You can deploy this model and start earning money today!

samitizerxu/
kelp-from-scratch-segformer-b1-lr-0.0001-cleaned

Name: kelp-from-scratch-segformer-b1-lr-0.0001-cleaned
Availability: InStock

generated_from_trainer

endpoints_compatible

Views

No views yet

kelp-from-scratch-segformer-b1-lr-0.0001-cleaned

This model is a fine-tuned version of on the samitizerxu/kelp_data_rgbagg_swin_nir_int_cleaned dataset. It achieves the following results on the evaluation set:

Iou Kelp: 0.1757
Loss: 0.7377

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: 22
eval_batch_size: 22
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 50

Training results

Training Loss	Epoch	Step	Iou Kelp	Validation Loss
0.9943	0.15	30	0.0093	0.9841
0.9987	0.3	60	0.0091	0.9797
0.9912	0.46	90	0.0093	0.9822
0.9809	0.61	120	0.0089	0.9788
0.9882	0.76	150	0.0096	0.9791
0.9804	0.91	180	0.0070	0.9799
0.9987	1.07	210	0.0104	0.9275
0.997	1.22	240	0.0086	0.9696
0.8985	1.37	270	0.0086	0.9513
0.9921	1.52	300	0.0234	0.8703
0.9656	1.68	330	0.0115	0.9610
0.9229	1.83	360	0.0235	0.8871
0.9999	1.98	390	0.0117	0.9416
0.8409	2.13	420	0.0172	0.9212
0.9533	2.28	450	0.0322	0.8209
0.9815	2.44	480	0.0167	0.9070
0.9567	2.59	510	0.0314	0.8028
0.9747	2.74	540	0.0300	0.8489
0.983	2.89	570	0.0176	0.9069
0.963	3.05	600	0.0312	0.8878
0.9616	3.2	630	0.0249	0.8482
0.9395	3.35	660	0.0418	0.7781
0.8966	3.5	690	0.0155	0.8981
0.9939	3.65	720	0.0219	0.8737
0.9895	3.81	750	0.0107	0.9480
0.9434	3.96	780	0.0266	0.8413
0.9813	4.11	810	0.0323	0.8320
0.9051	4.26	840	0.0379	0.8097
0.9629	4.42	870	0.0447	0.7619
0.9067	4.57	900	0.0330	0.8137
0.9245	4.72	930	0.0253	0.8327
1.0	4.87	960	0.0273	0.8226
0.9869	5.03	990	0.0182	0.8993
0.9236	5.18	1020	0.0390	0.7772
0.9487	5.33	1050	0.0433	0.7845
0.9085	5.48	1080	0.0181	0.9285
0.9518	5.63	1110	0.0514	0.7551
0.9768	5.79	1140	0.0344	0.8475
0.8779	5.94	1170	0.0123	0.9069
0.9556	6.09	1200	0.0183	0.9066
0.8875	6.24	1230	0.1934	0.7787
0.9932	6.4	1260	0.0543	0.8902
0.9991	6.55	1290	0.0467	0.7678
0.9654	6.7	1320	0.0420	0.8434
0.9794	6.85	1350	0.0190	0.9163
0.9659	7.01	1380	0.0345	0.9441
0.8959	7.16	1410	0.0255	0.8717
0.9775	7.31	1440	0.0296	0.9072
0.9406	7.46	1470	0.0331	0.8282
0.9702	7.61	1500	0.0283	0.8532
0.9828	7.77	1530	0.0164	0.8719
0.9511	7.92	1560	0.0248	0.8392
0.9046	8.07	1590	0.0116	0.9260
0.9508	8.22	1620	0.0243	0.8499
0.9535	8.38	1650	0.0185	0.8567
0.9586	8.53	1680	0.0176	0.8867
0.947	8.68	1710	0.0296	0.7973
0.9404	8.83	1740	0.0137	0.8879
1.0	8.98	1770	0.0227	0.8902
0.9618	9.14	1800	0.0419	0.8119
0.8463	9.29	1830	0.0500	0.8065
0.9683	9.44	1860	0.0136	0.9266
0.9087	9.59	1890	0.0357	0.8041
0.946	9.75	1920	0.0426	0.8023
0.9723	9.9	1950	0.0470	0.7823
0.9487	10.05	1980	0.0218	0.8771
0.9483	10.2	2010	0.0265	0.8317
0.9678	10.36	2040	0.0512	0.7447
0.9909	10.51	2070	0.0266	0.8505
0.9688	10.66	2100	0.0315	0.8457
0.9617	10.81	2130	0.0388	0.7916
0.9105	10.96	2160	0.0641	0.7939
0.9447	11.12	2190	0.0338	0.8046
0.9127	11.27	2220	0.0883	0.7771
0.9151	11.42	2250	0.0285	0.8568
0.9339	11.57	2280	0.0773	0.7554
1.0	11.73	2310	0.0452	0.7623
0.9428	11.88	2340	0.0147	0.9230
1.0	12.03	2370	0.0266	0.8265
0.9432	12.18	2400	0.0284	0.8732
0.9436	12.34	2430	0.0398	0.7938
0.9772	12.49	2460	0.0345	0.8073
0.9552	12.64	2490	0.0125	0.9084
1.0	12.79	2520	0.0255	0.8099
0.953	12.94	2550	0.0694	0.7384
0.9225	13.1	2580	0.0286	0.8104
0.9119	13.25	2610	0.0312	0.8538
0.9726	13.4	2640	0.0511	0.7505
0.9674	13.55	2670	0.0504	0.7473
0.972	13.71	2700	0.0496	0.8009
0.9238	13.86	2730	0.0179	0.8479
0.9535	14.01	2760	0.1306	0.7997
0.9509	14.16	2790	0.0254	0.8065
0.8756	14.31	2820	0.0584	0.7479
0.9335	14.47	2850	0.0297	0.7988
0.956	14.62	2880	0.0311	0.8155
0.9544	14.77	2910	0.0526	0.8159
0.8577	14.92	2940	0.0770	0.7906
0.965	15.08	2970	0.0709	0.7446
0.987	15.23	3000	0.0479	0.7767
0.9692	15.38	3030	0.0895	0.8900
0.9602	15.53	3060	0.1262	0.8408
0.9586	15.69	3090	0.0643	0.7370
0.8871	15.84	3120	0.1753	0.7848
0.9259	15.99	3150	0.1110	0.7571
0.9463	16.14	3180	0.0651	0.7799
0.9489	16.29	3210	0.0627	0.7776
0.9814	16.45	3240	0.0476	0.7595
0.926	16.6	3270	0.0588	0.7354
0.8921	16.75	3300	0.0608	0.7637
0.9722	16.9	3330	0.0404	0.7916
0.9535	17.06	3360	0.0520	0.8781
0.9442	17.21	3390	0.0326	0.8189
0.945	17.36	3420	0.1141	0.8753
0.9799	17.51	3450	0.0678	0.7472
0.8504	17.66	3480	0.1633	0.8211
1.0	17.82	3510	0.0479	0.7849
0.9681	17.97	3540	0.0585	0.7741
0.9492	18.12	3570	0.0656	0.7374
0.9481	18.27	3600	0.0817	0.7382
0.9405	18.43	3630	0.0805	0.9278
0.8967	18.58	3660	0.0457	0.7692
0.9215	18.73	3690	0.0615	0.8308
0.9722	18.88	3720	0.1454	0.8367
0.9352	19.04	3750	0.1014	0.7641
0.9581	19.19	3780	0.1549	0.8425
0.9438	19.34	3810	0.0689	0.7524
0.976	19.49	3840	0.1321	0.8181
0.9248	19.64	3870	0.1782	0.8164
0.9114	19.8	3900	0.1553	0.7879
0.8975	19.95	3930	0.1875	0.7522
0.9696	20.1	3960	0.1521	0.8031
0.9217	20.25	3990	0.0667	0.7436
0.9375	20.41	4020	0.0902	0.9042
0.886	20.56	4050	0.0672	0.7541
0.9647	20.71	4080	0.1952	0.7983
0.9029	20.86	4110	0.0600	0.8339
0.9865	21.02	4140	0.0353	0.8191
0.9348	21.17	4170	0.0683	0.9285
0.965	21.32	4200	0.1153	0.7778
0.9006	21.47	4230	0.2049	0.7928
0.9726	21.62	4260	0.0687	0.7431
0.8811	21.78	4290	0.1643	0.7903
0.9622	21.93	4320	0.1069	0.7641
0.9267	22.08	4350	0.0647	0.7764
0.9729	22.23	4380	0.0770	0.7323
0.951	22.39	4410	0.1069	0.8905
0.976	22.54	4440	0.1024	0.7324
0.9763	22.69	4470	0.0679	0.8286
0.912	22.84	4500	0.1492	0.7784
0.8856	22.99	4530	0.1400	0.7411
0.9663	23.15	4560	0.1588	0.8195
0.9577	23.3	4590	0.0532	0.7803
0.9898	23.45	4620	0.1014	0.7892
0.9079	23.6	4650	0.0457	0.7695
0.9014	23.76	4680	0.1119	0.7742
0.959	23.91	4710	0.0781	0.7461
0.9762	24.06	4740	0.0852	0.8429
0.952	24.21	4770	0.0978	0.7348
0.9606	24.37	4800	0.0966	0.7263
0.93	24.52	4830	0.0707	0.7334
0.9514	24.67	4860	0.2207	0.7526
0.9639	24.82	4890	0.0877	0.7545
0.8319	24.97	4920	0.0751	0.7816
0.959	25.13	4950	0.0457	0.7726
0.9875	25.28	4980	0.0877	0.9052
0.9567	25.43	5010	0.0875	0.7308
0.8535	25.58	5040	0.1697	0.8189
0.903	25.74	5070	0.2176	0.7322
0.9654	25.89	5100	0.2082	0.7325
0.9139	26.04	5130	0.0856	0.7604
0.9684	26.19	5160	0.1764	0.8378
0.9869	26.35	5190	0.0372	0.8459
0.9325	26.5	5220	0.2127	0.7494
0.9396	26.65	5250	0.2123	0.7630
0.9522	26.8	5280	0.1121	0.7878
0.9404	26.95	5310	0.0783	0.7300
0.8336	27.11	5340	0.0862	0.8091
0.9827	27.26	5370	0.1633	0.7761
0.9743	27.41	5400	0.1033	0.7903
0.8255	27.56	5430	0.1535	0.7349
0.9828	27.72	5460	0.0835	0.7236
0.9607	27.87	5490	0.1012	0.7503
0.9659	28.02	5520	0.1087	0.7412
0.9467	28.17	5550	0.0687	0.7867
0.9261	28.32	5580	0.1773	0.8152
1.0	28.48	5610	0.0922	0.7728
0.9543	28.63	5640	0.2284	0.7482
0.9198	28.78	5670	0.2101	0.7313
0.9667	28.93	5700	0.1985	0.7698
0.8591	29.09	5730	0.0994	0.7528
0.9697	29.24	5760	0.1437	0.7865
0.9313	29.39	5790	0.1197	0.7443
0.9457	29.54	5820	0.1529	0.8172
0.9283	29.7	5850	0.1204	0.7310
0.8794	29.85	5880	0.2253	0.7703
0.9999	30.0	5910	0.0922	0.7463
1.0	30.15	5940	0.0763	0.7472
0.9674	30.3	5970	0.0678	0.7574
0.9543	30.46	6000	0.1619	0.7388
0.96	30.61	6030	0.1436	0.8416
0.9778	30.76	6060	0.0994	0.7353
0.9436	30.91	6090	0.1649	0.7740
0.9054	31.07	6120	0.1537	0.7387
0.967	31.22	6150	0.1574	0.7569
0.9174	31.37	6180	0.1378	0.7870
0.9667	31.52	6210	0.1505	0.7650
0.9848	31.68	6240	0.1231	0.7584
0.9514	31.83	6270	0.1188	0.7533
0.9179	31.98	6300	0.2073	0.7696
0.9733	32.13	6330	0.0941	0.7400
0.9177	32.28	6360	0.1431	0.8260
0.9338	32.44	6390	0.1259	0.7474
0.9704	32.59	6420	0.2298	0.7447
0.9133	32.74	6450	0.1500	0.7347
0.9121	32.89	6480	0.1538	0.7280
0.9649	33.05	6510	0.1617	0.7385
0.9113	33.2	6540	0.1399	0.7408
0.998	33.35	6570	0.1663	0.7621
0.9567	33.5	6600	0.1559	0.7560
0.9421	33.65	6630	0.1966	0.7766
0.9441	33.81	6660	0.1558	0.7314
0.934	33.96	6690	0.1846	0.7564
0.9874	34.11	6720	0.2541	0.7462
0.8515	34.26	6750	0.2071	0.7591
0.9204	34.42	6780	0.1673	0.7342
0.9358	34.57	6810	0.1883	0.7930
0.8267	34.72	6840	0.2290	0.7462
0.8998	34.87	6870	0.2199	0.7532
0.9496	35.03	6900	0.1121	0.7522
0.9854	35.18	6930	0.1238	0.7288
0.9971	35.33	6960	0.1982	0.7527
0.9621	35.48	6990	0.1837	0.7460
0.9626	35.63	7020	0.1268	0.7404
0.9037	35.79	7050	0.1184	0.7267
0.908	35.94	7080	0.1914	0.7388
0.996	36.09	7110	0.2036	0.7363
0.9635	36.24	7140	0.1858	0.7450
0.9446	36.4	7170	0.1363	0.7285
0.9808	36.55	7200	0.1578	0.7666
0.9212	36.7	7230	0.2064	0.7660
0.8472	36.85	7260	0.1804	0.7241
0.9328	37.01	7290	0.1143	0.7270
0.9276	37.16	7320	0.2104	0.7725
0.9599	37.31	7350	0.2237	0.7334
0.9058	37.46	7380	0.1586	0.7304
0.8654	37.61	7410	0.1439	0.7490
0.9653	37.77	7440	0.1785	0.7817
0.9201	37.92	7470	0.1178	0.7317
0.9545	38.07	7500	0.1523	0.7752
0.9484	38.22	7530	0.1208	0.7194
0.8723	38.38	7560	0.2017	0.7564
0.9555	38.53	7590	0.1065	0.7323
0.9654	38.68	7620	0.1721	0.7586
0.9044	38.83	7650	0.1482	0.7538
0.9745	38.98	7680	0.1507	0.7523
0.991	39.14	7710	0.1344	0.7389
0.9504	39.29	7740	0.1108	0.7170
0.9948	39.44	7770	0.1555	0.7555
0.9458	39.59	7800	0.1324	0.7640
0.9725	39.75	7830	0.1792	0.7599
0.9747	39.9	7860	0.1785	0.7485
0.9779	40.05	7890	0.1751	0.7391
0.9325	40.2	7920	0.2171	0.7406
0.8857	40.36	7950	0.1687	0.7203
0.9229	40.51	7980	0.2092	0.7256
0.9177	40.66	8010	0.1453	0.7217
0.9315	40.81	8040	0.1878	0.7415
0.9942	40.96	8070	0.1602	0.7443
0.9101	41.12	8100	0.1596	0.7546
0.9029	41.27	8130	0.1510	0.7346
0.994	41.42	8160	0.1474	0.7336
0.9862	41.57	8190	0.1274	0.7234
0.9136	41.73	8220	0.1425	0.7433
0.9723	41.88	8250	0.1138	0.7286
0.937	42.03	8280	0.1345	0.7425
0.9773	42.18	8310	0.1405	0.7342
0.9655	42.34	8340	0.1193	0.7290
0.9165	42.49	8370	0.1306	0.7318
0.9409	42.64	8400	0.1504	0.7364
0.976	42.79	8430	0.2013	0.7437
1.0	42.94	8460	0.1821	0.7342
0.967	43.1	8490	0.1685	0.7384
0.9877	43.25	8520	0.1471	0.7409
0.9736	43.4	8550	0.1682	0.7372
1.0	43.55	8580	0.1467	0.7332
0.8718	43.71	8610	0.1380	0.7329
0.997	43.86	8640	0.1314	0.7350
1.0	44.01	8670	0.1372	0.7361
1.0	44.16	8700	0.1442	0.7400
0.8811	44.31	8730	0.1603	0.7432
1.0	44.47	8760	0.1651	0.7373
0.9233	44.62	8790	0.2112	0.7484
0.9555	44.77	8820	0.1837	0.7375
0.8655	44.92	8850	0.1394	0.7348
0.9908	45.08	8880	0.1355	0.7375
0.8959	45.23	8910	0.1391	0.7354
0.9595	45.38	8940	0.1437	0.7325
0.9383	45.53	8970	0.1448	0.7382
0.9417	45.69	9000	0.1793	0.7486
0.9317	45.84	9030	0.1720	0.7391
0.9744	45.99	9060	0.1552	0.7388
0.9443	46.14	9090	0.1486	0.7345
0.9325	46.29	9120	0.1391	0.7383
0.9421	46.45	9150	0.1539	0.7393
0.9451	46.6	9180	0.1436	0.7328
0.9538	46.75	9210	0.1419	0.7342
0.955	46.9	9240	0.1581	0.7433
0.9611	47.06	9270	0.1652	0.7407
0.9296	47.21	9300	0.1716	0.7377
0.9413	47.36	9330	0.1567	0.7374
0.9372	47.51	9360	0.1511	0.7376
0.9524	47.66	9390	0.1586	0.7369
0.9681	47.82	9420	0.1411	0.7370
0.9295	47.97	9450	0.1506	0.7394
0.9983	48.12	9480	0.1503	0.7318
0.8795	48.27	9510	0.1363	0.7276
0.9147	48.43	9540	0.1502	0.7331
0.9063	48.58	9570	0.1556	0.7400
0.9501	48.73	9600	0.1616	0.7346
1.0	48.88	9630	0.1546	0.7307
0.9565	49.04	9660	0.1458	0.7335
0.9266	49.19	9690	0.1525	0.7336
0.935	49.34	9720	0.1516	0.7333
0.8765	49.49	9750	0.1364	0.7313
0.9403	49.64	9780	0.1501	0.7282
0.91	49.8	9810	0.1577	0.7422
0.9521	49.95	9840	0.1757	0.7377

Framework versions

Transformers 4.37.1
Pytorch 2.1.2
Datasets 2.16.1
Tokenizers 0.15.1

samitizerxu/kelp-from-scratch-segformer-b1-lr-0.0001-cleaned

kelp-from-scratch-segformer-b1-lr-0.0001-cleaned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

samitizerxu/
kelp-from-scratch-segformer-b1-lr-0.0001-cleaned