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: 1.3077
Mean Iou: 0.4253
Mean Accuracy: 0.5189
Overall Accuracy: 0.8145
Accuracy Animal Skin: 0.5870
Iou Animal Skin: 0.2939
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.7441
Iou Brickwork: 0.6040
Accuracy Cardboard: 0.6235
Iou Cardboard: 0.4832
Accuracy Carpet Rug: 0.8743
Iou Carpet Rug: 0.7503
Accuracy Ceiling Tile: 0.8920
Iou Ceiling Tile: 0.7888
Accuracy Ceramic: 0.7749
Iou Ceramic: 0.6257
Accuracy Chalkboard Blackboard: 0.7382
Iou Chalkboard Blackboard: 0.6366
Accuracy Clutter: 0.0
Iou Clutter: 0.0
Accuracy Concrete: 0.5872
Iou Concrete: 0.4066
Accuracy Cork Corkboard: 0.0
Iou Cork Corkboard: 0.0
Accuracy Engineered Stone: 0.0
Iou Engineered Stone: 0.0
Accuracy Fabric Cloth: 0.8902
Iou Fabric Cloth: 0.7934
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.4331
Iou Fire: 0.4212
Accuracy Foliage: 0.9339
Iou Foliage: 0.8348
Accuracy Food: 0.9011
Iou Food: 0.7785
Accuracy Fur: 0.9033
Iou Fur: 0.8061
Accuracy Gemstone Quartz: 0.0
Iou Gemstone Quartz: 0.0
Accuracy Glass: 0.7496
Iou Glass: 0.6174
Accuracy Hair: 0.8421
Iou Hair: 0.7302
Accuracy Ice: 0.1799
Iou Ice: 0.1305
Accuracy Leather: 0.6400
Iou Leather: 0.5078
Accuracy Liquid Non-water: 0.0
Iou Liquid Non-water: 0.0
Accuracy Metal: 0.4655
Iou Metal: 0.3481
Accuracy Mirror: 0.5527
Iou Mirror: 0.4567
Accuracy Paint Plaster Enamel: 0.8732
Iou Paint Plaster Enamel: 0.7459
Accuracy Paper: 0.7452
Iou Paper: 0.5942
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4753
Iou Photograph Painting: 0.3295
Accuracy Plastic Clear: 0.3794
Iou Plastic Clear: 0.2747
Accuracy Plastic Non-clear: 0.5463
Iou Plastic Non-clear: 0.3914
Accuracy Rubber Latex: 0.2845
Iou Rubber Latex: 0.2550
Accuracy Sand: 0.5765
Iou Sand: 0.4156
Accuracy Skin Lips: 0.8413
Iou Skin Lips: 0.7308
Accuracy Sky: 0.9744
Iou Sky: 0.9326
Accuracy Snow: 0.7705
Iou Snow: 0.6370
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.6113
Iou Soil Mud: 0.4285
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6762
Iou Stone Natural: 0.5342
Accuracy Stone Polished: 0.3885
Iou Stone Polished: 0.2629
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.7947
Iou Tile: 0.6756
Accuracy Wallpaper: 0.5898
Iou Wallpaper: 0.4295
Accuracy Water: 0.9016
Iou Water: 0.8076
Accuracy Wax: 0.0
Iou Wax: 0.0
Accuracy Whiteboard: 0.7941
Iou Whiteboard: 0.6586
Accuracy Wicker: 0.5480
Iou Wicker: 0.4575
Accuracy Wood: 0.8556
Iou Wood: 0.7408
Accuracy Wood Tree: 0.4315
Iou Wood Tree: 0.3282
Accuracy Asphalt: 0.6128
Iou Asphalt: 0.4723

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: 0.0001
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

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 Concrete	Iou Concrete	Accuracy Fabric Cloth	Iou Fabric Cloth	Accuracy Fire	Iou Fire	Accuracy Foliage	Iou Foliage	Accuracy Food	Iou Food	Accuracy Fur	Iou Fur	Accuracy Glass	Iou Glass	Accuracy Hair	Iou Hair	Accuracy Ice	Iou Ice	Accuracy Leather	Iou Leather	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 Whiteboard	Iou Whiteboard	Accuracy Wicker	Iou Wicker	Accuracy Wood	Iou Wood	Accuracy Wood Tree	Iou Wood Tree	Accuracy Asphalt	Iou Asphalt
3.1266	1.7045	150	2.0470	0.1332	0.1807	0.6680	0.0	0.0	0.0000	0.0000	0.0	0.0	0.8237	0.5426	0.6377	0.6030	0.0	0.0	0.0	0.0	0.0000	0.0000	0.8806	0.6444	0.0	0.0	0.8859	0.6379	0.0001	0.0001	0.0	0.0	0.6208	0.4114	0.2371	0.2295	0.0	0.0	0.0	0.0	0.0001	0.0001	0.0	0.0	0.8711	0.6550	0.5900	0.3465	0.0	0.0	0.0	0.0	0.0331	0.0284	0.0	0.0	0.0	0.0	0.4288	0.3209	0.9656	0.8741	0.0	0.0	0.0	0.0	0.0102	0.0100	0.0	0.0	0.6919	0.4427	0.0	0.0	0.8808	0.6685	0.0	0.0	0.0	0.0	0.8373	0.5138	0.0	0.0	0.0	0.0
1.6266	3.4091	300	1.4601	0.2452	0.3118	0.7612	0.0	0.0	0.5589	0.4366	0.0	0.0	0.8917	0.6707	0.9010	0.7560	0.6389	0.5234	0.0	0.0	0.4071	0.2837	0.8879	0.7321	0.0	0.0	0.9466	0.7737	0.8977	0.6734	0.8448	0.7011	0.7377	0.5588	0.7789	0.6435	0.0	0.0	0.0015	0.0015	0.3779	0.2590	0.3591	0.3062	0.8587	0.7132	0.7375	0.5164	0.0207	0.0197	0.0	0.0	0.3572	0.2674	0.0	0.0	0.0	0.0	0.7716	0.6293	0.9576	0.9012	0.0	0.0	0.0	0.0	0.7128	0.3441	0.0	0.0	0.7963	0.6090	0.0	0.0	0.9190	0.7378	0.0	0.0	0.0	0.0	0.8546	0.6944	0.0	0.0	0.0	0.0
1.2720	5.1136	450	1.3447	0.3316	0.4104	0.7896	0.0	0.0	0.7478	0.5451	0.5884	0.3796	0.8540	0.7260	0.8883	0.7721	0.7486	0.5563	0.3403	0.3345	0.5696	0.3659	0.8863	0.7700	0.0	0.0	0.9421	0.7940	0.8469	0.7248	0.8960	0.7492	0.7052	0.5872	0.7949	0.6743	0.0	0.0	0.5574	0.4551	0.3857	0.2980	0.5314	0.4089	0.8726	0.7305	0.7624	0.5497	0.3791	0.2755	0.1261	0.1196	0.5391	0.3477	0.0	0.0	0.0	0.0	0.8003	0.6764	0.9653	0.9207	0.1193	0.1190	0.6007	0.2808	0.6866	0.4591	0.0813	0.0794	0.7917	0.6641	0.5070	0.4132	0.8735	0.7914	0.6222	0.5232	0.1000	0.0997	0.8204	0.7098	0.0	0.0	0.4105	0.3427
1.1379	6.8182	600	1.3081	0.3819	0.4664	0.8010	0.0490	0.0461	0.7274	0.5798	0.5532	0.4373	0.8861	0.7325	0.9033	0.7782	0.6821	0.5783	0.7019	0.6125	0.6244	0.3866	0.8933	0.7792	0.0	0.0	0.9288	0.8225	0.8828	0.7810	0.9066	0.7305	0.7618	0.6004	0.8108	0.6967	0.0	0.0	0.5526	0.4746	0.4685	0.3267	0.5202	0.4310	0.8546	0.7322	0.6959	0.5681	0.4042	0.2966	0.2329	0.1995	0.5245	0.3733	0.0105	0.0105	0.4181	0.3782	0.8142	0.6921	0.9770	0.9188	0.6685	0.4830	0.6491	0.4179	0.5688	0.4889	0.3195	0.2385	0.7905	0.6600	0.6868	0.4128	0.8790	0.7885	0.7605	0.6379	0.5190	0.4238	0.8590	0.7217	0.2166	0.1985	0.5500	0.4260
1.0634	8.5227	750	1.3006	0.4019	0.4920	0.8080	0.3699	0.2620	0.7329	0.5964	0.6608	0.4574	0.8618	0.7384	0.8733	0.7790	0.7490	0.6095	0.7356	0.6392	0.6012	0.3996	0.8758	0.7870	0.0031	0.0031	0.9263	0.8317	0.8998	0.7745	0.9170	0.7916	0.7234	0.6068	0.8269	0.7049	0.0	0.0	0.6082	0.4933	0.4902	0.3403	0.5999	0.4335	0.8664	0.7423	0.7104	0.5737	0.4555	0.3192	0.3542	0.2560	0.5450	0.3841	0.1623	0.1584	0.4816	0.3917	0.8430	0.7076	0.9731	0.9233	0.6777	0.5473	0.5624	0.3957	0.7024	0.5446	0.3731	0.2523	0.7957	0.6695	0.5623	0.4144	0.9054	0.7947	0.8212	0.6868	0.5285	0.4341	0.8737	0.7298	0.3633	0.2807	0.5733	0.4462
1.0209	10.2273	900	1.3041	0.4064	0.5009	0.8103	0.5856	0.2910	0.7201	0.5885	0.5736	0.4615	0.8778	0.7474	0.8946	0.7727	0.7565	0.6117	0.7275	0.6232	0.6068	0.4080	0.8950	0.7861	0.0393	0.0393	0.9336	0.8332	0.9065	0.7845	0.8986	0.8046	0.7285	0.6119	0.8362	0.7141	0.0000	0.0000	0.6110	0.4801	0.4687	0.3421	0.5397	0.4322	0.8685	0.7430	0.7377	0.5778	0.4600	0.3224	0.3850	0.2706	0.5130	0.3831	0.2478	0.2265	0.6569	0.4241	0.8271	0.7170	0.9750	0.9255	0.7143	0.5150	0.5883	0.4210	0.6578	0.5323	0.3653	0.2544	0.7753	0.6682	0.5172	0.3954	0.8987	0.8064	0.8012	0.6615	0.5654	0.4626	0.8761	0.7324	0.4044	0.3058	0.6129	0.4557
0.9947	11.9318	1050	1.3039	0.4154	0.5095	0.8114	0.5591	0.2799	0.7221	0.6003	0.6119	0.4872	0.8668	0.7475	0.9236	0.7641	0.7722	0.6163	0.7251	0.6246	0.5939	0.3938	0.8891	0.7909	0.1965	0.1948	0.9313	0.8350	0.8982	0.7785	0.9115	0.8078	0.7259	0.6100	0.8414	0.7209	0.1545	0.1346	0.6370	0.5032	0.4723	0.3438	0.5829	0.4495	0.8638	0.7432	0.7397	0.5903	0.4464	0.3188	0.3869	0.2780	0.5478	0.3916	0.2514	0.2324	0.5061	0.3590	0.8400	0.7234	0.9693	0.9299	0.7402	0.6098	0.6160	0.4255	0.6744	0.5304	0.3971	0.2655	0.8177	0.6663	0.5914	0.4309	0.9022	0.8018	0.7865	0.6512	0.5583	0.4532	0.8672	0.7377	0.4299	0.3239	0.5452	0.4532
0.9785	13.6364	1200	1.3080	0.4212	0.5165	0.8122	0.6066	0.2992	0.7270	0.5988	0.6112	0.4898	0.8715	0.7526	0.8936	0.7848	0.7466	0.6156	0.7435	0.6314	0.5955	0.3964	0.8895	0.7935	0.3439	0.3395	0.9309	0.8344	0.9014	0.7781	0.9031	0.8083	0.7532	0.6145	0.8393	0.7245	0.1260	0.0966	0.6376	0.5048	0.4756	0.3433	0.5374	0.4532	0.8615	0.7445	0.7457	0.5938	0.4762	0.3246	0.4005	0.2743	0.5811	0.3932	0.2686	0.2423	0.5457	0.4003	0.8419	0.7277	0.9763	0.9296	0.7664	0.6238	0.6024	0.4119	0.6903	0.5325	0.3751	0.2604	0.8065	0.6764	0.5792	0.4328	0.9019	0.7995	0.8292	0.6708	0.5375	0.4540	0.8521	0.7380	0.4359	0.3317	0.6527	0.4799
0.9659	15.3409	1350	1.3077	0.4249	0.5202	0.8136	0.5958	0.2990	0.7262	0.5949	0.6191	0.4861	0.8671	0.7505	0.8948	0.7844	0.7725	0.6265	0.7412	0.6359	0.5794	0.3990	0.8896	0.7923	0.4225	0.4133	0.9331	0.8349	0.9004	0.7787	0.9030	0.8067	0.7381	0.6179	0.8415	0.7281	0.1611	0.1190	0.6392	0.5112	0.4776	0.3477	0.5660	0.4586	0.8698	0.7444	0.7489	0.5937	0.4764	0.3321	0.3728	0.2760	0.5484	0.3912	0.2891	0.2562	0.5627	0.4188	0.8464	0.7301	0.9758	0.9331	0.7731	0.6259	0.6343	0.4261	0.6824	0.5348	0.4013	0.2663	0.8032	0.6777	0.6241	0.4227	0.8983	0.8082	0.8110	0.6685	0.5639	0.4608	0.8542	0.7400	0.4273	0.3306	0.6197	0.4729
0.9593	17.0455	1500	1.3077	0.4253	0.5189	0.8145	0.5870	0.2939	0.7441	0.6040	0.6235	0.4832	0.8743	0.7503	0.8920	0.7888	0.7749	0.6257	0.7382	0.6366	0.5872	0.4066	0.8902	0.7934	0.4331	0.4212	0.9339	0.8348	0.9011	0.7785	0.9033	0.8061	0.7496	0.6174	0.8421	0.7302	0.1799	0.1305	0.6400	0.5078	0.4655	0.3481	0.5527	0.4567	0.8732	0.7459	0.7452	0.5942	0.4753	0.3295	0.3794	0.2747	0.5463	0.3914	0.2845	0.2550	0.5765	0.4156	0.8413	0.7308	0.9744	0.9326	0.7705	0.6370	0.6113	0.4285	0.6762	0.5342	0.3885	0.2629	0.7947	0.6756	0.5898	0.4295	0.9016	0.8076	0.7941	0.6586	0.5480	0.4575	0.8556	0.7408	0.4315	0.3282	0.6128	0.4723

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-run5

segformer-b5-finetuned-apple-dms-run5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AllanK24/
segformer-b5-finetuned-apple-dms-run5