learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 1
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Medium	Mar Large	Map Polyp	Mar 100 Polyp
328.7584	1.0	100	105.4353	0.0217	0.0467	0.0164	0.0	0.0235	0.0844	0.2213	0.2839	0.0	0.2995	0.0217	0.2839
54.5087	2.0	200	29.3093	0.0641	0.1127	0.0625	0.1482	0.0671	0.2147	0.5171	0.6886	0.24	0.7145	0.0641	0.6886
18.273	3.0	300	13.9644	0.195	0.2841	0.2099	0.0152	0.2089	0.3654	0.6815	0.7872	0.59	0.801	0.195	0.7872
12.3686	4.0	400	9.2201	0.2061	0.2977	0.2192	0.0328	0.2194	0.3621	0.7118	0.8924	0.7	0.9065	0.2061	0.8924
10.3152	5.0	500	7.9569	0.4353	0.561	0.4655	0.1308	0.4571	0.5218	0.7815	0.8905	0.65	0.907	0.4353	0.8905
9.877	6.0	600	8.6031	0.3128	0.399	0.3417	0.1584	0.3279	0.4848	0.7682	0.91	0.76	0.922	0.3128	0.91
9.3949	7.0	700	7.6619	0.5258	0.7045	0.5629	0.1969	0.5489	0.5844	0.7763	0.9185	0.67	0.9355	0.5258	0.9185
8.9883	8.0	800	7.7975	0.4233	0.57	0.4376	0.1904	0.4408	0.5422	0.791	0.9128	0.82	0.922	0.4233	0.9128
8.5345	9.0	900	7.6628	0.2454	0.3437	0.2641	0.2014	0.2574	0.391	0.7118	0.9028	0.75	0.915	0.2454	0.9028
8.3354	10.0	1000	8.2167	0.2949	0.4087	0.3156	0.1208	0.3114	0.4507	0.7199	0.8872	0.6	0.906	0.2949	0.8872
8.044	11.0	1100	7.1574	0.4414	0.5745	0.4718	0.2895	0.461	0.5569	0.7905	0.9076	0.78	0.9185	0.4414	0.9076
8.0058	12.0	1200	7.0897	0.344	0.4723	0.3711	0.2359	0.3603	0.5246	0.8232	0.9137	0.78	0.925	0.344	0.9137
7.8317	13.0	1300	7.3114	0.3671	0.5186	0.3898	0.1734	0.3846	0.5351	0.7711	0.9081	0.8	0.918	0.3671	0.9081
7.5776	14.0	1400	7.5346	0.3043	0.4337	0.3217	0.1102	0.3199	0.473	0.7773	0.9109	0.79	0.9215	0.3043	0.9109
7.367	15.0	1500	6.6930	0.4188	0.5544	0.4475	0.2315	0.4357	0.546	0.8142	0.9218	0.84	0.9305	0.4188	0.9218
7.249	16.0	1600	6.7257	0.4509	0.6084	0.4975	0.2494	0.4683	0.5981	0.8469	0.909	0.8	0.919	0.4509	0.909
7.1063	17.0	1700	7.8123	0.3076	0.4209	0.3293	0.1455	0.3224	0.4943	0.7412	0.9043	0.77	0.9155	0.3076	0.9043
7.1784	18.0	1800	7.4297	0.3582	0.4877	0.3739	0.1887	0.3749	0.5071	0.7962	0.9166	0.74	0.93	0.3582	0.9166
7.0446	19.0	1900	7.1160	0.3486	0.4704	0.3853	0.203	0.3635	0.5555	0.7957	0.909	0.78	0.92	0.3486	0.909
7.0708	20.0	2000	6.9090	0.4352	0.6138	0.4673	0.1152	0.455	0.5341	0.7995	0.9185	0.87	0.9255	0.4352	0.9185
7.0069	21.0	2100	6.4302	0.4221	0.5845	0.439	0.1887	0.44	0.5038	0.828	0.9194	0.8	0.93	0.4221	0.9194
6.9179	22.0	2200	6.8735	0.2883	0.4071	0.3019	0.1738	0.301	0.4047	0.7839	0.9009	0.77	0.912	0.2883	0.9009
6.7794	23.0	2300	6.9011	0.4379	0.6018	0.481	0.1178	0.4587	0.5265	0.7915	0.9052	0.8	0.915	0.4379	0.9052
6.9367	24.0	2400	5.8450	0.5848	0.7835	0.6384	0.1811	0.61	0.6313	0.8398	0.9171	0.77	0.929	0.5848	0.9171
6.8596	25.0	2500	6.6438	0.3867	0.5434	0.4014	0.173	0.4041	0.5246	0.7773	0.8995	0.77	0.9105	0.3867	0.8995
6.7357	26.0	2600	7.0414	0.4158	0.5564	0.4631	0.2366	0.4349	0.5796	0.7967	0.8948	0.72	0.908	0.4158	0.8948
6.7968	27.0	2700	6.7433	0.528	0.7024	0.5641	0.1794	0.5525	0.6123	0.8573	0.9209	0.79	0.932	0.528	0.9209
6.5425	28.0	2800	6.8662	0.4346	0.587	0.4484	0.1637	0.4535	0.6251	0.828	0.9109	0.82	0.92	0.4346	0.9109
6.6149	29.0	2900	7.1492	0.4277	0.5659	0.4601	0.357	0.4467	0.5716	0.8237	0.9114	0.77	0.923	0.4277	0.9114
6.4956	30.0	3000	6.4835	0.4805	0.6497	0.5108	0.2116	0.5006	0.6493	0.8431	0.9081	0.78	0.919	0.4805	0.9081
6.325	31.0	3100	6.4725	0.4354	0.5666	0.4718	0.3062	0.4543	0.6327	0.8403	0.9114	0.77	0.923	0.4354	0.9114
6.4792	32.0	3200	6.5891	0.4733	0.6166	0.4951	0.1749	0.4961	0.646	0.8355	0.9118	0.77	0.9235	0.4733	0.9118
6.4267	33.0	3300	6.2471	0.5297	0.6574	0.584	0.417	0.5506	0.6559	0.8649	0.91	0.72	0.924	0.5297	0.91
6.4015	34.0	3400	6.2159	0.5927	0.7575	0.6442	0.3905	0.614	0.6559	0.8393	0.9071	0.85	0.9145	0.5927	0.9071
6.2961	35.0	3500	6.9543	0.4289	0.6037	0.45	0.3703	0.442	0.4882	0.7972	0.8815	0.81	0.8895	0.4289	0.8815
6.1437	36.0	3600	6.2903	0.5107	0.6672	0.5503	0.1753	0.5298	0.6137	0.8436	0.9095	0.8	0.9195	0.5107	0.9095
6.1999	37.0	3700	6.4701	0.465	0.6323	0.4888	0.2612	0.4825	0.5555	0.7877	0.8905	0.81	0.899	0.465	0.8905
6.1331	38.0	3800	6.0898	0.461	0.6161	0.4959	0.1816	0.4799	0.6327	0.8218	0.9	0.79	0.91	0.461	0.9
6.1776	39.0	3900	6.2661	0.468	0.6355	0.502	0.3374	0.486	0.546	0.8232	0.9062	0.8	0.916	0.468	0.9062
6.018	40.0	4000	5.8017	0.5797	0.7618	0.6432	0.2132	0.6023	0.6038	0.8445	0.9043	0.85	0.9115	0.5797	0.9043
6.1899	41.0	4100	5.7870	0.5732	0.7472	0.6297	0.1139	0.5978	0.6517	0.836	0.9047	0.85	0.912	0.5732	0.9047
5.9885	42.0	4200	5.8262	0.6162	0.793	0.6722	0.36	0.6394	0.637	0.8573	0.9076	0.83	0.916	0.6162	0.9076
5.9469	43.0	4300	5.9656	0.5803	0.7559	0.622	0.4129	0.603	0.6483	0.8384	0.9047	0.85	0.912	0.5803	0.9047
5.9479	44.0	4400	6.0606	0.5878	0.7564	0.6396	0.3012	0.6089	0.6066	0.8479	0.9043	0.84	0.912	0.5878	0.9043
5.8736	45.0	4500	6.1277	0.5992	0.7708	0.6414	0.25	0.6238	0.6588	0.8474	0.9071	0.83	0.9155	0.5992	0.9071
6.0308	46.0	4600	6.0510	0.5644	0.7258	0.6109	0.3322	0.5876	0.6626	0.8474	0.9014	0.85	0.9085	0.5644	0.9014
5.8821	47.0	4700	6.1862	0.5191	0.651	0.5563	0.1103	0.5429	0.6441	0.8384	0.9114	0.82	0.9205	0.5191	0.9114
5.8174	48.0	4800	6.2716	0.5513	0.7038	0.5877	0.1605	0.5752	0.6398	0.8526	0.9142	0.84	0.9225	0.5513	0.9142
5.8138	49.0	4900	6.0443	0.5792	0.7603	0.6092	0.3321	0.5998	0.6393	0.8346	0.8981	0.79	0.908	0.5792	0.8981
5.8482	50.0	5000	6.5761	0.4936	0.6482	0.5428	0.289	0.5163	0.5758	0.7943	0.8896	0.75	0.901	0.4936	0.8896
5.7062	51.0	5100	6.1185	0.5283	0.6686	0.5753	0.3035	0.5505	0.6232	0.8341	0.9118	0.87	0.9185	0.5283	0.9118
5.6989	52.0	5200	6.0044	0.5856	0.7479	0.6369	0.2705	0.6085	0.6844	0.8308	0.8962	0.82	0.9045	0.5856	0.8962
5.5779	53.0	5300	5.9162	0.6011	0.77	0.6466	0.3969	0.6239	0.7028	0.8536	0.9	0.84	0.9075	0.6011	0.9
5.5422	54.0	5400	6.1687	0.5609	0.7387	0.6005	0.2592	0.5814	0.6218	0.8336	0.8995	0.84	0.907	0.5609	0.8995
5.5615	55.0	5500	7.0190	0.456	0.631	0.4893	0.3124	0.4725	0.5336	0.8024	0.8886	0.83	0.896	0.456	0.8886
5.5674	56.0	5600	6.3056	0.5218	0.7175	0.5431	0.4512	0.5397	0.6318	0.837	0.8967	0.86	0.903	0.5218	0.8967
5.4516	57.0	5700	5.6783	0.6161	0.8042	0.6533	0.4324	0.6357	0.6716	0.8469	0.9	0.87	0.906	0.6161	0.9
5.415	58.0	5800	5.9260	0.6019	0.7819	0.6293	0.4337	0.6246	0.6725	0.8408	0.9009	0.83	0.909	0.6019	0.9009
5.5503	59.0	5900	6.1504	0.5291	0.7104	0.5634	0.4015	0.5479	0.591	0.8502	0.9014	0.82	0.91	0.5291	0.9014
5.4917	60.0	6000	5.8398	0.5809	0.7735	0.6137	0.3969	0.6001	0.6474	0.8573	0.8934	0.79	0.903	0.5809	0.8934
5.5592	61.0	6100	5.7688	0.611	0.7941	0.6522	0.3962	0.6297	0.6408	0.8403	0.8995	0.84	0.907	0.611	0.8995
5.3401	62.0	6200	5.7462	0.5848	0.775	0.5963	0.4851	0.6033	0.6536	0.8474	0.9047	0.88	0.9105	0.5848	0.9047
5.3558	63.0	6300	5.8356	0.5816	0.7882	0.619	0.4334	0.5982	0.6332	0.8223	0.8791	0.9	0.8825	0.5816	0.8791
5.326	64.0	6400	6.2211	0.5195	0.707	0.5443	0.3136	0.5373	0.5886	0.8256	0.8877	0.83	0.895	0.5195	0.8877
5.278	65.0	6500	5.3357	0.6396	0.8254	0.6918	0.473	0.6568	0.6773	0.8545	0.8986	0.87	0.9045	0.6396	0.8986
5.315	66.0	6600	5.8402	0.6183	0.8016	0.6708	0.4752	0.6386	0.6616	0.8602	0.9005	0.87	0.9065	0.6183	0.9005
5.3418	67.0	6700	5.4617	0.6167	0.8373	0.6565	0.4112	0.6353	0.6588	0.8474	0.8981	0.85	0.905	0.6167	0.8981
5.2841	68.0	6800	5.4826	0.6696	0.8578	0.7252	0.3299	0.6891	0.6882	0.8526	0.8981	0.87	0.904	0.6696	0.8981
5.2984	69.0	6900	5.4558	0.6553	0.866	0.6974	0.4049	0.671	0.6863	0.8355	0.9	0.88	0.9055	0.6553	0.9
5.1851	70.0	7000	5.9259	0.5741	0.7469	0.6277	0.2257	0.5971	0.6161	0.8303	0.8934	0.86	0.8995	0.5741	0.8934
5.181	71.0	7100	5.4635	0.6607	0.8592	0.7119	0.4913	0.6761	0.6806	0.8412	0.9024	0.84	0.91	0.6607	0.9024
5.1508	72.0	7200	5.6232	0.611	0.8037	0.6533	0.4713	0.6282	0.654	0.8422	0.9052	0.89	0.9105	0.611	0.9052
5.1352	73.0	7300	5.6329	0.6132	0.818	0.6484	0.5055	0.6291	0.6659	0.8408	0.8948	0.84	0.902	0.6132	0.8948
5.0835	74.0	7400	5.2254	0.6655	0.862	0.7302	0.4657	0.6816	0.6948	0.8545	0.8991	0.88	0.9045	0.6655	0.8991
5.0506	75.0	7500	5.5285	0.6407	0.8335	0.6843	0.3756	0.6603	0.6673	0.8559	0.9052	0.87	0.9115	0.6407	0.9052
5.0889	76.0	7600	5.6099	0.604	0.8039	0.6636	0.4564	0.6204	0.6583	0.8308	0.8924	0.86	0.8985	0.604	0.8924
5.0013	77.0	7700	5.2094	0.6616	0.863	0.7203	0.3771	0.6808	0.6962	0.8479	0.8896	0.85	0.896	0.6616	0.8896
5.1114	78.0	7800	5.1092	0.6651	0.8656	0.7255	0.4837	0.6786	0.6882	0.8488	0.8891	0.85	0.8955	0.6651	0.8891
5.0461	79.0	7900	5.1339	0.6623	0.8689	0.718	0.4655	0.6796	0.6962	0.8455	0.9033	0.86	0.91	0.6623	0.9033
5.0452	80.0	8000	5.3328	0.6569	0.8528	0.7302	0.4636	0.6762	0.6948	0.8469	0.8972	0.88	0.9025	0.6569	0.8972
5.0501	81.0	8100	5.5217	0.6503	0.8424	0.6965	0.408	0.6679	0.673	0.8431	0.8957	0.86	0.902	0.6503	0.8957
4.9118	82.0	8200	5.6498	0.6337	0.8333	0.6797	0.3812	0.6538	0.6597	0.8289	0.8938	0.84	0.901	0.6337	0.8938
4.971	83.0	8300	5.7732	0.6249	0.823	0.6661	0.467	0.6435	0.6559	0.8389	0.891	0.78	0.901	0.6249	0.891
4.8744	84.0	8400	5.3769	0.678	0.8753	0.738	0.4127	0.6943	0.6981	0.8517	0.9005	0.9	0.905	0.678	0.9005
4.9024	85.0	8500	5.2106	0.6799	0.8813	0.7143	0.392	0.6986	0.7033	0.8427	0.8943	0.84	0.9015	0.6799	0.8943
4.8361	86.0	8600	5.1924	0.6726	0.8596	0.7193	0.3602	0.6921	0.6981	0.8521	0.9005	0.86	0.907	0.6726	0.9005
4.7713	87.0	8700	5.2097	0.6786	0.8854	0.7263	0.389	0.6958	0.6853	0.8488	0.8991	0.87	0.905	0.6786	0.8991
4.8765	88.0	8800	5.4051	0.6566	0.8497	0.7137	0.4111	0.6757	0.6773	0.8398	0.8825	0.82	0.89	0.6566	0.8825
4.7893	89.0	8900	5.3679	0.6497	0.8468	0.7021	0.4852	0.6671	0.6886	0.8393	0.8967	0.86	0.903	0.6497	0.8967
4.7177	90.0	9000	5.3805	0.6533	0.8489	0.7066	0.4542	0.6711	0.6919	0.8313	0.8915	0.82	0.8995	0.6533	0.8915
4.7715	91.0	9100	5.6189	0.6267	0.8128	0.6736	0.4737	0.6424	0.6564	0.8427	0.8967	0.86	0.903	0.6267	0.8967
4.7119	92.0	9200	5.5511	0.6398	0.8312	0.6803	0.5786	0.6559	0.6526	0.8318	0.8929	0.83	0.9005	0.6398	0.8929
4.695	93.0	9300	5.5682	0.6327	0.8241	0.667	0.4086	0.6499	0.6716	0.8327	0.8924	0.85	0.899	0.6327	0.8924
4.6644	94.0	9400	5.3228	0.662	0.855	0.6994	0.3843	0.68	0.6858	0.8464	0.8962	0.87	0.902	0.662	0.8962
4.6613	95.0	9500	5.2617	0.6644	0.8642	0.7088	0.5257	0.6779	0.6929	0.8488	0.8981	0.83	0.906	0.6644	0.8981
4.6428	96.0	9600	5.0369	0.6893	0.8848	0.7569	0.4619	0.7064	0.709	0.8583	0.8934	0.82	0.9015	0.6893	0.8934
4.6383	97.0	9700	5.3712	0.6373	0.8179	0.6922	0.4024	0.6536	0.6664	0.8488	0.8991	0.89	0.904	0.6373	0.8991
4.6614	98.0	9800	5.2165	0.6599	0.857	0.7168	0.3705	0.6784	0.6891	0.8403	0.8882	0.86	0.894	0.6599	0.8882
4.6073	99.0	9900	5.2039	0.6479	0.8478	0.6905	0.4192	0.666	0.6825	0.8445	0.8877	0.83	0.895	0.6479	0.8877
4.5726	100.0	10000	5.2745	0.663	0.8686	0.7038	0.4939	0.6774	0.6886	0.8431	0.8848	0.85	0.891	0.663	0.8848
4.5516	101.0	10100	5.1948	0.6682	0.8786	0.7233	0.5113	0.6813	0.7066	0.8389	0.8863	0.87	0.8915	0.6682	0.8863
4.5178	102.0	10200	5.0016	0.6808	0.8885	0.7253	0.4145	0.6975	0.6991	0.8398	0.8882	0.88	0.893	0.6808	0.8882
4.554	103.0	10300	5.1041	0.6713	0.8764	0.7146	0.484	0.6839	0.7028	0.8365	0.8896	0.87	0.895	0.6713	0.8896
4.5047	104.0	10400	5.0536	0.6733	0.8881	0.7346	0.5161	0.6861	0.69	0.8313	0.8801	0.84	0.8865	0.6733	0.8801
4.4769	105.0	10500	5.0607	0.6881	0.9005	0.7435	0.5438	0.6998	0.7137	0.8389	0.882	0.82	0.8895	0.6881	0.882
4.4684	106.0	10600	5.0023	0.6854	0.8926	0.7468	0.5582	0.6987	0.718	0.8408	0.8953	0.82	0.9035	0.6854	0.8953
4.5203	107.0	10700	5.0049	0.6807	0.8826	0.7457	0.554	0.6952	0.7171	0.8645	0.8943	0.83	0.902	0.6807	0.8943
4.413	108.0	10800	4.9532	0.6846	0.8939	0.7492	0.5038	0.6991	0.718	0.8403	0.8915	0.81	0.9	0.6846	0.8915
4.4614	109.0	10900	4.9174	0.6956	0.8992	0.7565	0.4832	0.7107	0.7218	0.8427	0.8953	0.84	0.9025	0.6956	0.8953
4.4055	110.0	11000	5.0199	0.6802	0.8868	0.7409	0.4514	0.6963	0.7076	0.8346	0.8991	0.82	0.9075	0.6802	0.8991
4.3637	111.0	11100	4.9812	0.6814	0.8892	0.7393	0.5097	0.696	0.7171	0.8469	0.8919	0.79	0.9015	0.6814	0.8919
4.4024	112.0	11200	4.9323	0.6884	0.9042	0.7445	0.5403	0.7008	0.7133	0.845	0.8915	0.82	0.8995	0.6884	0.8915
4.3255	113.0	11300	4.8589	0.6922	0.9051	0.7643	0.5214	0.7066	0.7114	0.8417	0.8905	0.82	0.8985	0.6922	0.8905
4.3582	114.0	11400	4.9238	0.6879	0.9075	0.749	0.4796	0.7023	0.7147	0.8374	0.8877	0.81	0.896	0.6879	0.8877
4.287	115.0	11500	4.9863	0.6886	0.9018	0.7404	0.4966	0.703	0.7166	0.8408	0.8867	0.83	0.894	0.6886	0.8867
4.3159	116.0	11600	5.0250	0.6752	0.8889	0.7215	0.5872	0.6885	0.6948	0.8455	0.8948	0.82	0.903	0.6752	0.8948
4.3536	117.0	11700	4.8935	0.6867	0.8959	0.7413	0.4453	0.7024	0.7142	0.8398	0.8872	0.82	0.895	0.6867	0.8872
4.2804	118.0	11800	4.9059	0.6874	0.9026	0.7391	0.4907	0.701	0.7137	0.8403	0.8919	0.84	0.899	0.6874	0.8919
4.2833	119.0	11900	4.9931	0.679	0.8909	0.7321	0.5342	0.6925	0.7085	0.846	0.8953	0.84	0.9025	0.679	0.8953
4.294	120.0	12000	4.9152	0.6839	0.8974	0.7506	0.5001	0.6977	0.728	0.8469	0.8877	0.83	0.895	0.6839	0.8877
4.2876	121.0	12100	4.8559	0.6996	0.9114	0.768	0.4886	0.7135	0.7199	0.8393	0.8915	0.84	0.8985	0.6996	0.8915
4.3188	122.0	12200	5.0836	0.6661	0.8779	0.7081	0.5014	0.6801	0.6981	0.8403	0.8919	0.83	0.8995	0.6661	0.8919
4.2945	123.0	12300	4.9716	0.6945	0.9015	0.7443	0.5274	0.7081	0.7185	0.8436	0.8929	0.83	0.9005	0.6945	0.8929
4.2278	124.0	12400	4.9418	0.686	0.8984	0.7362	0.4882	0.6988	0.7204	0.8469	0.8867	0.83	0.894	0.686	0.8867
4.2593	125.0	12500	5.0332	0.686	0.9005	0.7381	0.5344	0.6971	0.7209	0.8408	0.8858	0.85	0.892	0.686	0.8858
4.2182	126.0	12600	4.9317	0.6852	0.8955	0.7404	0.5508	0.6976	0.7223	0.8441	0.8886	0.84	0.8955	0.6852	0.8886
4.2638	127.0	12700	5.0511	0.6708	0.8849	0.7274	0.5834	0.683	0.7123	0.8393	0.8886	0.85	0.895	0.6708	0.8886
4.1952	128.0	12800	5.0145	0.6722	0.889	0.7223	0.5595	0.6845	0.7052	0.8346	0.8825	0.84	0.889	0.6722	0.8825
4.2047	129.0	12900	4.9855	0.6842	0.8966	0.732	0.5326	0.6973	0.7175	0.8398	0.8806	0.84	0.887	0.6842	0.8806
4.215	130.0	13000	4.9706	0.6808	0.8958	0.7319	0.5291	0.6942	0.7114	0.8332	0.8877	0.83	0.895	0.6808	0.8877
4.2377	131.0	13100	4.9718	0.6839	0.898	0.7366	0.5537	0.6973	0.7104	0.8351	0.881	0.82	0.8885	0.6839	0.881
4.1521	132.0	13200	4.9658	0.6859	0.8964	0.745	0.5394	0.6986	0.7152	0.8384	0.8801	0.85	0.886	0.6859	0.8801
4.205	133.0	13300	4.9849	0.6792	0.8912	0.7382	0.4949	0.693	0.7081	0.8322	0.8839	0.83	0.891	0.6792	0.8839
4.1716	134.0	13400	4.9999	0.6804	0.8935	0.7385	0.4934	0.6937	0.7038	0.8408	0.8834	0.82	0.891	0.6804	0.8834
4.1556	135.0	13500	5.0120	0.6811	0.8936	0.7433	0.5101	0.6943	0.7019	0.8417	0.8815	0.82	0.889	0.6811	0.8815
4.1656	136.0	13600	5.0309	0.674	0.8867	0.7315	0.5147	0.6871	0.7066	0.8365	0.882	0.84	0.8885	0.674	0.882
4.1715	137.0	13700	4.9570	0.6795	0.8966	0.7313	0.4992	0.6932	0.7109	0.8313	0.8834	0.83	0.8905	0.6795	0.8834
4.1291	138.0	13800	4.9760	0.6796	0.8925	0.7315	0.511	0.6934	0.7161	0.8336	0.8872	0.84	0.894	0.6796	0.8872
4.1406	139.0	13900	4.9690	0.6851	0.8992	0.7308	0.5089	0.6988	0.7114	0.8294	0.8815	0.84	0.888	0.6851	0.8815
4.1242	140.0	14000	4.9718	0.6788	0.8946	0.7239	0.4992	0.6924	0.7038	0.8322	0.8829	0.84	0.8895	0.6788	0.8829
4.1337	141.0	14100	4.9832	0.6771	0.8921	0.7255	0.4862	0.6907	0.7095	0.8355	0.882	0.82	0.8895	0.6771	0.882
4.1766	142.0	14200	4.9979	0.6767	0.8893	0.7238	0.5086	0.6902	0.7147	0.8403	0.8834	0.84	0.89	0.6767	0.8834
4.158	143.0	14300	4.9638	0.6823	0.8977	0.7327	0.5184	0.6953	0.7104	0.8351	0.8863	0.84	0.893	0.6823	0.8863
4.1759	144.0	14400	4.9966	0.6756	0.8865	0.7228	0.5223	0.6885	0.7071	0.8384	0.8825	0.84	0.889	0.6756	0.8825
4.122	145.0	14500	4.9467	0.6819	0.8952	0.7302	0.5193	0.695	0.7152	0.8379	0.8858	0.83	0.893	0.6819	0.8858
4.1834	146.0	14600	4.9808	0.6808	0.895	0.7261	0.5239	0.6937	0.7057	0.8322	0.8867	0.83	0.894	0.6808	0.8867
4.1443	147.0	14700	4.9647	0.6848	0.9002	0.7371	0.5254	0.6979	0.7076	0.837	0.8825	0.84	0.889	0.6848	0.8825
4.1243	148.0	14800	5.0032	0.6754	0.8847	0.7296	0.525	0.6883	0.7104	0.8389	0.8844	0.84	0.891	0.6754	0.8844
4.1306	149.0	14900	4.9693	0.6777	0.8919	0.7318	0.5181	0.6902	0.7118	0.8336	0.8848	0.84	0.8915	0.6777	0.8848
4.1316	150.0	15000	5.0121	0.6732	0.8882	0.7185	0.5121	0.6864	0.7137	0.8332	0.8834	0.84	0.89	0.6732	0.8834

Framework versions

Transformers 4.53.0.dev0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1

goodcasper/kvasir_seg_rtdetr_r18

kvasir_seg_rtdetr_r18

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

goodcasper/
kvasir_seg_rtdetr_r18