ModelListedExternal

Segformer B0 FT Uff Roi Segmentation All5 Vc1 Pc0 Cw1 E50 260717 2234

This model is a fine-tuned version of nvidia/mit-b0 on the LeninGF/uff-thermography-multiview-segmentation dataset. It achieves the following results on the evaluation set: Loss: 0.0878 Mean Iou: 0.9213 Mean...

Sourcehuggingface.co/LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234/tree/26de674b4e1e5600d1bdfab5613786a138fb4d8d

KindOtherBase modelnvidia/mit-b0VersionRevision 26de674Bgrade

Model source

Kind: Other
File size: 14.2 MB
Base model: nvidia/mit-b0
Version: vRevision 26de674
Updated: Oct 10, 2026
Downloads: 11
Source: Hugging Face

Source description

segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234

This model is a fine-tuned version of nvidia/mit-b0 on the LeninGF/uff-thermography-multiview-segmentation dataset. It achieves the following results on the evaluation set:

Loss: 0.0878
Mean Iou: 0.9213
Mean Accuracy: 0.9619
Overall Accuracy: 0.9605
Accuracy Background: 0.9553
Accuracy Roi: 0.9685
Iou Background: 0.9359
Iou Roi: 0.9067
Roi Precision: 0.9343
Roi Recall: 0.9685
Roi F1: 0.9511
Roi Dice: 0.9511
Roi Specificity: 0.9553

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Roi	Iou Background	Iou Roi	Roi Precision	Roi Recall	Roi F1	Roi Dice	Roi Specificity
0.3274	1.0	124	0.3163	0.6952	0.8467	0.8206	0.7209	0.9725	0.7081	0.6822	0.6957	0.9725	0.8111	0.8111	0.7209
0.2583	2.0	248	0.2382	0.7916	0.8983	0.8853	0.8356	0.9611	0.8148	0.7685	0.7932	0.9611	0.8691	0.8691	0.8356
0.2154	3.0	372	0.2100	0.8052	0.9083	0.8934	0.8366	0.9800	0.8257	0.7846	0.7973	0.9800	0.8793	0.8793	0.8366
0.2206	4.0	496	0.1878	0.8413	0.9254	0.9155	0.8775	0.9733	0.8624	0.8202	0.8391	0.9733	0.9012	0.9012	0.8775
0.2005	5.0	620	0.1602	0.8769	0.9401	0.9363	0.9220	0.9582	0.8974	0.8564	0.8896	0.9582	0.9226	0.9226	0.9220
0.1672	6.0	744	0.1496	0.8751	0.9411	0.9351	0.9121	0.9701	0.8946	0.8556	0.8787	0.9701	0.9222	0.9222	0.9121
0.1584	7.0	868	0.1457	0.8733	0.9413	0.9340	0.9062	0.9765	0.8924	0.8543	0.8722	0.9765	0.9214	0.9214	0.9062
0.1650	8.0	992	0.1349	0.8847	0.9448	0.9406	0.9246	0.9651	0.9039	0.8656	0.8936	0.9651	0.9280	0.9280	0.9246
0.1242	9.0	1116	0.1344	0.8822	0.9457	0.9390	0.9132	0.9782	0.9004	0.8640	0.8809	0.9782	0.9270	0.9270	0.9132
0.1423	10.0	1240	0.1251	0.8923	0.9491	0.9448	0.9284	0.9698	0.9103	0.8743	0.8988	0.9698	0.9329	0.9329	0.9284
0.1359	11.0	1364	0.1216	0.8948	0.9499	0.9462	0.9319	0.9679	0.9127	0.8769	0.9031	0.9679	0.9344	0.9344	0.9319
0.1415	12.0	1488	0.1207	0.8931	0.9503	0.9451	0.9251	0.9755	0.9105	0.8756	0.8953	0.9755	0.9337	0.9337	0.9251
0.1076	13.0	1612	0.1222	0.8909	0.9494	0.9439	0.9227	0.9761	0.9085	0.8733	0.8923	0.9761	0.9323	0.9323	0.9227
0.1299	14.0	1736	0.1111	0.9029	0.9538	0.9506	0.9382	0.9695	0.9198	0.8860	0.9114	0.9695	0.9396	0.9396	0.9382
0.1018	15.0	1860	0.1128	0.8991	0.9532	0.9484	0.9301	0.9763	0.9159	0.8823	0.9017	0.9763	0.9375	0.9375	0.9301
0.1195	16.0	1984	0.1177	0.8946	0.9510	0.9460	0.9269	0.9750	0.9120	0.8773	0.8974	0.9750	0.9346	0.9346	0.9269
0.1092	17.0	2108	0.1087	0.9019	0.9544	0.9500	0.9330	0.9759	0.9184	0.8854	0.9052	0.9759	0.9392	0.9392	0.9330
0.1051	18.0	2232	0.1052	0.9058	0.9554	0.9522	0.9400	0.9708	0.9223	0.8894	0.9138	0.9708	0.9415	0.9415	0.9400
0.0945	19.0	2356	0.1076	0.9037	0.9544	0.9510	0.9381	0.9706	0.9204	0.8870	0.9115	0.9706	0.9401	0.9401	0.9381
0.1093	20.0	2480	0.1021	0.9080	0.9575	0.9533	0.9372	0.9777	0.9237	0.8923	0.9108	0.9777	0.9431	0.9431	0.9372
0.1270	21.0	2604	0.1002	0.9137	0.9578	0.9565	0.9513	0.9644	0.9296	0.8978	0.9285	0.9644	0.9461	0.9461	0.9513
0.0953	22.0	2728	0.0973	0.9141	0.9585	0.9567	0.9498	0.9673	0.9298	0.8985	0.9267	0.9673	0.9465	0.9465	0.9498
0.1226	23.0	2852	0.0976	0.9158	0.9578	0.9577	0.9574	0.9581	0.9318	0.8997	0.9366	0.9581	0.9472	0.9472	0.9574
0.0806	24.0	2976	0.0965	0.9129	0.9592	0.9559	0.9432	0.9753	0.9281	0.8976	0.9185	0.9753	0.9460	0.9460	0.9432
0.0877	25.0	3100	0.0964	0.9132	0.9587	0.9561	0.9464	0.9710	0.9287	0.8976	0.9223	0.9710	0.9461	0.9461	0.9464
0.0982	26.0	3224	0.1004	0.9063	0.9574	0.9523	0.9328	0.9819	0.9219	0.8907	0.9056	0.9819	0.9422	0.9422	0.9328
0.1049	27.0	3348	0.0974	0.9117	0.9585	0.9553	0.9433	0.9737	0.9273	0.8962	0.9184	0.9737	0.9453	0.9453	0.9433
0.0758	28.0	3472	0.0938	0.9143	0.9600	0.9567	0.9440	0.9759	0.9293	0.8992	0.9196	0.9759	0.9469	0.9469	0.9440
0.0989	29.0	3596	0.0922	0.9179	0.9602	0.9587	0.9533	0.9670	0.9331	0.9028	0.9314	0.9670	0.9489	0.9489	0.9533
0.0779	30.0	3720	0.0929	0.9166	0.9600	0.9580	0.9506	0.9693	0.9318	0.9014	0.9279	0.9693	0.9482	0.9482	0.9506
0.0661	31.0	3844	0.0902	0.9190	0.9612	0.9592	0.9519	0.9704	0.9338	0.9041	0.9297	0.9704	0.9497	0.9497	0.9519
0.0701	32.0	3968	0.0953	0.9130	0.9591	0.9560	0.9440	0.9743	0.9283	0.8977	0.9194	0.9743	0.9461	0.9461	0.9440
0.0952	33.0	4092	0.0924	0.9178	0.9593	0.9587	0.9563	0.9624	0.9333	0.9023	0.9353	0.9624	0.9486	0.9486	0.9563
0.0834	34.0	4216	0.0915	0.9160	0.9604	0.9576	0.9470	0.9737	0.9310	0.9010	0.9234	0.9737	0.9479	0.9479	0.9470
0.0877	35.0	4340	0.0897	0.9186	0.9611	0.9590	0.9513	0.9708	0.9334	0.9037	0.9290	0.9708	0.9494	0.9494	0.9513
0.0650	36.0	4464	0.0900	0.9189	0.9610	0.9592	0.9525	0.9695	0.9338	0.9041	0.9306	0.9695	0.9496	0.9496	0.9525
0.0811	37.0	4588	0.0908	0.9181	0.9606	0.9588	0.9516	0.9697	0.9330	0.9031	0.9293	0.9697	0.9491	0.9491	0.9516
0.0823	38.0	4712	0.0891	0.9203	0.9614	0.9600	0.9544	0.9684	0.9350	0.9055	0.9330	0.9684	0.9504	0.9504	0.9544
0.0631	39.0	4836	0.0919	0.9156	0.9604	0.9574	0.9458	0.9751	0.9305	0.9006	0.9218	0.9751	0.9477	0.9477	0.9458
0.0731	40.0	4960	0.0892	0.9195	0.9612	0.9595	0.9530	0.9695	0.9343	0.9047	0.9312	0.9695	0.9499	0.9499	0.9530
0.0804	41.0	5084	0.0883	0.9207	0.9617	0.9602	0.9545	0.9688	0.9354	0.9060	0.9332	0.9688	0.9507	0.9507	0.9545
0.0765	42.0	5208	0.0904	0.9184	0.9609	0.9589	0.9516	0.9701	0.9333	0.9034	0.9293	0.9701	0.9493	0.9493	0.9516
0.0706	43.0	5332	0.0886	0.9210	0.9614	0.9604	0.9564	0.9664	0.9358	0.9061	0.9357	0.9664	0.9508	0.9508	0.9564
0.0753	44.0	5456	0.0890	0.9198	0.9612	0.9597...

Sources

1 source

Hugging FacePrimaryVersion 26de674b4e1e5600d1bdfab5613786a138fb4d8d

huggingface.co/LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234/tree/26de674b4e1e5600d1bdfab5613786a138fb4d8d

Verified Oct 10

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Source excerpts

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ReadmeAug 1huggingface.co/LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234

--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - medical-imaging - thermography - multiview - breast-cancer - roi-segmentation - generated_from_trainer model-index: - name: segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234 results: [] ---  # segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the LeninGF/uff-thermography-multiview-segmentation dataset. It achieves the following results on the evaluation set: - Loss: 0.0878 - Mean Iou: 0.9213 - Mean Accuracy: 0.9619 - Overall Accuracy: 0.9605 - Accuracy Background: 0.9553 - Accuracy Roi: 0.9685 - Iou Background: 0.9359 - Iou Roi: 0.9067 - Roi Precision: 0.9343 - Roi Recall: 0.9685 - Roi F1: 0.9511 - Roi Dice: 0.9511 - Roi Specificity: 0.9553 ## 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: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Roi | Iou Background | Iou Roi | Roi Precision | Roi Recall | Roi F1 | Roi Dice | Roi Specificity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:--------------:|:-------:|:-------------:|:----------:|:------:|:--------:|:---------------:| | 0.3274 | 1.0 | 124 | 0.3163 | 0.6952 | 0.8467 | 0.8206 | 0.7209 | 0.9725 | 0.7081 | 0.6822 | 0.6957 | 0.9725 | 0.8111 | 0.8111 | 0.7209 | | 0.2583 | 2.0 | 248 | 0.2382 | 0.7916 | 0.8983 | 0.8853 | 0.8356 | 0.9611 | 0.8148 | 0.7685 | 0.7932 | 0.9611 | 0.8691 | 0.8691 | 0.8356 | | 0.2154 | 3.0 | 372...

NoteOct 10huggingface.co/LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234

Source context: 11 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234

NoteAug 1huggingface.co/LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234

Source context: 21 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo LeninGF/segformer-b0-FT-uff-roi-segmentation-all5-vc1-pc0-cw1-e50-260717-2234