learning_rate: 6e-05
train_batch_size: 48
eval_batch_size: 48
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: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Necrosis	Accuracy Root	Iou Background	Iou Necrosis	Iou Root
1.0774	0.9091	20	1.0862	0.2257	0.4936	0.3667	0.2174	0.3590	0.9045	0.2172	0.0290	0.4308
0.79	1.8182	40	0.8729	0.5114	0.6752	0.8224	0.8068	0.2428	0.9761	0.8053	0.0818	0.6471
0.5765	2.7273	60	0.5984	0.6150	0.7260	0.9117	0.9226	0.2759	0.9794	0.9197	0.1919	0.7335
0.4356	3.6364	80	0.4563	0.7147	0.8218	0.9384	0.9483	0.5489	0.9683	0.9448	0.4062	0.7931
0.4001	4.5455	100	0.3937	0.7578	0.8837	0.9442	0.9511	0.7475	0.9525	0.9478	0.5178	0.8078
0.372	5.4545	120	0.3180	0.7836	0.8850	0.9531	0.9617	0.7345	0.9587	0.9578	0.5651	0.8279
0.2767	6.3636	140	0.2898	0.7942	0.9005	0.9541	0.9612	0.7829	0.9576	0.9574	0.5929	0.8322
0.26	7.2727	160	0.2609	0.7970	0.8839	0.9542	0.9600	0.7185	0.9731	0.9565	0.6054	0.8290
0.2399	8.1818	180	0.2031	0.8365	0.9099	0.9683	0.9782	0.7887	0.9628	0.9730	0.6605	0.8761
0.1764	9.0909	200	0.1796	0.8441	0.9299	0.9724	0.9886	0.8701	0.9311	0.9801	0.6641	0.8882
0.1436	10.0	220	0.1594	0.8520	0.9046	0.9752	0.9886	0.7623	0.9630	0.9813	0.6742	0.9004
0.1332	10.9091	240	0.1391	0.8572	0.9300	0.9755	0.9920	0.8626	0.9354	0.9825	0.6892	0.8998
0.1218	11.8182	260	0.1351	0.8519	0.9349	0.9744	0.9903	0.8812	0.9333	0.9822	0.6776	0.8959
0.1128	12.7273	280	0.1128	0.8644	0.9170	0.9774	0.9930	0.8082	0.9499	0.9833	0.7022	0.9077
0.0997	13.6364	300	0.1102	0.8603	0.9145	0.9763	0.9881	0.7900	0.9654	0.9822	0.6948	0.9038
0.0914	14.5455	320	0.1025	0.8661	0.9256	0.9776	0.9929	0.8378	0.9462	0.9838	0.7067	0.9078
0.0958	15.4545	340	0.0968	0.8653	0.9096	0.9779	0.9931	0.7792	0.9565	0.9835	0.7030	0.9095
0.0864	16.3636	360	0.0903	0.8672	0.9139	0.9784	0.9909	0.7847	0.9660	0.9844	0.7064	0.9109
0.0817	17.2727	380	0.0900	0.8543	0.8939	0.9774	0.9924	0.7229	0.9664	0.9843	0.6714	0.9071
0.0737	18.1818	400	0.0841	0.8661	0.9204	0.9775	0.9908	0.8130	0.9574	0.9834	0.7071	0.9078
0.0828	19.0909	420	0.0858	0.8697	0.9343	0.9786	0.9916	0.8593	0.9521	0.9853	0.7117	0.9120
0.0907	20.0	440	0.0799	0.8706	0.9186	0.9784	0.9945	0.8131	0.9482	0.9840	0.7180	0.9096
0.0776	20.9091	460	0.0769	0.8733	0.9256	0.9796	0.9935	0.8283	0.9551	0.9859	0.7192	0.9149
0.0662	21.8182	480	0.0734	0.8698	0.9184	0.9793	0.9921	0.7996	0.9634	0.9856	0.7094	0.9143
0.071	22.7273	500	0.0727	0.8671	0.9088	0.9791	0.9932	0.7694	0.9637	0.9854	0.7023	0.9135
0.0563	23.6364	520	0.0701	0.8723	0.9325	0.9794	0.9922	0.8505	0.9548	0.9861	0.7168	0.9138
0.0666	24.5455	540	0.0730	0.8678	0.9177	0.9786	0.9889	0.7913	0.9731	0.9849	0.7067	0.9118
0.0662	25.4545	560	0.0677	0.8743	0.9269	0.9795	0.9945	0.8365	0.9498	0.9856	0.7232	0.9142
0.0566	26.3636	580	0.0678	0.8737	0.9329	0.9794	0.9936	0.8561	0.9492	0.9859	0.7214	0.9138
0.0536	27.2727	600	0.0646	0.8745	0.9278	0.9800	0.9930	0.8320	0.9584	0.9866	0.7206	0.9164
0.0736	28.1818	620	0.0671	0.8657	0.9091	0.9790	0.9914	0.7651	0.9707	0.9856	0.6980	0.9134
0.0651	29.0909	640	0.0631	0.8717	0.9166	0.9800	0.9920	0.7889	0.9688	0.9864	0.7123	0.9166
0.0484	30.0	660	0.0627	0.8754	0.9213	0.9803	0.9926	0.8058	0.9655	0.9865	0.7217	0.9180
0.0527	30.9091	680	0.0620	0.8762	0.9316	0.9801	0.9912	0.8395	0.9639	0.9865	0.7251	0.9171
0.0592	31.8182	700	0.0646	0.8708	0.9337	0.9789	0.9947	0.8652	0.9411	0.9859	0.7160	0.9106
0.0591	32.7273	720	0.0608	0.8749	0.9204	0.9805	0.9929	0.8026	0.9656	0.9869	0.7196	0.9181
0.052	33.6364	740	0.0603	0.8773	0.9283	0.9803	0.9942	0.8360	0.9546	0.9865	0.7282	0.9174
0.0506	34.5455	760	0.0611	0.8758	0.9367	0.9799	0.9932	0.8654	0.9514	0.9867	0.7252	0.9155
0.0467	35.4545	780	0.0601	0.8771	0.9336	0.9803	0.9944	0.8560	0.9503	0.9868	0.7274	0.9170
0.0466	36.3636	800	0.0578	0.8779	0.9249	0.9810	0.9937	0.8186	0.9623	0.9873	0.7263	0.9202
0.0456	37.2727	820	0.0583	0.8757	0.9191	0.9806	0.9944	0.8020	0.9607	0.9868	0.7217	0.9185
0.0481	38.1818	840	0.0606	0.8642	0.9006	0.9798	0.9940	0.7383	0.9696	0.9869	0.6897	0.9159
0.0601	39.0909	860	0.0566	0.8743	0.9159	0.9808	0.9936	0.7863	0.9677	0.9874	0.7155	0.9198
0.0441	40.0	880	0.0568	0.8799	0.9306	0.9810	0.9934	0.8377	0.9608	0.9873	0.7319	0.9204
0.0416	40.9091	900	0.0569	0.8779	0.9313	0.9806	0.9948	0.8469	0.9520	0.9871	0.7286	0.9180
0.0489	41.8182	920	0.0557	0.8797	0.9288	0.9813	0.9940	0.8318	0.9605	0.9876	0.7300	0.9215
0.0477	42.7273	940	0.0564	0.8759	0.9191	0.9809	0.9935	0.7973	0.9666	0.9873	0.7204	0.9200
0.045	43.6364	960	0.0583	0.8774	0.9408	0.9801	0.9910	0.8721	0.9592	0.9868	0.7284	0.9171
0.0402	44.5455	980	0.0561	0.8795	0.9368	0.9808	0.9951	0.8664	0.9488	0.9874	0.7321	0.9190
0.0463	45.4545	1000	0.0557	0.8761	0.9182	0.9811	0.9928	0.7907	0.9711	0.9876	0.7198	0.9209
0.0356	46.3636	1020	0.0559	0.8793	0.9308	0.9810	0.9945	0.8421	0.9558	0.9874	0.7306	0.9199
0.0446	47.2727	1040	0.0569	0.8749	0.9249	0.9799	0.9963	0.8323	0.9461	0.9863	0.7233	0.9151
0.041	48.1818	1060	0.0561	0.8772	0.9299	0.9807	0.9931	0.8360	0.9606	0.9872	0.7246	0.9198
0.042	49.0909	1080	0.0543	0.8815	0.9339	0.9815	0.9931	0.8457	0.9630	0.9880	0.7339	0.9226
0.0502	50.0	1100	0.0557	0.8742	0.9168	0.9809	0.9937	0.7896	0.9671	0.9876	0.7148	0.9203
0.0403	50.9091	1120	0.0552	0.8771	0.9231	0.9810	0.9917	0.8061	0.9716	0.9873	0.7235	0.9205
0.0412	51.8182	1140	0.0544	0.8779	0.9203	0.9811	0.9951	0.8056	0.9603	0.9873	0.7257	0.9205
0.0426	52.7273	1160	0.0539	0.8797	0.9310	0.9811	0.9943	0.8420	0.9568	0.9874	0.7311	0.9204
0.0423	53.6364	1180	0.0538	0.8795	0.9291	0.9813	0.9940	0.8325	0.9610	0.9878	0.7289	0.9217
0.0302	54.5455	1200	0.0545	0.8778	0.9207	0.9812	0.9940	0.8030	0.9652	0.9875	0.7246	0.9213
0.0563	55.4545	1220	0.0546	0.8786	0.9303	0.9812	0.9935	0.8360	0.9613	0.9878	0.7267	0.9213
0.0462	56.3636	1240	0.0544	0.8758	0.9166	0.9811	0.9939	0.7887	0.9673	0.9874	0.7191	0.9210
0.0323	57.2727	1260	0.0549	0.8784	0.9242	0.9812	0.9924	0.8105	0.9697	0.9875	0.7261	0.9215
0.036	58.1818	1280	0.0528	0.8809	0.9357	0.9814	0.9939	0.8554	0.9577	0.9880	0.7326	0.9220
0.0368	59.0909	1300	0.0553	0.8770	0.9240	0.9809	0.9929	0.8131	0.9662	0.9872	0.7229	0.9208
0.0406	60.0	1320	0.0549	0.8726	0.9119	0.9809	0.9947	0.7753	0.9658	0.9875	0.7101	0.9203
0.0316	60.9091	1340	0.0545	0.8788	0.9244	0.9811	0.9942	0.8177	0.9612	0.9870	0.7280	0.9213
0.0343	61.8182	1360	0.0530	0.8808	0.9294	0.9815	0.9937	0.8317	0.9628	0.9877	0.7321	0.9225
0.0342	62.7273	1380	0.0535	0.8801	0.9315	0.9813	0.9934	0.8396	0.9614	0.9875	0.7306	0.9221
0.0515	63.6364	1400	0.0543	0.8794	0.9299	0.9812	0.9933	0.8340	0.9623	0.9873	0.7290	0.9219
0.0312	64.5455	1420	0.0533	0.8798	0.9312	0.9813	0.9944	0.8419	0.9573	0.9876	0.7302	0.9216
0.0359	65.4545	1440	0.0548	0.8785	0.9351	0.9809	0.9928	0.8527	0.9597	0.9874	0.7273	0.9207
0.0333	66.3636	1460	0.0521	0.8813	0.9276	0.9817	0.9948	0.8276	0.9604	0.9878	0.7327	0.9233
0.0358	67.2727	1480	0.0531	0.8797	0.9284	0.9814	0.9939	0.8292	0.9622	0.9878	0.7290	0.9221
0.0341	68.1818	1500	0.0528	0.8813	0.9319	0.9815	0.9937	0.8410	0.9611	0.9877	0.7336	0.9226
0.0316	69.0909	1520	0.0551	0.8763	0.9212	0.9810	0.9918	0.7992	0.9727	0.9875	0.7209	0.9206
0.0261	70.0	1540	0.0534	0.8803	0.9286	0.9815	0.9930	0.8270	0.9659	0.9877	0.7308	0.9225
0.0331	70.9091	1560	0.0551	0.8764	0.9190	0.9810	0.9927	0.7937	0.9705	0.9874	0.7210	0.9209
0.0398	71.8182	1580	0.0547	0.8758	0.9183	0.9811	0.9930	0.7917	0.9701	0.9876	0.7186	0.9212
0.0368	72.7273	1600	0.0524	0.8804	0.9280	0.9814	0.9943	0.8288	0.9609	0.9877	0.7312	0.9222
0.0368	73.6364	1620	0.0526	0.8811	0.9268	0.9817	0.9939	0.8217	0.9648	0.9879	0.7320	0.9234
0.0319	74.5455	1640	0.0528	0.8812	0.9337	0.9814	0.9934	0.8464	0.9612	0.9878	0.7335	0.9223
0.0321	75.4545	1660	0.0534	0.8802	0.9299	0.9813	0.9938	0.8349	0.9611	0.9876	0.7310	0.9221
0.0339	76.3636	1680	0.0539	0.8794	0.9283	0.9813	0.9927	0.8256	0.9667	0.9877	0.7285	0.9221
0.0289	77.2727	1700	0.0530	0.8795	0.9317	0.9813	0.9940	0.8421	0.9589	0.9878	0.7289	0.9218
0.0355	78.1818	1720	0.0540	0.8797	0.9348	0.9811	0.9940	0.8546	0.9559	0.9875	0.7307	0.9209
0.037	79.0909	1740	0.0531	0.8805	0.9360	0.9812	0.9932	0.8554	0.9594	0.9877	0.7320	0.9217
0.0354	80.0	1760	0.0530	0.8808	0.9319	0.9813	0.9946	0.8446	0.9566	0.9876	0.7334	0.9215
0.0335	80.9091	1780	0.0528	0.8805	0.9359	0.9813	0.9932	0.8549	0.9597	0.9878	0.7319	0.9217
0.0311	81.8182	1800	0.0529	0.8809	0.9361	0.9813	0.9933	0.8555	0.9597	0.9878	0.7328	0.9221
0.0395	82.7273	1820	0.0543	0.8772	0.9413	0.9805	0.9947	0.8838	0.9454	0.9875	0.7264	0.9176
0.0424	83.6364	1840	0.0540	0.8791	0.9403	0.9809	0.9934	0.8733	0.9541	0.9877	0.7294	0.9203
0.035	84.5455	1860	0.0522	0.8814	0.9275	0.9817	0.9942	0.8255	0.9629	0.9879	0.7331	0.9232
0.0418	85.4545	1880	0.0522	0.8803	0.9285	0.9815	0.9941	0.8297	0.9617	0.9879	0.7305	0.9225
0.0365	86.3636	1900	0.0536	0.8802	0.9378	0.9812	0.9935	0.8631	0.9569	0.9878	0.7315	0.9213
0.0344	87.2727	1920	0.0532	0.8809	0.9364	0.9812	0.9934	0.8580	0.9579	0.9875	0.7341	0.9211
0.0257	88.1818	1940	0.0540	0.8775	0.9189	0.9814	0.9932	0.7925	0.9710	0.9879	0.7223	0.9223
0.0333	89.0909	1960	0.0534	0.8797	0.9253	0.9814	0.9926	0.8140	0.9693	0.9876	0.7289	0.9226
0.0407	90.0	1980	0.0531	0.8818	0.9335	0.9814	0.9935	0.8463	0.9608	0.9876	0.7354	0.9225
0.031	90.9091	2000	0.0529	0.8822	0.9345	0.9815	0.9935	0.8496	0.9604	0.9876	0.7366	0.9224
0.0371	91.8182	2020	0.0525	0.8810	0.9316	0.9815	0.9934	0.8388	0.9628	0.9878	0.7325	0.9226
0.0418	92.7273	2040	0.0526	0.8809	0.9302	0.9815	0.9932	0.8329	0.9644	0.9878	0.7321	0.9229
0.0337	93.6364	2060	0.0530	0.8808	0.9279	0.9815	0.9936	0.8259	0.9641	0.9877	0.7318	0.9228
0.0336	94.5455	2080	0.0525	0.8810	0.9289	0.9815	0.9944	0.8320	0.9602	0.9877	0.7331	0.9222
0.0343	95.4545	2100	0.0527	0.8811	0.9285	0.9816	0.9937	0.8279	0.9639	0.9878	0.7323	0.9233
0.0337	96.3636	2120	0.0535	0.8809	0.9311	0.9815	0.9933	0.8367	0.9633	0.9877	0.7322	0.9228
0.0297	97.2727	2140	0.0527	0.8804	0.9292	0.9815	0.9937	0.8312	0.9627	0.9877	0.7308	0.9228
0.0302	98.1818	2160	0.0529	0.8790	0.9227	0.9815	0.9941	0.8084	0.9655	0.9878	0.7265	0.9228
0.037	99.0909	2180	0.0534	0.8814	0.9331	0.9815	0.9936	0.8448	0.9610	0.9877	0.7338	0.9226
0.0591	100.0	2200	0.0520	0.8812	0.9305	0.9816	0.9940	0.8364	0.9611	0.9878	0.7330	0.9229
0.0294	100.9091	2220	0.0538	0.8792	0.9235	0.9815	0.9932	0.8091	0.9682	0.9877	0.7270	0.9228
0.0301	101.8182	2240	0.0530	0.8817	0.9333	0.9815	0.9930	0.8434	0.9634	0.9877	0.7347	0.9227
0.0286	102.7273	2260	0.0533	0.8814	0.9297	0.9816	0.9933	0.8310	0.9647	0.9877	0.7334	0.9230
0.0332	103.6364	2280	0.0523	0.8823	0.9335	0.9816	0.9942	0.8474	0.9589	0.9878	0.7360	0.9229
0.0329	104.5455	2300	0.0526	0.8820	0.9324	0.9816	0.9942	0.8436	0.9593	0.9878	0.7354	0.9228
0.0295	105.4545	2320	0.0528	0.8816	0.9326	0.9814	0.9945	0.8461	0.9571	0.9877	0.7353	0.9220
0.0288	106.3636	2340	0.0526	0.8813	0.9288	0.9816	0.9939	0.8295	0.9629	0.9878	0.7330	0.9230
0.0352	107.2727	2360	0.0527	0.8814	0.9309	0.9816	0.9937	0.8365	0.9625	0.9878	0.7334	0.9230
0.0334	108.1818	2380	0.0526	0.8816	0.9315	0.9816	0.9944	0.8407	0.9592	0.9879	0.7342	0.9229
0.033	109.0909	2400	0.0520	0.8817	0.9300	0.9817	0.9937	0.8328	0.9635	0.9880	0.7337	0.9235
0.0267	110.0	2420	0.0523	0.8814	0.9296	0.9816	0.9941	0.8328	0.9619	0.9879	0.7332	0.9232
0.0281	110.9091	2440	0.0527	0.8817	0.9318	0.9816	0.9939	0.8406	0.9609	0.9878	0.7346	0.9228
0.0329	111.8182	2460	0.0530	0.8816	0.9302	0.9816	0.9941	0.8353	0.9613	0.9877	0.7342	0.9229
0.0374	112.7273	2480	0.0530	0.8812	0.9300	0.9815	0.9936	0.8333	0.9631	0.9877	0.7330	0.9228
0.0298	113.6364	2500	0.0530	0.8811	0.9290	0.9815	0.9941	0.8314	0.9617	0.9877	0.7330	0.9227
0.028	114.5455	2520	0.0529	0.8812	0.9286	0.9816	0.9936	0.8280	0.9642	0.9878	0.7327	0.9231
0.0292	115.4545	2540	0.0535	0.8811	0.9293	0.9815	0.9932	0.8295	0.9651	0.9877	0.7327	0.9230
0.0326	116.3636	2560	0.0530	0.8814	0.9305	0.9815	0.9939	0.8361	0.9616	0.9877	0.7339	0.9228
0.0312	117.2727	2580	0.0532	0.8814	0.9306	0.9815	0.9937	0.8359	0.9623	0.9877	0.7338	0.9227
0.0292	118.1818	2600	0.0533	0.8811	0.9291	0.9815	0.9937	0.8303	0.9633	0.9877	0.7328	0.9228
0.0287	119.0909	2620	0.0533	0.8812	0.9306	0.9815	0.9937	0.8362	0.9619	0.9877	0.7333	0.9226
0.0352	120.0	2640	0.0528	0.8813	0.9318	0.9815	0.9940	0.8416	0.9598	0.9877	0.7339	0.9224

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

mujerry/mit-b0_necrosis

mit-b0_necrosis

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mujerry/
mit-b0_necrosis