This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set:

Loss: 1.7623
Mean Iou: 0.4532
Mean Accuracy: 0.5406
Overall Accuracy: 0.8224
Accuracy Animal Skin: 0.6755
Iou Animal Skin: 0.4518
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.7190
Iou Brickwork: 0.6032
Accuracy Cardboard: 0.6659
Iou Cardboard: 0.5084
Accuracy Carpet Rug: 0.8853
Iou Carpet Rug: 0.7646
Accuracy Ceiling Tile: 0.8605
Iou Ceiling Tile: 0.7578
Accuracy Ceramic: 0.7609
Iou Ceramic: 0.6274
Accuracy Chalkboard Blackboard: 0.7714
Iou Chalkboard Blackboard: 0.6618
Accuracy Clutter: 0.0209
Iou Clutter: 0.0189
Accuracy Concrete: 0.6176
Iou Concrete: 0.4249
Accuracy Cork Corkboard: 0.0777
Iou Cork Corkboard: 0.0771
Accuracy Engineered Stone: 0.0887
Iou Engineered Stone: 0.0831
Accuracy Fabric Cloth: 0.9012
Iou Fabric Cloth: 0.8021
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.3660
Iou Fire: 0.3238
Accuracy Foliage: 0.9401
Iou Foliage: 0.8457
Accuracy Food: 0.9134
Iou Food: 0.7870
Accuracy Fur: 0.9191
Iou Fur: 0.8267
Accuracy Gemstone Quartz: 0.0206
Iou Gemstone Quartz: 0.0165
Accuracy Glass: 0.7480
Iou Glass: 0.6279
Accuracy Hair: 0.8541
Iou Hair: 0.7449
Accuracy Ice: 0.2601
Iou Ice: 0.2271
Accuracy Leather: 0.6014
Iou Leather: 0.5052
Accuracy Liquid Non-water: 0.1274
Iou Liquid Non-water: 0.1127
Accuracy Metal: 0.4793
Iou Metal: 0.3624
Accuracy Mirror: 0.5963
Iou Mirror: 0.5059
Accuracy Paint Plaster Enamel: 0.8822
Iou Paint Plaster Enamel: 0.7548
Accuracy Paper: 0.7413
Iou Paper: 0.6046
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4709
Iou Photograph Painting: 0.3654
Accuracy Plastic Clear: 0.3889
Iou Plastic Clear: 0.2853
Accuracy Plastic Non-clear: 0.5811
Iou Plastic Non-clear: 0.4103
Accuracy Rubber Latex: 0.2984
Iou Rubber Latex: 0.2706
Accuracy Sand: 0.6261
Iou Sand: 0.4917
Accuracy Skin Lips: 0.8534
Iou Skin Lips: 0.7521
Accuracy Sky: 0.9745
Iou Sky: 0.9388
Accuracy Snow: 0.7361
Iou Snow: 0.6369
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5831
Iou Soil Mud: 0.4231
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6938
Iou Stone Natural: 0.5581
Accuracy Stone Polished: 0.3082
Iou Stone Polished: 0.2441
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8167
Iou Tile: 0.6844
Accuracy Wallpaper: 0.5244
Iou Wallpaper: 0.4070
Accuracy Water: 0.9011
Iou Water: 0.8107
Accuracy Wax: 0.4492
Iou Wax: 0.4449
Accuracy Whiteboard: 0.8354
Iou Whiteboard: 0.7040
Accuracy Wicker: 0.5684
Iou Wicker: 0.4837
Accuracy Wood: 0.8578
Iou Wood: 0.7432
Accuracy Wood Tree: 0.5182
Iou Wood Tree: 0.3877
Accuracy Asphalt: 0.6330
Iou Asphalt: 0.4985

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: 0.001
train_batch_size: 32
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 256
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 20
label_smoothing_factor: 0.2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Animal Skin	Iou Animal Skin	Accuracy Brickwork	Iou Brickwork	Accuracy Cardboard	Iou Cardboard	Accuracy Carpet Rug	Iou Carpet Rug	Accuracy Ceiling Tile	Iou Ceiling Tile	Accuracy Ceramic	Iou Ceramic	Accuracy Chalkboard Blackboard	Iou Chalkboard Blackboard	Accuracy Clutter	Iou Clutter	Accuracy Concrete	Iou Concrete	Accuracy Cork Corkboard	Iou Cork Corkboard	Accuracy Engineered Stone	Iou Engineered Stone	Accuracy Fabric Cloth	Iou Fabric Cloth	Accuracy Fire	Iou Fire	Accuracy Foliage	Iou Foliage	Accuracy Food	Iou Food	Accuracy Fur	Iou Fur	Accuracy Gemstone Quartz	Iou Gemstone Quartz	Accuracy Glass	Iou Glass	Accuracy Hair	Iou Hair	Accuracy Ice	Iou Ice	Accuracy Leather	Iou Leather	Accuracy Liquid Non-water	Iou Liquid Non-water	Accuracy Metal	Iou Metal	Accuracy Mirror	Iou Mirror	Accuracy Paint Plaster Enamel	Iou Paint Plaster Enamel	Accuracy Paper	Iou Paper	Accuracy Photograph Painting	Iou Photograph Painting	Accuracy Plastic Clear	Iou Plastic Clear	Accuracy Plastic Non-clear	Iou Plastic Non-clear	Accuracy Rubber Latex	Iou Rubber Latex	Accuracy Sand	Iou Sand	Accuracy Skin Lips	Iou Skin Lips	Accuracy Sky	Iou Sky	Accuracy Snow	Iou Snow	Accuracy Soil Mud	Iou Soil Mud	Accuracy Stone Natural	Iou Stone Natural	Accuracy Stone Polished	Iou Stone Polished	Accuracy Tile	Iou Tile	Accuracy Wallpaper	Iou Wallpaper	Accuracy Water	Iou Water	Accuracy Wax	Iou Wax	Accuracy Whiteboard	Iou Whiteboard	Accuracy Wicker	Iou Wicker	Accuracy Wood	Iou Wood	Accuracy Wood Tree	Iou Wood Tree	Accuracy Asphalt	Iou Asphalt
2.6193	1.7045	150	1.8642	0.2821	0.3476	0.7673	0.0	0.0	0.6057	0.4818	0.3378	0.2951	0.8666	0.6817	0.7559	0.7038	0.6468	0.5399	0.0	0.0	0.0	0.0	0.5856	0.3512	0.0	0.0	0.0	0.0	0.8997	0.7296	0.0	0.0	0.9295	0.8081	0.8663	0.7632	0.9250	0.7312	0.0	0.0	0.7066	0.5480	0.7776	0.6620	0.0	0.0	0.1158	0.1113	0.0	0.0	0.2884	0.2353	0.4135	0.3635	0.8811	0.7023	0.7941	0.4967	0.3673	0.2405	0.0189	0.0188	0.3949	0.2972	0.0	0.0	0.0045	0.0045	0.7649	0.6506	0.9660	0.9170	0.0	0.0	0.5459	0.2846	0.6543	0.4633	0.0439	0.0438	0.6978	0.6032	0.1110	0.1078	0.9223	0.7673	0.0	0.0	0.1463	0.1418	0.0001	0.0001	0.7926	0.6851	0.0031	0.0031	0.2456	0.2381
1.7355	3.4091	300	1.7668	0.4046	0.4972	0.8030	0.5913	0.4254	0.7720	0.5484	0.6033	0.4574	0.9162	0.6958	0.8729	0.7558	0.7240	0.5942	0.7488	0.6230	0.0	0.0	0.6174	0.3871	0.0	0.0	0.0	0.0	0.8968	0.7796	0.0	0.0	0.9396	0.8300	0.8924	0.7827	0.9300	0.8202	0.0	0.0	0.6984	0.5928	0.8213	0.7092	0.0002	0.0002	0.5808	0.4909	0.0	0.0	0.4542	0.3241	0.5424	0.4592	0.8673	0.7334	0.7827	0.5697	0.5307	0.3418	0.3438	0.2548	0.4151	0.3378	0.2082	0.1953	0.5337	0.4660	0.8315	0.7113	0.9733	0.9313	0.6864	0.6194	0.5607	0.3887	0.7327	0.4994	0.2578	0.2195	0.7267	0.6399	0.6744	0.4793	0.9066	0.7942	0.0	0.0	0.7937	0.6195	0.4981	0.4386	0.8444	0.7199	0.4196	0.3075	0.6639	0.4970
1.6191	5.1136	450	1.7432	0.4243	0.5097	0.8144	0.6092	0.4588	0.7118	0.5979	0.6956	0.4453	0.8702	0.7497	0.8831	0.7652	0.7527	0.6141	0.7189	0.6294	0.0	0.0	0.5582	0.3853	0.0	0.0	0.0	0.0	0.8932	0.7891	0.1717	0.1618	0.9278	0.8388	0.9053	0.7878	0.9360	0.8334	0.0	0.0	0.7627	0.6177	0.8198	0.7222	0.1795	0.1531	0.5574	0.4845	0.0	0.0	0.4838	0.3552	0.5726	0.4888	0.8773	0.7464	0.7667	0.5842	0.4415	0.3421	0.3711	0.2815	0.5224	0.3888	0.2410	0.2261	0.6382	0.5091	0.8460	0.7301	0.9683	0.9326	0.7487	0.6580	0.6673	0.4500	0.6798	0.5481	0.2790	0.2380	0.8388	0.6661	0.5316	0.4360	0.9055	0.8073	0.0	0.0	0.7628	0.6489	0.5316	0.4575	0.8372	0.7294	0.4348	0.3176	0.6057	0.4857
1.5588	6.8182	600	1.7473	0.4308	0.5269	0.8149	0.6117	0.4138	0.7434	0.5984	0.6801	0.4788	0.8850	0.7525	0.8566	0.7600	0.7356	0.6199	0.7765	0.6598	0.0	0.0	0.6434	0.4104	0.0	0.0	0.0	0.0	0.8960	0.7961	0.4068	0.2696	0.9267	0.8439	0.9022	0.7866	0.9470	0.8377	0.0024	0.0023	0.7816	0.6154	0.8516	0.7305	0.2744	0.2226	0.6143	0.5076	0.0	0.0	0.5142	0.3643	0.6128	0.5035	0.8523	0.7453	0.7248	0.5885	0.4679	0.3429	0.3457	0.2623	0.5808	0.4045	0.2870	0.2606	0.6795	0.5014	0.8525	0.7381	0.9723	0.9355	0.7143	0.6309	0.6018	0.4166	0.7005	0.5535	0.3759	0.2465	0.7995	0.6740	0.6215	0.4400	0.9029	0.8031	0.0	0.0	0.8257	0.6465	0.5561	0.4781	0.8578	0.7371	0.4609	0.3572	0.5548	0.4644
1.5247	8.5227	750	1.7518	0.4350	0.5261	0.8188	0.6663	0.4424	0.7161	0.5921	0.6493	0.4981	0.8907	0.7577	0.8622	0.7597	0.7655	0.6298	0.7968	0.6739	0.0	0.0	0.6074	0.4314	0.0007	0.0007	0.0377	0.0376	0.9025	0.7946	0.3015	0.2309	0.9386	0.8433	0.9140	0.7810	0.9278	0.8216	0.0228	0.0192	0.7366	0.6193	0.8486	0.7361	0.2523	0.2274	0.6016	0.4953	0.0	0.0	0.5214	0.3686	0.5761	0.4885	0.8635	0.7524	0.7393	0.5908	0.5127	0.3698	0.3750	0.2777	0.5604	0.4056	0.2875	0.2671	0.6182	0.4901	0.8603	0.7434	0.9759	0.9390	0.7488	0.6711	0.5286	0.3854	0.7347	0.5366	0.4034	0.2692	0.8108	0.6822	0.5975	0.4477	0.9002	0.8097	0.0	0.0	0.7853	0.6437	0.5719	0.4936	0.8754	0.7391	0.4672	0.3655	0.6027	0.4934
1.5030	10.2273	900	1.7591	0.4375	0.5292	0.8200	0.6528	0.3962	0.7359	0.5948	0.6475	0.4863	0.8811	0.7654	0.8714	0.7623	0.7702	0.6310	0.7778	0.6554	0.0020	0.0020	0.6547	0.4233	0.0	0.0	0.0526	0.0519	0.9016	0.7990	0.3784	0.2541	0.9375	0.8448	0.9098	0.7870	0.9318	0.8338	0.0364	0.0288	0.7312	0.6206	0.8469	0.7405	0.3046	0.2287	0.6042	0.5055	0.0625	0.0590	0.4752	0.3603	0.5728	0.4990	0.8800	0.7517	0.7486	0.5993	0.4594	0.3574	0.3670	0.2867	0.5731	0.4067	0.3112	0.2800	0.6364	0.4865	0.8628	0.7481	0.9754	0.9379	0.8116	0.6813	0.5794	0.4195	0.6859	0.5465	0.3597	0.2640	0.7919	0.6786	0.5173	0.4171	0.8964	0.8115	0.0043	0.0043	0.8032	0.6615	0.5594	0.4772	0.8592	0.7401	0.5082	0.3815	0.5871	0.4842
1.4878	11.9318	1050	1.7587	0.4443	0.5333	0.8220	0.6410	0.4534	0.7198	0.6076	0.6592	0.4968	0.8767	0.7619	0.8673	0.7656	0.7730	0.6306	0.7623	0.6756	0.0078	0.0074	0.6262	0.4327	0.0552	0.0547	0.0821	0.0796	0.9052	0.8006	0.3560	0.2650	0.9393	0.8452	0.9111	0.7868	0.9279	0.8294	0.0337	0.0273	0.7243	0.6211	0.8559	0.7433	0.3035	0.2180	0.6411	0.5189	0.0676	0.0646	0.4799	0.3627	0.6125	0.5151	0.8833	0.7531	0.7479	0.6037	0.4667	0.3620	0.3773	0.2851	0.5570	0.4073	0.2921	0.2681	0.6186	0.4826	0.8532	0.7510	0.9742	0.9389	0.7981	0.6705	0.5839	0.4256	0.7143	0.5639	0.3564	0.2592	0.8101	0.6850	0.5650	0.4234	0.8946	0.8151	0.0436	0.0435	0.8234	0.6931	0.5984	0.4943	0.8596	0.7419	0.4865	0.3786	0.5961	0.4913
1.4774	13.6364	1200	1.7623	0.4532	0.5406	0.8224	0.6755	0.4518	0.7190	0.6032	0.6659	0.5084	0.8853	0.7646	0.8605	0.7578	0.7609	0.6274	0.7714	0.6618	0.0209	0.0189	0.6176	0.4249	0.0777	0.0771	0.0887	0.0831	0.9012	0.8021	0.3660	0.3238	0.9401	0.8457	0.9134	0.7870	0.9191	0.8267	0.0206	0.0165	0.7480	0.6279	0.8541	0.7449	0.2601	0.2271	0.6014	0.5052	0.1274	0.1127	0.4793	0.3624	0.5963	0.5059	0.8822	0.7548	0.7413	0.6046	0.4709	0.3654	0.3889	0.2853	0.5811	0.4103	0.2984	0.2706	0.6261	0.4917	0.8534	0.7521	0.9745	0.9388	0.7361	0.6369	0.5831	0.4231	0.6938	0.5581	0.3082	0.2441	0.8167	0.6844	0.5244	0.4070	0.9011	0.8107	0.4492	0.4449	0.8354	0.7040	0.5684	0.4837	0.8578	0.7432	0.5182	0.3877	0.6330	0.4985

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

AllanK24/segformer-b5-finetuned-apple-dms-run10

segformer-b5-finetuned-apple-dms-run10

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AllanK24/
segformer-b5-finetuned-apple-dms-run10