mit-b0_corm

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0433
• Mean Iou: 0.9210
• Mean Accuracy: 0.9571
• Overall Accuracy: 0.9853
• Accuracy Background: 0.9977
• Accuracy Corm: 0.9360
• Accuracy Damage: 0.9377
• Iou Background: 0.9944
• Iou Corm: 0.8762
• Iou Damage: 0.8923

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: 6e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Corm	Accuracy Damage	Iou Background	Iou Corm	Iou Damage
0.933	0.6061	20	1.0299	0.3591	0.6054	0.6910	0.7236	0.1098	0.9827	0.7236	0.0867	0.2671
0.6505	1.2121	40	0.6909	0.6522	0.8240	0.9013	0.9328	0.5651	0.9740	0.9328	0.4509	0.5728
0.4133	1.8182	60	0.4184	0.7567	0.8872	0.9394	0.9609	0.7307	0.9701	0.9607	0.6218	0.6875
0.3299	2.4242	80	0.3451	0.8351	0.9306	0.9617	0.9751	0.8924	0.9243	0.9748	0.7569	0.7735
0.2594	3.0303	100	0.2506	0.8703	0.9412	0.9727	0.9862	0.8989	0.9384	0.9852	0.8019	0.8237
0.2253	3.6364	120	0.2006	0.8851	0.9403	0.9779	0.9939	0.8672	0.9599	0.9915	0.8207	0.8430
0.2222	4.2424	140	0.1654	0.8990	0.9490	0.9805	0.9946	0.9446	0.9079	0.9920	0.8438	0.8612
0.1347	4.8485	160	0.1413	0.9048	0.9508	0.9819	0.9956	0.9334	0.9234	0.9928	0.8526	0.8689
0.1366	5.4545	180	0.1155	0.9094	0.9516	0.9829	0.9966	0.9258	0.9325	0.9933	0.8583	0.8765
0.1121	6.0606	200	0.1086	0.8938	0.9447	0.9801	0.9961	0.9628	0.8753	0.9933	0.8392	0.8487
0.0982	6.6667	220	0.0963	0.9115	0.9524	0.9835	0.9972	0.9374	0.9227	0.9938	0.8626	0.8780
0.0993	7.2727	240	0.0892	0.9094	0.9513	0.9832	0.9968	0.9001	0.9571	0.9940	0.8570	0.8773
0.0813	7.8788	260	0.0842	0.9127	0.9543	0.9837	0.9966	0.9380	0.9281	0.9939	0.8643	0.8798
0.1059	8.4848	280	0.0774	0.9152	0.9541	0.9842	0.9973	0.9258	0.9391	0.9940	0.8673	0.8843
0.082	9.0909	300	0.0729	0.9159	0.9541	0.9843	0.9975	0.9294	0.9355	0.9940	0.8681	0.8854
0.0725	9.6970	320	0.0692	0.9162	0.9544	0.9844	0.9975	0.9247	0.9411	0.9941	0.8686	0.8861
0.0814	10.3030	340	0.0687	0.9161	0.9541	0.9844	0.9975	0.9155	0.9492	0.9942	0.8675	0.8865
0.076	10.9091	360	0.0640	0.9157	0.9555	0.9843	0.9968	0.9219	0.9479	0.9941	0.8680	0.8849
0.07	11.5152	380	0.0633	0.9166	0.9553	0.9845	0.9973	0.9375	0.9310	0.9941	0.8698	0.8859
0.0674	12.1212	400	0.0611	0.9176	0.9549	0.9847	0.9977	0.9217	0.9453	0.9943	0.8704	0.8881
0.0638	12.7273	420	0.0601	0.9116	0.9522	0.9836	0.9977	0.9529	0.9059	0.9941	0.8641	0.8768
0.0566	13.3333	440	0.0582	0.9176	0.9561	0.9847	0.9972	0.9322	0.9387	0.9943	0.8714	0.8872
0.0582	13.9394	460	0.0614	0.9077	0.9502	0.9829	0.9976	0.9583	0.8948	0.9941	0.8588	0.8700
0.0555	14.5455	480	0.0561	0.9146	0.9534	0.9841	0.9978	0.9481	0.9142	0.9941	0.8679	0.8817
0.053	15.1515	500	0.0540	0.9182	0.9551	0.9848	0.9977	0.9185	0.9492	0.9943	0.8707	0.8895
0.059	15.7576	520	0.0549	0.9180	0.9565	0.9848	0.9970	0.9248	0.9478	0.9943	0.8711	0.8887
0.0484	16.3636	540	0.0529	0.9177	0.9563	0.9847	0.9973	0.9405	0.9311	0.9943	0.8721	0.8866
0.0559	16.9697	560	0.0510	0.9192	0.9565	0.9850	0.9974	0.9268	0.9453	0.9943	0.8729	0.8904
0.0542	17.5758	580	0.0512	0.9190	0.9569	0.9850	0.9973	0.9351	0.9382	0.9944	0.8733	0.8894
0.0451	18.1818	600	0.0505	0.9184	0.9557	0.9848	0.9977	0.9428	0.9265	0.9943	0.8729	0.8880
0.05	18.7879	620	0.0499	0.9178	0.9542	0.9848	0.9979	0.9098	0.9549	0.9943	0.8691	0.8899
0.063	19.3939	640	0.0491	0.9190	0.9560	0.9850	0.9975	0.9221	0.9483	0.9943	0.8723	0.8904
0.0484	20.0	660	0.0501	0.9185	0.9569	0.9849	0.9972	0.9427	0.9308	0.9944	0.8732	0.8880
0.0527	20.6061	680	0.0492	0.9186	0.9561	0.9849	0.9976	0.9430	0.9276	0.9943	0.8732	0.8884
0.0583	21.2121	700	0.0476	0.9195	0.9563	0.9851	0.9976	0.9208	0.9506	0.9944	0.8730	0.8911
0.0557	21.8182	720	0.0488	0.9188	0.9565	0.9850	0.9973	0.9191	0.9531	0.9945	0.8723	0.8896
0.0458	22.4242	740	0.0481	0.9194	0.9568	0.9851	0.9973	0.9242	0.9489	0.9944	0.8729	0.8909
0.042	23.0303	760	0.0472	0.9202	0.9570	0.9852	0.9975	0.9326	0.9409	0.9944	0.8749	0.8911
0.0459	23.6364	780	0.0468	0.9191	0.9565	0.9850	0.9976	0.9423	0.9295	0.9944	0.8740	0.8889
0.0491	24.2424	800	0.0464	0.9204	0.9568	0.9852	0.9977	0.9361	0.9366	0.9944	0.8753	0.8914
0.0548	24.8485	820	0.0454	0.9201	0.9565	0.9852	0.9976	0.9244	0.9475	0.9944	0.8740	0.8917
0.0447	25.4545	840	0.0473	0.9176	0.9558	0.9847	0.9976	0.9477	0.9222	0.9944	0.8723	0.8863
0.0457	26.0606	860	0.0468	0.9203	0.9567	0.9852	0.9976	0.9270	0.9456	0.9944	0.8745	0.8922
0.0468	26.6667	880	0.0454	0.9201	0.9572	0.9852	0.9974	0.9403	0.9341	0.9944	0.8753	0.8905
0.0433	27.2727	900	0.0452	0.9208	0.9563	0.9853	0.9980	0.9339	0.9371	0.9943	0.8759	0.8923
0.0438	27.8788	920	0.0452	0.9208	0.9574	0.9853	0.9975	0.9352	0.9396	0.9944	0.8760	0.8920
0.0446	28.4848	940	0.0447	0.9210	0.9568	0.9853	0.9978	0.9349	0.9377	0.9943	0.8760	0.8926
0.0492	29.0909	960	0.0452	0.9211	0.9568	0.9853	0.9978	0.9352	0.9374	0.9943	0.8762	0.8928
0.0481	29.6970	980	0.0456	0.9195	0.9567	0.9851	0.9976	0.9443	0.9283	0.9944	0.8747	0.8893
0.0405	30.3030	1000	0.0447	0.9206	0.9574	0.9853	0.9975	0.9391	0.9355	0.9944	0.8758	0.8916
0.0505	30.9091	1020	0.0443	0.9210	0.9570	0.9853	0.9978	0.9370	0.9364	0.9944	0.8763	0.8923
0.047	31.5152	1040	0.0450	0.9204	0.9568	0.9853	0.9976	0.9223	0.9505	0.9945	0.8744	0.8923
0.0548	32.1212	1060	0.0452	0.9192	0.9561	0.9850	0.9978	0.9442	0.9261	0.9944	0.8744	0.8889
0.0445	32.7273	1080	0.0442	0.9208	0.9573	0.9853	0.9975	0.9320	0.9426	0.9944	0.8758	0.8921
0.0539	33.3333	1100	0.0435	0.9208	0.9571	0.9853	0.9976	0.9359	0.9379	0.9944	0.8758	0.8921
0.0383	33.9394	1120	0.0459	0.9171	0.9549	0.9846	0.9979	0.9493	0.9175	0.9943	0.8716	0.8853
0.0478	34.5455	1140	0.0443	0.9203	0.9572	0.9852	0.9974	0.9246	0.9496	0.9945	0.8748	0.8916
0.0432	35.1515	1160	0.0442	0.9210	0.9571	0.9853	0.9977	0.9349	0.9388	0.9944	0.8762	0.8924
0.0468	35.7576	1180	0.0439	0.9208	0.9572	0.9853	0.9976	0.9371	0.9368	0.9944	0.8761	0.8919
0.0475	36.3636	1200	0.0443	0.9209	0.9571	0.9853	0.9977	0.9371	0.9364	0.9944	0.8762	0.8921
0.0388	36.9697	1220	0.0436	0.9208	0.9573	0.9853	0.9976	0.9371	0.9373	0.9944	0.8761	0.8919
0.0468	37.5758	1240	0.0431	0.9208	0.9574	0.9853	0.9975	0.9343	0.9405	0.9944	0.8760	0.8921
0.0426	38.1818	1260	0.0445	0.9205	0.9570	0.9852	0.9977	0.9415	0.9318	0.9944	0.8758	0.8912
0.0549	38.7879	1280	0.0436	0.9209	0.9571	0.9853	0.9977	0.9373	0.9362	0.9944	0.8761	0.8921
0.045	39.3939	1300	0.0438	0.9208	0.9573	0.9853	0.9976	0.9381	0.9362	0.9944	0.8760	0.8919
0.0287	40.0	1320	0.0433	0.9210	0.9571	0.9853	0.9977	0.9360	0.9377	0.9944	0.8762	0.8923

Framework versions

• Transformers 4.44.1
• Pytorch 2.6.0+cpu
• Datasets 2.21.0
• Tokenizers 0.19.1