You can deploy this model and start earning money today!

mujerry/
segformer-b4-finetuned-ade-512-512_corm

Name: segformer-b4-finetuned-ade-512-512_corm
Availability: InStock

generated_from_trainer

nvidia/segformer-b4-finetuned-ade-512-512

finetune

other

endpoints_compatible

Views

No views yet

segformer-b4-finetuned-ade-512-512_corm

This model is a fine-tuned version of nvidia/segformer-b4-finetuned-ade-512-512 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0391
Mean Iou: 0.9261
Mean Accuracy: 0.9594
Overall Accuracy: 0.9863
Accuracy Background: 0.9977
Accuracy Corm: 0.9268
Accuracy Damage: 0.9537
Iou Background: 0.9944
Iou Corm: 0.8758
Iou Damage: 0.9082

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: 50

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
1.0242	0.6061	20	1.0187	0.3316	0.6012	0.5573	0.5345	0.5453	0.7237	0.5341	0.1224	0.3383
0.7686	1.2121	40	0.6913	0.7059	0.8624	0.9170	0.9369	0.7070	0.9434	0.9369	0.4718	0.7090
0.5281	1.8182	60	0.4782	0.8060	0.9165	0.9537	0.9697	0.8764	0.9032	0.9696	0.6651	0.7833
0.3931	2.4242	80	0.3279	0.8530	0.9308	0.9690	0.9843	0.8578	0.9503	0.9837	0.7547	0.8206
0.2574	3.0303	100	0.2112	0.8733	0.9335	0.9753	0.9915	0.8406	0.9685	0.9899	0.7898	0.8402
0.2112	3.6364	120	0.1588	0.8990	0.9450	0.9807	0.9952	0.8824	0.9576	0.9918	0.8337	0.8716
0.1545	4.2424	140	0.1198	0.8960	0.9398	0.9805	0.9965	0.8539	0.9690	0.9924	0.8245	0.8711
0.1127	4.8485	160	0.1152	0.8851	0.9395	0.9782	0.9973	0.9609	0.8604	0.9923	0.8191	0.8440
0.1147	5.4545	180	0.0862	0.9130	0.9546	0.9834	0.9956	0.9170	0.9513	0.9930	0.8579	0.8881
0.0945	6.0606	200	0.0793	0.9083	0.9457	0.9829	0.9977	0.8728	0.9667	0.9929	0.8437	0.8881
0.0942	6.6667	220	0.0730	0.9170	0.9519	0.9842	0.9985	0.9263	0.9309	0.9926	0.8624	0.8960
0.0766	7.2727	240	0.0675	0.9185	0.9565	0.9847	0.9972	0.9365	0.9358	0.9936	0.8656	0.8963
0.0674	7.8788	260	0.0635	0.9160	0.9523	0.9844	0.9972	0.8897	0.9700	0.9937	0.8585	0.8957
0.0662	8.4848	280	0.0593	0.9199	0.9520	0.9849	0.9985	0.8984	0.9590	0.9931	0.8637	0.9030
0.0683	9.0909	300	0.0582	0.9176	0.9516	0.9847	0.9981	0.8914	0.9652	0.9937	0.8598	0.8994
0.0591	9.6970	320	0.0548	0.9222	0.9565	0.9855	0.9974	0.9076	0.9644	0.9941	0.8693	0.9031
0.0649	10.3030	340	0.0541	0.9201	0.9553	0.9849	0.9984	0.9407	0.9269	0.9935	0.8685	0.8982
0.0622	10.9091	360	0.0525	0.9155	0.9497	0.9844	0.9982	0.8803	0.9707	0.9938	0.8551	0.8976
0.0637	11.5152	380	0.0542	0.9124	0.9466	0.9838	0.9986	0.8720	0.9692	0.9933	0.8491	0.8948
0.0647	12.1212	400	0.0486	0.9244	0.9562	0.9857	0.9988	0.9340	0.9358	0.9933	0.8738	0.9060
0.0418	12.7273	420	0.0466	0.9267	0.9589	0.9862	0.9980	0.9288	0.9500	0.9939	0.8773	0.9088
0.0459	13.3333	440	0.0460	0.9260	0.9601	0.9862	0.9977	0.9378	0.9448	0.9942	0.8770	0.9069
0.048	13.9394	460	0.0453	0.9253	0.9586	0.9861	0.9981	0.9335	0.9443	0.9941	0.8751	0.9067
0.0397	14.5455	480	0.0446	0.9263	0.9589	0.9863	0.9978	0.9219	0.9569	0.9943	0.8759	0.9088
0.0546	15.1515	500	0.0457	0.9219	0.9572	0.9854	0.9983	0.9447	0.9285	0.9940	0.8707	0.9010
0.0427	15.7576	520	0.0432	0.9267	0.9611	0.9863	0.9973	0.9374	0.9485	0.9943	0.8774	0.9084
0.0463	16.3636	540	0.0424	0.9263	0.9576	0.9863	0.9982	0.9148	0.9598	0.9941	0.8750	0.9098
0.048	16.9697	560	0.0421	0.9272	0.9588	0.9865	0.9981	0.9229	0.9555	0.9942	0.8776	0.9099
0.0534	17.5758	580	0.0420	0.9269	0.9610	0.9863	0.9975	0.9393	0.9460	0.9943	0.8777	0.9085
0.0411	18.1818	600	0.0420	0.9249	0.9581	0.9861	0.9976	0.9133	0.9634	0.9944	0.8734	0.9068
0.0417	18.7879	620	0.0425	0.9231	0.9573	0.9858	0.9973	0.9042	0.9705	0.9945	0.8699	0.9048
0.0368	19.3939	640	0.0405	0.9283	0.9603	0.9867	0.9979	0.9281	0.9548	0.9944	0.8795	0.9111
0.048	20.0	660	0.0399	0.9279	0.9608	0.9866	0.9977	0.9325	0.9522	0.9944	0.8793	0.9100
0.0363	20.6061	680	0.0415	0.9255	0.9593	0.9861	0.9981	0.9421	0.9378	0.9943	0.8761	0.9061
0.0459	21.2121	700	0.0421	0.9243	0.9583	0.9858	0.9984	0.9471	0.9294	0.9940	0.8748	0.9042
0.0436	21.8182	720	0.0403	0.9269	0.9610	0.9863	0.9976	0.9426	0.9429	0.9943	0.8782	0.9080
0.0461	22.4242	740	0.0406	0.9260	0.9615	0.9862	0.9974	0.9476	0.9396	0.9945	0.8771	0.9065
0.0319	23.0303	760	0.0395	0.9269	0.9614	0.9864	0.9970	0.9306	0.9567	0.9944	0.8780	0.9082
0.0366	23.6364	780	0.0392	0.9277	0.9607	0.9866	0.9978	0.9352	0.9492	0.9944	0.8793	0.9095
0.0351	24.2424	800	0.0390	0.9282	0.9605	0.9866	0.9979	0.9338	0.9497	0.9943	0.8796	0.9106
0.0322	24.8485	820	0.0388	0.9280	0.9600	0.9866	0.9980	0.9289	0.9531	0.9944	0.8790	0.9108
0.0346	25.4545	840	0.0392	0.9266	0.9595	0.9864	0.9976	0.9173	0.9635	0.9944	0.8760	0.9095
0.0342	26.0606	860	0.0398	0.9243	0.9575	0.9861	0.9976	0.9059	0.9691	0.9945	0.8715	0.9070
0.0389	26.6667	880	0.0387	0.9275	0.9617	0.9865	0.9970	0.9272	0.9609	0.9945	0.8793	0.9087
0.033	27.2727	900	0.0392	0.9272	0.9596	0.9865	0.9976	0.9168	0.9645	0.9944	0.8772	0.9099
0.0316	27.8788	920	0.0388	0.9269	0.9602	0.9865	0.9973	0.9196	0.9636	0.9945	0.8768	0.9095
0.0391	28.4848	940	0.0396	0.9262	0.9604	0.9863	0.9970	0.9200	0.9642	0.9945	0.8760	0.9082
0.0305	29.0909	960	0.0386	0.9275	0.9600	0.9865	0.9978	0.9241	0.9580	0.9945	0.8780	0.9101
0.034	29.6970	980	0.0392	0.9267	0.9600	0.9863	0.9980	0.9399	0.9421	0.9943	0.8777	0.9081
0.0322	30.3030	1000	0.0383	0.9275	0.9607	0.9865	0.9976	0.9321	0.9524	0.9945	0.8786	0.9094
0.0288	30.9091	1020	0.0389	0.9271	0.9606	0.9864	0.9976	0.9339	0.9504	0.9944	0.8782	0.9087
0.0324	31.5152	1040	0.0394	0.9265	0.9601	0.9863	0.9978	0.9362	0.9462	0.9944	0.8770	0.9080
0.0329	32.1212	1060	0.0399	0.9259	0.9599	0.9862	0.9980	0.9421	0.9396	0.9943	0.8767	0.9068
0.0211	32.7273	1080	0.0390	0.9268	0.9593	0.9864	0.9981	0.9310	0.9490	0.9943	0.8773	0.9087
0.0227	33.3333	1100	0.0389	0.9269	0.9581	0.9864	0.9983	0.9194	0.9565	0.9941	0.8764	0.9101
0.0328	33.9394	1120	0.0391	0.9270	0.9587	0.9864	0.9983	0.9284	0.9494	0.9941	0.8773	0.9096
0.0297	34.5455	1140	0.0389	0.9267	0.9597	0.9864	0.9979	0.9304	0.9509	0.9944	0.8771	0.9087
0.0346	35.1515	1160	0.0390	0.9267	0.9595	0.9864	0.9979	0.9292	0.9516	0.9943	0.8769	0.9088
0.0231	35.7576	1180	0.0391	0.9266	0.9587	0.9863	0.9981	0.9232	0.9547	0.9942	0.8764	0.9093
0.0301	36.3636	1200	0.0387	0.9267	0.9594	0.9864	0.9978	0.9232	0.9572	0.9944	0.8764	0.9093
0.0331	36.9697	1220	0.0388	0.9269	0.9597	0.9864	0.9979	0.9290	0.9522	0.9943	0.8772	0.9091
0.0281	37.5758	1240	0.0389	0.9268	0.9589	0.9864	0.9981	0.9266	0.9520	0.9943	0.8769	0.9093
0.0208	38.1818	1260	0.0390	0.9266	0.9605	0.9863	0.9975	0.9318	0.9523	0.9944	0.8768	0.9086
0.0348	38.7879	1280	0.0397	0.9257	0.9598	0.9862	0.9978	0.9387	0.9429	0.9943	0.8760	0.9068
0.0276	39.3939	1300	0.0388	0.9269	0.9590	0.9864	0.9981	0.9267	0.9522	0.9942	0.8772	0.9093
0.0286	40.0	1320	0.0395	0.9248	0.9572	0.9861	0.9979	0.9082	0.9655	0.9944	0.8723	0.9076
0.0298	40.6061	1340	0.0391	0.9263	0.9592	0.9863	0.9977	0.9212	0.9587	0.9944	0.8759	0.9087
0.0235	41.2121	1360	0.0389	0.9262	0.9590	0.9863	0.9978	0.9220	0.9574	0.9944	0.8757	0.9085
0.0223	41.8182	1380	0.0392	0.9265	0.9593	0.9863	0.9979	0.9273	0.9528	0.9943	0.8765	0.9088
0.0216	42.4242	1400	0.0390	0.9264	0.9592	0.9863	0.9979	0.9265	0.9531	0.9943	0.8761	0.9086
0.027	43.0303	1420	0.0395	0.9264	0.9601	0.9863	0.9976	0.9315	0.9511	0.9944	0.8764	0.9083
0.0261	43.6364	1440	0.0392	0.9265	0.9593	0.9863	0.9980	0.9309	0.9491	0.9943	0.8768	0.9083
0.0213	44.2424	1460	0.0390	0.9262	0.9592	0.9863	0.9977	0.9213	0.9584	0.9944	0.8756	0.9087
0.0228	44.8485	1480	0.0390	0.9260	0.9596	0.9863	0.9976	0.9240	0.9571	0.9944	0.8756	0.9081
0.0317	45.4545	1500	0.0391	0.9259	0.9584	0.9862	0.9980	0.9227	0.9544	0.9942	0.8751	0.9082
0.0244	46.0606	1520	0.0392	0.9261	0.9591	0.9863	0.9979	0.9274	0.9521	0.9943	0.8759	0.9081
0.0282	46.6667	1540	0.0391	0.9259	0.9589	0.9862	0.9978	0.9224	0.9564	0.9944	0.8750	0.9082
0.0244	47.2727	1560	0.0397	0.9262	0.9592	0.9863	0.9979	0.9280	0.9519	0.9944	0.8760	0.9081
0.0265	47.8788	1580	0.0393	0.9258	0.9592	0.9862	0.9977	0.9226	0.9572	0.9944	0.8751	0.9080
0.0282	48.4848	1600	0.0394	0.9260	0.9585	0.9863	0.9980	0.9209	0.9567	0.9943	0.8752	0.9084
0.0229	49.0909	1620	0.0390	0.9262	0.9592	0.9863	0.9979	0.9281	0.9517	0.9943	0.8760	0.9083
0.0236	49.6970	1640	0.0391	0.9261	0.9594	0.9863	0.9977	0.9268	0.9537	0.9944	0.8758	0.9082

Framework versions

Transformers 4.44.1
Pytorch 2.6.0+cpu
Datasets 2.21.0
Tokenizers 0.19.1

mujerry/segformer-b4-finetuned-ade-512-512_corm

segformer-b4-finetuned-ade-512-512_corm

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mujerry/
segformer-b4-finetuned-ade-512-512_corm