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greenkwd/
lr0.0001_bs16_0519_1844

Name: lr0.0001_bs16_0519_1844
Availability: InStock

generated_from_trainer

nvidia/mit-b0

finetune

other

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lr0.0001_bs16_0519_1844

This model is a fine-tuned version of nvidia/mit-b0 on the greenkwd/upwellingdetection_SST dataset. It achieves the following results on the evaluation set:

Loss: 0.1755
Mean Iou: 0.5363
Mean Accuracy: 0.8678
Overall Accuracy: 0.9055
Accuracy Land: nan
Accuracy Upwelling: 0.9625
Accuracy Not Upwelling: 0.7731
Iou Land: 0.0
Iou Upwelling: 0.8931
Iou Not Upwelling: 0.7159
Dice Macro: 0.9156
Dice Micro: 0.9409

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Land	Accuracy Upwelling	Accuracy Not Upwelling	Iou Upwelling	Iou Not Upwelling	Dice Macro	Dice Micro
1.0767	0.8	20	1.0897	0.1542	0.2922	0.2833	nan	0.2698	0.3145	0.2594	0.2032	0.4326	0.4993
0.8329	1.6	40	0.8808	0.3310	0.6229	0.7352	nan	0.9050	0.3408	0.7083	0.2845	0.6762	0.7598
0.6151	2.4	60	0.5929	0.3649	0.6626	0.7741	nan	0.9425	0.3827	0.7479	0.3467	0.7374	0.8229
0.4885	3.2	80	0.4468	0.4048	0.7183	0.8024	nan	0.9295	0.5072	0.7737	0.4407	0.7906	0.8565
0.4143	4.0	100	0.3858	0.4490	0.7850	0.8224	nan	0.8789	0.6911	0.7932	0.5539	0.8415	0.8837
0.3887	4.8	120	0.3404	0.4770	0.8074	0.8604	nan	0.9405	0.6743	0.8365	0.5943	0.8629	0.9042
0.3883	5.6	140	0.3227	0.4651	0.7818	0.8551	nan	0.9659	0.5977	0.8399	0.5552	0.8560	0.9048
0.3399	6.4	160	0.3085	0.4864	0.8117	0.8689	nan	0.9554	0.6680	0.8524	0.6069	0.8738	0.9139
0.3222	7.2	180	0.2960	0.4991	0.8489	0.8725	nan	0.9081	0.7897	0.8455	0.6519	0.8854	0.9162
0.3004	8.0	200	0.2766	0.5100	0.8492	0.8864	nan	0.9425	0.7559	0.8623	0.6677	0.8914	0.9227
0.2923	8.8	220	0.2705	0.4866	0.8061	0.8739	nan	0.9764	0.6357	0.8558	0.6039	0.8764	0.9183
0.3322	9.6	240	0.2498	0.5111	0.8532	0.8824	nan	0.9265	0.7800	0.8652	0.6681	0.8976	0.9268
0.2637	10.4	260	0.2543	0.5118	0.8506	0.8818	nan	0.9289	0.7723	0.8693	0.6660	0.8983	0.9276
0.311	11.2	280	0.2464	0.4967	0.8166	0.8759	nan	0.9654	0.6678	0.8686	0.6214	0.8873	0.9247
0.306	12.0	300	0.2564	0.4747	0.8084	0.8370	nan	0.8802	0.7365	0.8400	0.5842	0.8695	0.9057
0.3015	12.8	320	0.2357	0.5030	0.8268	0.8869	nan	0.9777	0.6758	0.8652	0.6438	0.8873	0.9242
0.2985	13.6	340	0.2304	0.5180	0.8549	0.8879	nan	0.9378	0.7720	0.8756	0.6784	0.9031	0.9314
0.2767	14.4	360	0.2629	0.4760	0.8013	0.8677	nan	0.9680	0.6345	0.8372	0.5908	0.8557	0.9005
0.2844	15.2	380	0.2568	0.4779	0.7979	0.8715	nan	0.9827	0.6132	0.8440	0.5898	0.8605	0.9067
0.3176	16.0	400	0.2520	0.4860	0.8099	0.8757	nan	0.9750	0.6449	0.8480	0.6100	0.8656	0.9086
0.25	16.8	420	0.2347	0.4974	0.8230	0.8829	nan	0.9734	0.6726	0.8585	0.6339	0.8846	0.9221
0.2454	17.6	440	0.2346	0.4617	0.7711	0.8570	nan	0.9869	0.5554	0.8442	0.5410	0.8628	0.9145
0.2631	18.4	460	0.2196	0.4960	0.8192	0.8679	nan	0.9415	0.6968	0.8694	0.6186	0.8869	0.9225
0.2994	19.2	480	0.2176	0.5328	0.8791	0.9040	nan	0.9415	0.8167	0.8780	0.7205	0.9081	0.9333
0.2413	20.0	500	0.2250	0.4867	0.8032	0.8695	nan	0.9697	0.6368	0.8602	0.5998	0.8799	0.9209
0.2591	20.8	520	0.2358	0.4633	0.7715	0.8520	nan	0.9736	0.5693	0.8500	0.5400	0.8628	0.9131
0.2416	21.6	540	0.2255	0.4960	0.8126	0.8731	nan	0.9645	0.6608	0.8695	0.6186	0.8870	0.9245
0.2587	22.4	560	0.2337	0.5028	0.8335	0.8856	nan	0.9642	0.7029	0.8585	0.6499	0.8842	0.9200
0.2565	23.2	580	0.2204	0.5088	0.8514	0.8783	nan	0.9191	0.7836	0.8624	0.6641	0.8974	0.9263
0.2457	24.0	600	0.2147	0.5111	0.8427	0.8855	nan	0.9501	0.7353	0.8711	0.6621	0.8992	0.9302
0.245	24.8	620	0.2229	0.4848	0.8016	0.8741	nan	0.9837	0.6194	0.8560	0.5983	0.8798	0.9223
0.2615	25.6	640	0.2461	0.4785	0.8043	0.8680	nan	0.9642	0.6443	0.8395	0.5958	0.8673	0.9102
0.2555	26.4	660	0.2112	0.5254	0.8631	0.9000	nan	0.9556	0.7706	0.8755	0.7005	0.9013	0.9300
0.2515	27.2	680	0.2216	0.4729	0.7804	0.8546	nan	0.9669	0.5939	0.8614	0.5574	0.8686	0.9153
0.245	28.0	700	0.2174	0.5126	0.8428	0.8919	nan	0.9661	0.7195	0.8688	0.6689	0.8950	0.9276
0.2598	28.8	720	0.2073	0.5114	0.8465	0.8814	nan	0.9341	0.7590	0.8709	0.6634	0.8996	0.9292
0.2785	29.6	740	0.2287	0.4689	0.7821	0.8399	nan	0.9272	0.6370	0.8471	0.5596	0.8644	0.9075
0.258	30.4	760	0.2406	0.4617	0.7761	0.8604	nan	0.9878	0.5645	0.8349	0.5502	0.8527	0.9052
0.2635	31.2	780	0.2163	0.5149	0.8665	0.8872	nan	0.9186	0.8144	0.8574	0.6874	0.8979	0.9252
0.2929	32.0	800	0.2160	0.4825	0.7950	0.8577	nan	0.9525	0.6376	0.8636	0.5839	0.8759	0.9173
0.2228	32.8	820	0.2137	0.4972	0.8123	0.8704	nan	0.9582	0.6663	0.8744	0.6172	0.8869	0.9239
0.2695	33.6	840	0.2031	0.5196	0.8525	0.8914	nan	0.9501	0.7548	0.8789	0.6799	0.9051	0.9338
0.2341	34.4	860	0.2128	0.5023	0.8243	0.8836	nan	0.9733	0.6752	0.8679	0.6391	0.8927	0.9285
0.2409	35.2	880	0.2230	0.4762	0.8062	0.8444	nan	0.9021	0.7103	0.8396	0.5890	0.8725	0.9097
0.3406	36.0	900	0.2113	0.5042	0.8423	0.8730	nan	0.9193	0.7653	0.8617	0.6510	0.8948	0.9250
0.2631	36.8	920	0.2131	0.5020	0.8260	0.8842	nan	0.9722	0.6798	0.8643	0.6417	0.8898	0.9259
0.2785	37.6	940	0.2164	0.4900	0.8384	0.8587	nan	0.8894	0.7873	0.8356	0.6344	0.8848	0.9158
0.2398	38.4	960	0.2228	0.4711	0.7815	0.8520	nan	0.9585	0.6046	0.8550	0.5584	0.8682	0.9141
0.2301	39.2	980	0.2086	0.5192	0.8543	0.8973	nan	0.9623	0.7463	0.8705	0.6870	0.8954	0.9263
0.2785	40.0	1000	0.2152	0.5016	0.8277	0.8856	nan	0.9730	0.6824	0.8610	0.6438	0.8876	0.9240
0.2151	40.8	1020	0.1982	0.5171	0.8453	0.8935	nan	0.9665	0.7241	0.8759	0.6755	0.9025	0.9332
0.2154	41.6	1040	0.2038	0.5045	0.8361	0.8779	nan	0.9412	0.7309	0.8644	0.6491	0.8951	0.9270
0.2456	42.4	1060	0.2037	0.5180	0.8462	0.8954	nan	0.9698	0.7225	0.8764	0.6775	0.9027	0.9335
0.224	43.2	1080	0.2028	0.5123	0.8421	0.8911	nan	0.9652	0.7191	0.8695	0.6673	0.8993	0.9310
0.1998	44.0	1100	0.2064	0.5007	0.8202	0.8736	nan	0.9542	0.6862	0.8711	0.6311	0.8908	0.9256
0.2385	44.8	1120	0.2041	0.5048	0.8264	0.8779	nan	0.9558	0.6971	0.8752	0.6392	0.8942	0.9280
0.2756	45.6	1140	0.1974	0.5092	0.8329	0.8842	nan	0.9618	0.7039	0.8745	0.6532	0.8981	0.9307
0.2633	46.4	1160	0.2093	0.5000	0.8205	0.8796	nan	0.9690	0.6719	0.8657	0.6342	0.8900	0.9262
0.2228	47.2	1180	0.2020	0.5294	0.8770	0.8993	nan	0.9330	0.8211	0.8752	0.7129	0.9099	0.9347
0.2872	48.0	1200	0.2062	0.5195	0.8533	0.8968	nan	0.9624	0.7443	0.8725	0.6860	0.9010	0.9310
0.2825	48.8	1220	0.2029	0.5010	0.8263	0.8746	nan	0.9476	0.7050	0.8653	0.6378	0.8914	0.9252
0.2834	49.6	1240	0.2036	0.5027	0.8279	0.8811	nan	0.9614	0.6943	0.8646	0.6434	0.8925	0.9270
0.2133	50.4	1260	0.2026	0.5012	0.8286	0.8747	nan	0.9443	0.7129	0.8644	0.6392	0.8924	0.9257
0.3216	51.2	1280	0.1995	0.5016	0.8281	0.8724	nan	0.9392	0.7170	0.8664	0.6383	0.8918	0.9248
0.2363	52.0	1300	0.2124	0.4931	0.8134	0.8757	nan	0.9699	0.6570	0.8596	0.6199	0.8854	0.9238
0.2252	52.8	1320	0.2090	0.5106	0.8486	0.8888	nan	0.9497	0.7475	0.8612	0.6705	0.8955	0.9264
0.2469	53.6	1340	0.2095	0.5055	0.8336	0.8880	nan	0.9702	0.6970	0.8629	0.6536	0.8886	0.9236
0.2603	54.4	1360	0.1962	0.5208	0.8544	0.8952	nan	0.9568	0.7520	0.8750	0.6874	0.9042	0.9332
0.2463	55.2	1380	0.1993	0.5008	0.8397	0.8684	nan	0.9119	0.7675	0.8558	0.6467	0.8919	0.9224
0.2423	56.0	1400	0.2084	0.4959	0.8173	0.8817	nan	0.9791	0.6555	0.8601	0.6276	0.8859	0.9244
0.2718	56.8	1420	0.1986	0.5201	0.8512	0.8956	nan	0.9628	0.7396	0.8755	0.6849	0.9018	0.9318
0.2315	57.6	1440	0.1987	0.5104	0.8367	0.8915	nan	0.9743	0.6990	0.8693	0.6618	0.8949	0.9287
0.2303	58.4	1460	0.1963	0.5038	0.8331	0.8769	nan	0.9431	0.7232	0.8628	0.6487	0.8930	0.9254
0.2283	59.2	1480	0.2002	0.5065	0.8310	0.8845	nan	0.9654	0.6966	0.8698	0.6498	0.8956	0.9293
0.2131	60.0	1500	0.2003	0.4833	0.8058	0.8560	nan	0.9319	0.6798	0.8502	0.5998	0.8781	0.9159
0.2131	60.8	1520	0.1922	0.5047	0.8282	0.8774	nan	0.9518	0.7046	0.8718	0.6422	0.8943	0.9275
0.2232	61.6	1540	0.1890	0.5022	0.8251	0.8778	nan	0.9575	0.6926	0.8681	0.6385	0.8920	0.9265
0.2984	62.4	1560	0.1902	0.5128	0.8412	0.8893	nan	0.9618	0.7206	0.8715	0.6668	0.8992	0.9308
0.2443	63.2	1580	0.1959	0.5127	0.8385	0.8896	nan	0.9668	0.7102	0.8743	0.6637	0.9003	0.9323
0.2432	64.0	1600	0.1885	0.5081	0.8329	0.8797	nan	0.9504	0.7154	0.8723	0.6520	0.8965	0.9285
0.2299	64.8	1620	0.1925	0.5240	0.8567	0.9010	nan	0.9679	0.7454	0.8771	0.6950	0.9024	0.9320
0.2139	65.6	1640	0.1978	0.5060	0.8332	0.8771	nan	0.9436	0.7229	0.8725	0.6455	0.8953	0.9275
0.2197	66.4	1660	0.1851	0.5259	0.8600	0.8975	nan	0.9543	0.7658	0.8806	0.6970	0.9079	0.9353
0.2402	67.2	1680	0.1803	0.5280	0.8606	0.9016	nan	0.9635	0.7577	0.8826	0.7016	0.9091	0.9367
0.2252	68.0	1700	0.1895	0.5155	0.8424	0.8942	nan	0.9725	0.7123	0.8746	0.6719	0.9002	0.9321
0.2216	68.8	1720	0.1863	0.5135	0.8488	0.8823	nan	0.9331	0.7645	0.8709	0.6697	0.9011	0.9299
0.2667	69.6	1740	0.1836	0.5263	0.8559	0.8975	nan	0.9605	0.7513	0.8853	0.6937	0.9092	0.9369
0.2432	70.4	1760	0.1840	0.5274	0.8563	0.9002	nan	0.9666	0.7459	0.8856	0.6965	0.9096	0.9375
0.2606	71.2	1780	0.1809	0.5290	0.8588	0.9008	nan	0.9641	0.7535	0.8869	0.6999	0.9111	0.9384
0.2306	72.0	1800	0.1864	0.5073	0.8318	0.8773	nan	0.9460	0.7176	0.8751	0.6469	0.8953	0.9275
0.2273	72.8	1820	0.1843	0.5057	0.8310	0.8753	nan	0.9422	0.7198	0.8730	0.6441	0.8943	0.9266
0.2476	73.6	1840	0.1841	0.5224	0.8581	0.8907	nan	0.9400	0.7763	0.8786	0.6887	0.9069	0.9339
0.2497	74.4	1860	0.1771	0.5231	0.8544	0.8956	nan	0.9579	0.7508	0.8809	0.6885	0.9074	0.9356
0.2067	75.2	1880	0.1901	0.4959	0.8193	0.8657	nan	0.9358	0.7028	0.8649	0.6228	0.8869	0.9215
0.2265	76.0	1900	0.1807	0.5142	0.8410	0.8838	nan	0.9485	0.7335	0.8795	0.6630	0.9009	0.9311
0.2208	76.8	1920	0.1809	0.5377	0.8750	0.9078	nan	0.9574	0.7925	0.8892	0.7239	0.9154	0.9400
0.2382	77.6	1940	0.1847	0.5317	0.8641	0.9063	nan	0.9700	0.7581	0.8848	0.7103	0.9079	0.9357
0.2228	78.4	1960	0.1793	0.5329	0.8652	0.9055	nan	0.9666	0.7637	0.8870	0.7118	0.9115	0.9382
0.2175	79.2	1980	0.1885	0.5019	0.8226	0.8771	nan	0.9593	0.6860	0.8707	0.6349	0.8925	0.9273
0.2571	80.0	2000	0.1814	0.5280	0.8581	0.8972	nan	0.9562	0.7600	0.8870	0.6969	0.9105	0.9374
0.2099	80.8	2020	0.1805	0.5287	0.8592	0.9024	nan	0.9676	0.7507	0.8852	0.7009	0.9104	0.9381
0.2782	81.6	2040	0.1817	0.5246	0.8561	0.8915	nan	0.9451	0.7671	0.8848	0.6889	0.9078	0.9348
0.2216	82.4	2060	0.1796	0.5229	0.8559	0.8890	nan	0.9391	0.7727	0.8828	0.6858	0.9069	0.9339
0.2124	83.2	2080	0.1782	0.5331	0.8661	0.9007	nan	0.9530	0.7792	0.8910	0.7084	0.9141	0.9394
0.2405	84.0	2100	0.1816	0.5204	0.8454	0.8913	nan	0.9607	0.7300	0.8841	0.6772	0.9051	0.9345
0.2015	84.8	2120	0.1795	0.5258	0.8529	0.8965	nan	0.9623	0.7436	0.8874	0.6902	0.9090	0.9370
0.2171	85.6	2140	0.1761	0.5374	0.8719	0.9066	nan	0.9591	0.7847	0.8920	0.7203	0.9161	0.9409
0.2519	86.4	2160	0.1784	0.5341	0.8656	0.9058	nan	0.9665	0.7647	0.8898	0.7123	0.9134	0.9397
0.2269	87.2	2180	0.1841	0.5166	0.8412	0.8915	nan	0.9675	0.7150	0.8799	0.6700	0.9026	0.9337
0.236	88.0	2200	0.1765	0.5341	0.8647	0.9044	nan	0.9643	0.7651	0.8916	0.7107	0.9142	0.9402
0.2295	88.8	2220	0.1803	0.5212	0.8488	0.8923	nan	0.9581	0.7395	0.8828	0.6807	0.9058	0.9348
0.2201	89.6	2240	0.1831	0.5217	0.8478	0.8954	nan	0.9672	0.7283	0.8835	0.6816	0.9064	0.9360
0.2367	90.4	2260	0.1825	0.5292	0.8616	0.9021	nan	0.9632	0.7601	0.8843	0.7034	0.9098	0.9371
0.2274	91.2	2280	0.1818	0.5229	0.8503	0.8942	nan	0.9606	0.7400	0.8850	0.6836	0.9073	0.9360
0.228	92.0	2300	0.1794	0.5319	0.8604	0.9036	nan	0.9689	0.7520	0.8904	0.7053	0.9130	0.9399
0.2333	92.8	2320	0.1811	0.5207	0.8481	0.8889	nan	0.9506	0.7457	0.8846	0.6774	0.9052	0.9338
0.2323	93.6	2340	0.1800	0.5333	0.8630	0.9052	nan	0.9690	0.7570	0.8904	0.7096	0.9134	0.9400
0.2387	94.4	2360	0.1779	0.5320	0.8614	0.9014	nan	0.9619	0.7608	0.8917	0.7043	0.9132	0.9396
0.2345	95.2	2380	0.1803	0.5233	0.8505	0.8929	nan	0.9569	0.7441	0.8859	0.6841	0.9072	0.9355
0.2076	96.0	2400	0.1768	0.5368	0.8680	0.9069	nan	0.9656	0.7704	0.8929	0.7175	0.9155	0.9410
0.2541	96.8	2420	0.1764	0.5347	0.8654	0.9045	nan	0.9636	0.7671	0.8917	0.7125	0.9143	0.9401
0.2084	97.6	2440	0.1772	0.5322	0.8621	0.9015	nan	0.9611	0.7630	0.8909	0.7058	0.9131	0.9393
0.2323	98.4	2460	0.1766	0.5336	0.8641	0.9028	nan	0.9612	0.7669	0.8917	0.7091	0.9140	0.9399
0.2347	99.2	2480	0.1756	0.5359	0.8673	0.9047	nan	0.9614	0.7732	0.8931	0.7146	0.9155	0.9408
0.1893	100.0	2500	0.1755	0.5363	0.8678	0.9055	nan	0.9625	0.7731	0.8931	0.7159	0.9156	0.9409

Framework versions

Transformers 4.41.2
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.19.1

greenkwd/lr0.0001_bs16_0519_1844

lr0.0001_bs16_0519_1844

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

greenkwd/
lr0.0001_bs16_0519_1844