189.0	4347	1.2123	0.7441
0.1174	190.0	4370	1.2335	0.7441
0.0846	191.0	4393	1.2399	0.7441
0.1073	192.0	4416	1.2432	0.7441
0.0892	193.0	4439	1.2604	0.7480
0.1158	194.0	4462	1.2473	0.7480
0.1153	195.0	4485	1.2267	0.7441
0.1208	196.0	4508	1.2178	0.7441
0.083	197.0	4531	1.2145	0.7480
0.1331	198.0	4554	1.2215	0.7441
0.0943	199.0	4577	1.2238	0.7441
0.0926	200.0	4600	1.2236	0.7441

Framework versions

Transformers 4.46.0.dev0

Pytorch 2.4.1+cu121

Datasets 3.0.1

Tokenizers 0.20.1

davanstrien/convnextv2-tiny-1k-224-text

convnextv2-tiny-1k-224-text

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

convnextv2-tiny-1k-224-text

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions