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: 1.3158
Mean Iou: 0.3623
Mean Accuracy: 0.4418
Overall Accuracy: 0.7931
Accuracy Animal Skin: 0.0006
Iou Animal Skin: 0.0006
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.6866
Iou Brickwork: 0.5481
Accuracy Cardboard: 0.5888
Iou Cardboard: 0.4554
Accuracy Carpet Rug: 0.8537
Iou Carpet Rug: 0.7194
Accuracy Ceiling Tile: 0.8663
Iou Ceiling Tile: 0.7477
Accuracy Ceramic: 0.7220
Iou Ceramic: 0.5581
Accuracy Chalkboard Blackboard: 0.5775
Iou Chalkboard Blackboard: 0.5219
Accuracy Clutter: 0.0
Iou Clutter: 0.0
Accuracy Concrete: 0.5451
Iou Concrete: 0.3830
Accuracy Cork Corkboard: 0.0
Iou Cork Corkboard: 0.0
Accuracy Engineered Stone: 0.0
Iou Engineered Stone: 0.0
Accuracy Fabric Cloth: 0.8904
Iou Fabric Cloth: 0.7591
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.0
Iou Fire: 0.0
Accuracy Foliage: 0.9360
Iou Foliage: 0.8185
Accuracy Food: 0.8928
Iou Food: 0.7456
Accuracy Fur: 0.9172
Iou Fur: 0.7602
Accuracy Gemstone Quartz: 0.0
Iou Gemstone Quartz: 0.0
Accuracy Glass: 0.7353
Iou Glass: 0.5745
Accuracy Hair: 0.8039
Iou Hair: 0.6858
Accuracy Ice: 0.0
Iou Ice: 0.0
Accuracy Leather: 0.4730
Iou Leather: 0.3979
Accuracy Liquid Non-water: 0.0
Iou Liquid Non-water: 0.0
Accuracy Metal: 0.3912
Iou Metal: 0.3013
Accuracy Mirror: 0.5493
Iou Mirror: 0.4350
Accuracy Paint Plaster Enamel: 0.8595
Iou Paint Plaster Enamel: 0.7257
Accuracy Paper: 0.7236
Iou Paper: 0.5573
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4124
Iou Photograph Painting: 0.3168
Accuracy Plastic Clear: 0.1583
Iou Plastic Clear: 0.1403
Accuracy Plastic Non-clear: 0.4829
Iou Plastic Non-clear: 0.3457
Accuracy Rubber Latex: 0.0001
Iou Rubber Latex: 0.0001
Accuracy Sand: 0.1871
Iou Sand: 0.1813
Accuracy Skin Lips: 0.8032
Iou Skin Lips: 0.6802
Accuracy Sky: 0.9700
Iou Sky: 0.9207
Accuracy Snow: 0.4513
Iou Snow: 0.4329
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5892
Iou Soil Mud: 0.3437
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6717
Iou Stone Natural: 0.4867
Accuracy Stone Polished: 0.1281
Iou Stone Polished: 0.1186
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8029
Iou Tile: 0.6400
Accuracy Wallpaper: 0.5318
Iou Wallpaper: 0.4360
Accuracy Water: 0.9102
Iou Water: 0.7919
Accuracy Wax: 0.0
Iou Wax: 0.0
Accuracy Whiteboard: 0.7470
Iou Whiteboard: 0.5827
Accuracy Wicker: 0.4441
Iou Wicker: 0.3946
Accuracy Wood: 0.8514
Iou Wood: 0.7134
Accuracy Wood Tree: 0.1826
Iou Wood Tree: 0.1617
Accuracy Asphalt: 0.6368
Iou Asphalt: 0.4573

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
label_smoothing_factor: 0.1

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 Foliage	Iou Foliage	Accuracy Food	Iou Food	Accuracy Fur	Iou Fur	Accuracy Glass	Iou Glass	Accuracy Hair	Iou Hair	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
3.5688	1.7045	150	2.6137	0.0684	0.0998	0.5339	0.0	0.0	0.0	0.0	0.0	0.0	0.3088	0.2399	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.8568	0.4306	0.7912	0.5496	0.0	0.0	0.0	0.0	0.4691	0.2483	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.7949	0.5744	0.0934	0.0876	0.0	0.0	0.0	0.0	0.0851	0.0734	0.0	0.0	0.0	0.0	0.0	0.0	0.9228	0.8755	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.2255	0.1953	0.0	0.0	0.0038	0.0038	0.0	0.0	0.0	0.0	0.6390	0.2788	0.0	0.0	0.0	0.0
1.9211	3.4091	300	1.6497	0.1718	0.2221	0.7026	0.0	0.0	0.0	0.0	0.0	0.0	0.7838	0.5890	0.7946	0.6866	0.0131	0.0130	0.0	0.0	0.0537	0.0525	0.8937	0.6446	0.9330	0.6310	0.8090	0.6758	0.0000	0.0000	0.6752	0.4690	0.4928	0.4514	0.0	0.0	0.0434	0.0413	0.0046	0.0046	0.8531	0.6583	0.6791	0.4309	0.0	0.0	0.0	0.0	0.3271	0.2328	0.0	0.0	0.0	0.0	0.4968	0.3999	0.9584	0.9021	0.0	0.0	0.0	0.0	0.2658	0.2160	0.0	0.0	0.7223	0.5106	0.0	0.0	0.9016	0.7058	0.0	0.0	0.0	0.0	0.8499	0.6209	0.0	0.0	0.0	0.0
1.5028	5.1136	450	1.4704	0.2406	0.3013	0.7487	0.0	0.0	0.2897	0.2634	0.0140	0.0140	0.8362	0.6516	0.8453	0.7138	0.6080	0.4702	0.0	0.0	0.3415	0.2590	0.8937	0.7035	0.9425	0.7495	0.8695	0.7195	0.7967	0.6711	0.6795	0.5239	0.7263	0.6182	0.0	0.0	0.2419	0.1988	0.4566	0.3693	0.8605	0.6916	0.7287	0.4862	0.2295	0.1865	0.0	0.0	0.3840	0.2779	0.0	0.0	0.0	0.0	0.7021	0.5816	0.9626	0.9116	0.0	0.0	0.0737	0.0701	0.6191	0.3434	0.0	0.0	0.7829	0.5881	0.0000	0.0000	0.9084	0.7466	0.0262	0.0260	0.0	0.0	0.8399	0.6702	0.0	0.0	0.0067	0.0067
1.3738	6.8182	600	1.3994	0.2871	0.3563	0.7682	0.0	0.0	0.5933	0.4517	0.3638	0.3274	0.8446	0.6768	0.8384	0.7310	0.6566	0.5143	0.0002	0.0002	0.4812	0.3422	0.8782	0.7294	0.9405	0.7912	0.8890	0.7304	0.8957	0.7288	0.6983	0.5410	0.7793	0.6494	0.0140	0.0139	0.3161	0.2497	0.4864	0.3975	0.8675	0.7052	0.7150	0.5289	0.3421	0.2572	0.0007	0.0007	0.3896	0.2926	0.0	0.0	0.0	0.0	0.7434	0.6241	0.9651	0.9163	0.0	0.0	0.4577	0.2661	0.6438	0.4209	0.0000	0.0000	0.7742	0.6133	0.2617	0.2405	0.9063	0.7827	0.5245	0.4024	0.0	0.0	0.8600	0.6829	0.0	0.0	0.4019	0.3214
1.3074	8.5227	750	1.3624	0.3062	0.3833	0.7779	0.0	0.0	0.6482	0.4869	0.5089	0.4049	0.8582	0.6968	0.8547	0.7378	0.7063	0.5348	0.0125	0.0125	0.5220	0.3647	0.8886	0.7402	0.9360	0.8043	0.8915	0.7420	0.9024	0.7365	0.7085	0.5541	0.7820	0.6668	0.2439	0.2241	0.3429	0.2704	0.5404	0.4121	0.8621	0.7150	0.7302	0.5411	0.3841	0.2874	0.0117	0.0116	0.4478	0.3203	0.0	0.0	0.0	0.0	0.7778	0.6488	0.9688	0.9166	0.0	0.0	0.5712	0.3040	0.6293	0.4485	0.0028	0.0028	0.7874	0.6242	0.4531	0.3827	0.9103	0.7865	0.6365	0.4227	0.0326	0.0326	0.8433	0.6982	0.0000	0.0000	0.5344	0.3900
1.2658	10.2273	900	1.3462	0.3228	0.4012	0.7828	0.0	0.0	0.6567	0.5265	0.5569	0.4308	0.8420	0.7075	0.8632	0.7423	0.7176	0.5415	0.2656	0.2578	0.5309	0.3676	0.8909	0.7461	0.9345	0.8103	0.8990	0.7369	0.9111	0.7475	0.6987	0.5598	0.7946	0.6730	0.3752	0.3252	0.3508	0.2778	0.5315	0.4176	0.8618	0.7180	0.7105	0.5476	0.3978	0.3011	0.0358	0.0351	0.4730	0.3324	0.0	0.0	0.0023	0.0023	0.7865	0.6617	0.9707	0.9154	0.0000	0.0000	0.6132	0.3008	0.6699	0.4709	0.0265	0.0263	0.7903	0.6315	0.4821	0.4054	0.9078	0.7889	0.7034	0.4958	0.1628	0.1600	0.8534	0.7028	0.0107	0.0106	0.5860	0.4135
1.2378	11.9318	1050	1.3312	0.3380	0.4175	0.7870	0.0	0.0	0.6778	0.5330	0.5772	0.4394	0.8673	0.7090	0.8605	0.7450	0.7195	0.5508	0.3793	0.3575	0.5300	0.3790	0.8896	0.7528	0.9396	0.8077	0.8880	0.7441	0.9088	0.7550	0.7071	0.5654	0.7963	0.6796	0.4251	0.3618	0.3783	0.2920	0.5525	0.4247	0.8571	0.7220	0.7336	0.5504	0.4271	0.3118	0.0769	0.0731	0.4776	0.3382	0.0	0.0	0.0566	0.0562	0.7978	0.6700	0.9688	0.9189	0.0896	0.0895	0.5679	0.3013	0.6787	0.4728	0.0725	0.0693	0.7887	0.6365	0.5238	0.4290	0.9096	0.7886	0.7214	0.5278	0.3295	0.3123	0.8575	0.7067	0.0440	0.0428	0.6361	0.4609
1.2215	13.6364	1200	1.3248	0.3517	0.4307	0.7900	0.0	0.0	0.6877	0.5403	0.5812	0.4504	0.8554	0.7148	0.8656	0.7473	0.7195	0.5531	0.5399	0.4932	0.5427	0.3793	0.8917	0.7551	0.9389	0.8136	0.8954	0.7466	0.9134	0.7592	0.7213	0.5705	0.8016	0.6808	0.4466	0.3779	0.3777	0.2941	0.5322	0.4285	0.8577	0.7240	0.7171	0.5551	0.4204	0.3161	0.1070	0.0990	0.4940	0.3439	0.0	0.0	0.1158	0.1140	0.7976	0.6745	0.9700	0.9198	0.3121	0.3043	0.5806	0.3254	0.6769	0.4762	0.0882	0.0837	0.7989	0.6351	0.5246	0.4316	0.9105	0.7910	0.7383	0.5688	0.3929	0.3607	0.8514	0.7101	0.1055	0.0988	0.6250	0.4500
1.2064	15.3409	1350	1.3207	0.3571	0.4362	0.7914	0.0000	0.0000	0.6850	0.5453	0.5846	0.4522	0.8558	0.7158	0.8546	0.7498	0.7210	0.5554	0.5726	0.5196	0.5473	0.3815	0.8874	0.7589	0.9381	0.8149	0.8911	0.7424	0.9186	0.7574	0.7069	0.5679	0.8050	0.6850	0.4640	0.3898	0.3907	0.2991	0.5293	0.4230	0.8644	0.7245	0.7226	0.5560	0.4203	0.3160	0.1310	0.1200	0.4817	0.3439	0.0000	0.0000	0.1269	0.1234	0.8050	0.6786	0.9705	0.9200	0.3876	0.3741	0.6014	0.3333	0.6697	0.4821	0.1135	0.1057	0.7979	0.6392	0.5285	0.4360	0.9072	0.7929	0.7434	0.5773	0.4286	0.3858	0.8513	0.7111	0.1509	0.1360	0.6294	0.4530
1.1984	17.0455	1500	1.3180	0.3601	0.4398	0.7925	0.0004	0.0004	0.6866	0.5485	0.5912	0.4540	0.8599	0.7154	0.8612	0.7493	0.7194	0.5573	0.5756	0.5187	0.5455	0.3841	0.8843	0.7607	0.9375	0.8172	0.8885	0.7442	0.9148	0.7614	0.7219	0.5725	0.8050	0.6858	0.4795	0.3998	0.3906	0.3000	0.5431	0.4321	0.8614	0.7252	0.7201	0.5573	0.4147	0.3164	0.1534	0.1366	0.4879	0.3461	0.0001	0.0001	0.1564	0.1519	0.8036	0.6797	0.9702	0.9209	0.4214	0.4056	0.5866	0.3357	0.6812	0.4817	0.1247	0.1153	0.8012	0.6404	0.5272	0.4339	0.9086	0.7921	0.7520	0.5788	0.4392	0.3911	0.8557	0.7121	0.1634	0.1459	0.6344	0.4573
1.1937	18.75	1650	1.3158	0.3623	0.4418	0.7931	0.0006	0.0006	0.6866	0.5481	0.5888	0.4554	0.8537	0.7194	0.8663	0.7477	0.7220	0.5581	0.5775	0.5219	0.5451	0.3830	0.8904	0.7591	0.9360	0.8185	0.8928	0.7456	0.9172	0.7602	0.7353	0.5745	0.8039	0.6858	0.4730	0.3979	0.3912	0.3013	0.5493	0.4350	0.8595	0.7257	0.7236	0.5573	0.4124	0.3168	0.1583	0.1403	0.4829	0.3457	0.0001	0.0001	0.1871	0.1813	0.8032	0.6802	0.9700	0.9207	0.4513	0.4329	0.5892	0.3437	0.6717	0.4867	0.1281	0.1186	0.8029	0.6400	0.5318	0.4360	0.9102	0.7919	0.7470	0.5827	0.4441	0.3946	0.8514	0.7134	0.1826	0.1617	0.6368	0.4573

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-run8

segformer-b5-finetuned-apple-dms-run8

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AllanK24/
segformer-b5-finetuned-apple-dms-run8