segformer-b2-finetuned-ade-512-512_corm

This model is a fine-tuned version of nvidia/segformer-b2-finetuned-ade-512-512 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0415
• Mean Iou: 0.9264
• Mean Accuracy: 0.9599
• Overall Accuracy: 0.9860
• Accuracy Background: 0.9978
• Accuracy Corm: 0.9362
• Accuracy Damage: 0.9456
• Iou Background: 0.9942
• Iou Corm: 0.8799
• Iou Damage: 0.9052

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
0.8746	0.9524	20	0.8170	0.4637	0.6489	0.8404	0.9133	0.0390	0.9944	0.9132	0.0327	0.4451
0.61	1.9048	40	0.4500	0.7608	0.8748	0.9451	0.9731	0.6946	0.9566	0.9730	0.6023	0.7071
0.3681	2.8571	60	0.2802	0.8597	0.9314	0.9711	0.9879	0.8621	0.9443	0.9873	0.7716	0.8201
0.2433	3.8095	80	0.2201	0.8866	0.9456	0.9774	0.9916	0.9159	0.9293	0.9904	0.8181	0.8513
0.1669	4.7619	100	0.1607	0.8891	0.9431	0.9783	0.9930	0.8723	0.9640	0.9915	0.8178	0.8580
0.1484	5.7143	120	0.1250	0.9017	0.9489	0.9812	0.9964	0.9423	0.9080	0.9932	0.8432	0.8688
0.1126	6.6667	140	0.1024	0.9092	0.9524	0.9827	0.9962	0.9247	0.9363	0.9934	0.8539	0.8804
0.0909	7.6190	160	0.0932	0.9017	0.9492	0.9813	0.9968	0.9563	0.8944	0.9935	0.8445	0.8670
0.0994	8.5714	180	0.0803	0.9122	0.9527	0.9833	0.9967	0.9118	0.9495	0.9936	0.8568	0.8861
0.0768	9.5238	200	0.0716	0.9147	0.9533	0.9838	0.9975	0.9247	0.9376	0.9937	0.8615	0.8889
0.0749	10.4762	220	0.0671	0.9177	0.9550	0.9844	0.9973	0.9191	0.9487	0.9939	0.8661	0.8932
0.0663	11.4286	240	0.0668	0.9097	0.9528	0.9829	0.9973	0.9528	0.9083	0.9939	0.8558	0.8795
0.0725	12.3810	260	0.0608	0.9189	0.9554	0.9847	0.9974	0.9123	0.9564	0.9940	0.8677	0.8951
0.0594	13.3333	280	0.0588	0.9167	0.9533	0.9843	0.9975	0.9000	0.9625	0.9940	0.8622	0.8940
0.062	14.2857	300	0.0552	0.9201	0.9565	0.9849	0.9972	0.9170	0.9553	0.9941	0.8691	0.8970
0.0535	15.2381	320	0.0543	0.9195	0.9559	0.9848	0.9972	0.9078	0.9626	0.9942	0.8683	0.8962
0.0555	16.1905	340	0.0517	0.9212	0.9566	0.9851	0.9973	0.9113	0.9612	0.9942	0.8704	0.8990
0.0553	17.1429	360	0.0513	0.9198	0.9553	0.9849	0.9975	0.9047	0.9638	0.9942	0.8679	0.8974
0.0572	18.0952	380	0.0501	0.9219	0.9563	0.9853	0.9977	0.9108	0.9603	0.9942	0.8713	0.9002
0.0503	19.0476	400	0.0483	0.9245	0.9573	0.9856	0.9981	0.9212	0.9525	0.9940	0.8757	0.9037
0.0539	20.0	420	0.0474	0.9245	0.9593	0.9857	0.9974	0.9309	0.9497	0.9942	0.8769	0.9024
0.0542	20.9524	440	0.0484	0.9202	0.9575	0.9849	0.9978	0.9511	0.9235	0.9941	0.8718	0.8949
0.033	21.9048	460	0.0478	0.9209	0.9576	0.9850	0.9977	0.9464	0.9287	0.9941	0.8726	0.8961
0.0421	22.8571	480	0.0452	0.9247	0.9591	0.9857	0.9974	0.9244	0.9555	0.9942	0.8766	0.9033
0.0472	23.8095	500	0.0455	0.9243	0.9583	0.9857	0.9976	0.9231	0.9543	0.9942	0.8759	0.9028
0.0381	24.7619	520	0.0456	0.9233	0.9570	0.9855	0.9977	0.9109	0.9625	0.9942	0.8732	0.9026
0.0486	25.7143	540	0.0444	0.9249	0.9593	0.9857	0.9978	0.9408	0.9394	0.9941	0.8780	0.9026
0.0501	26.6667	560	0.0458	0.9208	0.9579	0.9850	0.9977	0.9508	0.9252	0.9942	0.8725	0.8957
0.0343	27.6190	580	0.0436	0.9251	0.9594	0.9857	0.9978	0.9413	0.9391	0.9941	0.8782	0.9031
0.0407	28.5714	600	0.0434	0.9251	0.9597	0.9858	0.9977	0.9416	0.9396	0.9942	0.8784	0.9028
0.0419	29.5238	620	0.0445	0.9221	0.9586	0.9852	0.9977	0.9496	0.9285	0.9942	0.8743	0.8978
0.0506	30.4762	640	0.0425	0.9262	0.9593	0.9860	0.9978	0.9311	0.9491	0.9942	0.8791	0.9053
0.0422	31.4286	660	0.0424	0.9262	0.9595	0.9860	0.9977	0.9267	0.9540	0.9942	0.8790	0.9054
0.0362	32.3810	680	0.0425	0.9258	0.9600	0.9859	0.9977	0.9402	0.9421	0.9942	0.8793	0.9039
0.0437	33.3333	700	0.0424	0.9262	0.9599	0.9860	0.9978	0.9377	0.9441	0.9942	0.8796	0.9047
0.0363	34.2857	720	0.0415	0.9264	0.9602	0.9860	0.9976	0.9367	0.9463	0.9942	0.8800	0.9049
0.039	35.2381	740	0.0421	0.9267	0.9596	0.9861	0.9978	0.9290	0.9521	0.9942	0.8798	0.9060
0.0425	36.1905	760	0.0418	0.9259	0.9598	0.9859	0.9978	0.9391	0.9426	0.9942	0.8794	0.9040
0.0462	37.1429	780	0.0417	0.9267	0.9600	0.9861	0.9976	0.9311	0.9513	0.9942	0.8801	0.9057
0.0466	38.0952	800	0.0416	0.9261	0.9599	0.9860	0.9978	0.9392	0.9427	0.9942	0.8795	0.9045
0.0428	39.0476	820	0.0414	0.9266	0.9598	0.9861	0.9978	0.9323	0.9494	0.9942	0.8800	0.9057
0.04	40.0	840	0.0415	0.9264	0.9599	0.9860	0.9978	0.9362	0.9456	0.9942	0.8799	0.9052

Framework versions

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