This model is a fine-tuned version of facebook/mask2former-swin-base-ade-semantic on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set:

Mean Iou: 0.4055
Mean Accuracy: 0.4571
Overall Accuracy: 0.7261
Iou Animal Skin: 0.2844
Accuracy Animal Skin: 0.3396
Iou Bone Teeth Horn: 0.1287
Accuracy Bone Teeth Horn: 0.1353
Iou Brickwork: 0.5506
Accuracy Brickwork: 0.6007
Iou Cardboard: 0.4753
Accuracy Cardboard: 0.5163
Iou Carpet Rug: 0.7248
Accuracy Carpet Rug: 0.7993
Iou Ceiling Tile: 0.7318
Accuracy Ceiling Tile: 0.7822
Iou Ceramic: 0.6421
Accuracy Ceramic: 0.7324
Iou Chalkboard Blackboard: 0.6229
Accuracy Chalkboard Blackboard: 0.6613
Iou Clutter: 0.0
Accuracy Clutter: 0.0
Iou Concrete: 0.2897
Accuracy Concrete: 0.3533
Iou Cork Corkboard: 0.0
Accuracy Cork Corkboard: 0.0
Iou Engineered Stone: 0.0
Accuracy Engineered Stone: 0.0
Iou Fabric Cloth: 0.7774
Accuracy Fabric Cloth: 0.8431
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.0462
Accuracy Fire: 0.0485
Iou Foliage: 0.8463
Accuracy Foliage: 0.9090
Iou Food: 0.8038
Accuracy Food: 0.8915
Iou Fur: 0.8120
Accuracy Fur: 0.9058
Iou Gemstone Quartz: 0.0
Accuracy Gemstone Quartz: 0.0
Iou Glass: 0.5701
Accuracy Glass: 0.6385
Iou Hair: 0.7468
Accuracy Hair: 0.8342
Iou Ice: 0.0
Accuracy Ice: 0.0
Iou Leather: 0.4000
Accuracy Leather: 0.4492
Iou Liquid Non-water: 0.0044
Accuracy Liquid Non-water: 0.0045
Iou Metal: 0.3217
Accuracy Metal: 0.3957
Iou Mirror: 0.4412
Accuracy Mirror: 0.4830
Iou Paint Plaster Enamel: 0.6995
Accuracy Paint Plaster Enamel: 0.7585
Iou Paper: 0.5897
Accuracy Paper: 0.6750
Iou Pearl: 0.0
Accuracy Pearl: 0.0
Iou Photograph Painting: 0.2360
Accuracy Photograph Painting: 0.2656
Iou Plastic Clear: 0.2166
Accuracy Plastic Clear: 0.2648
Iou Plastic Non-clear: 0.3274
Accuracy Plastic Non-clear: 0.3924
Iou Rubber Latex: 0.2258
Accuracy Rubber Latex: 0.2529
Iou Sand: 0.4271
Accuracy Sand: 0.5089
Iou Skin Lips: 0.7549
Accuracy Skin Lips: 0.8461
Iou Sky: 0.9267
Accuracy Sky: 0.9488
Iou Snow: 0.5493
Accuracy Snow: 0.6568
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.4185
Accuracy Soil Mud: 0.6073
Iou Sponge: 0.0
Accuracy Sponge: 0.0
Iou Stone Natural: 0.4712
Accuracy Stone Natural: 0.5925
Iou Stone Polished: 0.1409
Accuracy Stone Polished: 0.1469
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.6098
Accuracy Tile: 0.6803
Iou Wallpaper: 0.4314
Accuracy Wallpaper: 0.5003
Iou Water: 0.8358
Accuracy Water: 0.9263
Iou Wax: 0.5110
Accuracy Wax: 0.5657
Iou Whiteboard: 0.6395
Accuracy Whiteboard: 0.7026
Iou Wicker: 0.4063
Accuracy Wicker: 0.4495
Iou Wood: 0.6898
Accuracy Wood: 0.7487
Iou Wood Tree: 0.3589
Accuracy Wood Tree: 0.4629
Iou Asphalt: 0.4000
Accuracy Asphalt: 0.4936
Loss: 275.7852
Eval Runtime: 36.0974
Eval Samples Per Second: 32.8
Eval Steps Per Second: 8.2

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: 64
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 512
total_eval_batch_size: 256
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: 30

Training results

Training Loss	Epoch	Step	Mean Iou	Mean Accuracy	Overall Accuracy	Iou Animal Skin	Accuracy Animal Skin	Iou Bone Teeth Horn	Accuracy Bone Teeth Horn	Iou Brickwork	Accuracy Brickwork	Iou Cardboard	Accuracy Cardboard	Iou Carpet Rug	Accuracy Carpet Rug	Iou Ceiling Tile	Accuracy Ceiling Tile	Iou Ceramic	Accuracy Ceramic	Iou Chalkboard Blackboard	Accuracy Chalkboard Blackboard	Iou Concrete	Accuracy Concrete	Iou Fabric Cloth	Accuracy Fabric Cloth	Iou Fire	Accuracy Fire	Iou Foliage	Accuracy Foliage	Iou Food	Accuracy Food	Iou Fur	Accuracy Fur	Iou Gemstone Quartz	Accuracy Gemstone Quartz	Iou Glass	Accuracy Glass	Iou Hair	Accuracy Hair	Iou Leather	Accuracy Leather	Iou Liquid Non-water	Accuracy Liquid Non-water	Iou Metal	Accuracy Metal	Iou Mirror	Accuracy Mirror	Iou Paint Plaster Enamel	Accuracy Paint Plaster Enamel	Iou Paper	Accuracy Paper	Iou Photograph Painting	Accuracy Photograph Painting	Iou Plastic Clear	Accuracy Plastic Clear	Iou Plastic Non-clear	Accuracy Plastic Non-clear	Iou Rubber Latex	Accuracy Rubber Latex	Iou Sand	Accuracy Sand	Iou Skin Lips	Accuracy Skin Lips	Iou Sky	Accuracy Sky	Iou Snow	Accuracy Snow	Iou Soil Mud	Accuracy Soil Mud	Iou Stone Natural	Accuracy Stone Natural	Iou Stone Polished	Accuracy Stone Polished	Iou Tile	Accuracy Tile	Iou Wallpaper	Accuracy Wallpaper	Iou Water	Accuracy Water	Iou Wax	Accuracy Wax	Iou Whiteboard	Accuracy Whiteboard	Iou Wicker	Accuracy Wicker	Iou Wood	Accuracy Wood	Iou Wood Tree	Accuracy Wood Tree	Iou Asphalt	Accuracy Asphalt	Validation Loss	Eval Runtime	Eval Samples Per Second	Eval Steps Per Second
301.1912	3.4091	150	0.2898	0.3579	0.7339	0.0076	0.0076	0.0377	0.0393	0.3809	0.4826	0.3801	0.4998	0.6153	0.6646	0.7289	0.8201	0.6005	0.7090	0.1974	0.2470	0.0980	0.1084	0.7572	0.8487	0.0	0.0	0.8214	0.9371	0.6580	0.8915	0.6749	0.8054	0.0	0.0	0.5515	0.6922	0.6846	0.7658	0.1850	0.1962	0.0	0.0	0.1701	0.1861	0.4358	0.4965	0.7083	0.8100	0.5016	0.6030	0.2623	0.3442	0.0906	0.1053	0.3206	0.4415	0.1005	0.1111	0.0	0.0	0.6904	0.8394	0.9211	0.9453	0.0	0.0	0.1476	0.1718	0.3222	0.6059	0.0476	0.0493	0.5149	0.7829	0.0990	0.2126	0.6378	0.9376	0.0	0.0	0.3753	0.5290	0.3661	0.4363	0.6920	0.8453	0.2524	0.4036	0.0336	0.0369	275.1264	41.4146	28.589	7.147
301.1912	3.4091	150	0.2898	0.3579	0.7339	0.0076	0.0076	0.0377	0.0393	0.3809	0.4826	0.3801	0.4998	0.6153	0.6646	0.7289	0.8201	0.6005	0.7090	0.1974	0.2470	0.0980	0.1084	0.7572	0.8487	0.0	0.0	0.8214	0.9371	0.6580	0.8915	0.6749	0.8054	0.0	0.0	0.5515	0.6922	0.6846	0.7658	0.1850	0.1962	0.0	0.0	0.1701	0.1861	0.4358	0.4965	0.7083	0.8100	0.5016	0.6030	0.2623	0.3442	0.0906	0.1053	0.3206	0.4415	0.1005	0.1111	0.0	0.0	0.6904	0.8394	0.9211	0.9453	0.0	0.0	0.1476	0.1718	0.3222	0.6059	0.0476	0.0493	0.5149	0.7829	0.0990	0.2126	0.6378	0.9376	0.0	0.0	0.3753	0.5290	0.3661	0.4363	0.6920	0.8453	0.2524	0.4036	0.0336	0.0369	275.1264	41.4146	28.589	7.147
260.4968	6.8182	300	0.3046	0.3354	0.6998	0.0306	0.0313	0.0838	0.0897	0.4298	0.4795	0.3178	0.3286	0.5790	0.6100	0.7563	0.8095	0.6313	0.6848	0.0988	0.0988	0.1094	0.1207	0.7643	0.8200	0.0	0.0	0.8398	0.9283	0.7867	0.8784	0.7336	0.8030	0.0	0.0	0.5445	0.6045	0.7326	0.8224	0.3016	0.3129	0.0	0.0	0.2118	0.2253	0.4509	0.5097	0.7097	0.7853	0.5292	0.5938	0.0297	0.0299	0.0998	0.1036	0.3123	0.3692	0.1398	0.1453	0.0000	0.0000	0.7189	0.8138	0.9222	0.9453	0.0	0.0	0.2938	0.3435	0.3864	0.4797	0.0082	0.0082	0.5603	0.6100	0.0918	0.0925	0.8343	0.9162	0.0	0.0	0.2328	0.2346	0.2893	0.2946	0.7171	0.8185	0.2034	0.2094	0.3567	0.4905	267.0715	36.1774	32.728	8.182
260.4968	6.8182	300	0.3046	0.3354	0.6998	0.0306	0.0313	0.0838	0.0897	0.4298	0.4795	0.3178	0.3286	0.5790	0.6100	0.7563	0.8095	0.6313	0.6848	0.0988	0.0988	0.1094	0.1207	0.7643	0.8200	0.0	0.0	0.8398	0.9283	0.7867	0.8784	0.7336	0.8030	0.0	0.0	0.5445	0.6045	0.7326	0.8224	0.3016	0.3129	0.0	0.0	0.2118	0.2253	0.4509	0.5097	0.7097	0.7853	0.5292	0.5938	0.0297	0.0299	0.0998	0.1036	0.3123	0.3692	0.1398	0.1453	0.0000	0.0000	0.7189	0.8138	0.9222	0.9453	0.0	0.0	0.2938	0.3435	0.3864	0.4797	0.0082	0.0082	0.5603	0.6100	0.0918	0.0925	0.8343	0.9162	0.0	0.0	0.2328	0.2346	0.2893	0.2946	0.7171	0.8185	0.2034	0.2094	0.3567	0.4905	267.0715	36.1774	32.728	8.182
239.0256	10.2273	450	0.3383	0.3755	0.7218	0.0175	0.0177	0.0970	0.1027	0.4031	0.4319	0.4332	0.4707	0.6502	0.6834	0.7271	0.7624	0.6117	0.7097	0.4543	0.4716	0.0872	0.0940	0.7793	0.8516	0.0	0.0	0.8402	0.9010	0.7756	0.8417	0.8023	0.8929	0.0	0.0	0.5819	0.6892	0.7348	0.8024	0.4064	0.4350	0.0	0.0	0.2974	0.3458	0.4461	0.4729	0.7118	0.8044	0.5731	0.6437	0.1389	0.1436	0.1583	0.1797	0.2511	0.2724	0.2062	0.2215	0.0121	0.0121	0.7291	0.8328	0.9191	0.9364	0.0608	0.0608	0.3750	0.5687	0.3882	0.4414	0.0206	0.0206	0.5869	0.6600	0.1148	0.1163	0.8138	0.8746	0.0192	0.0193	0.5996	0.7349	0.3965	0.4299	0.7229	0.8147	0.3473	0.4139	0.3010	0.3459	267.4137	35.7406	33.128	8.282
239.0256	10.2273	450	0.3383	0.3755	0.7218	0.0175	0.0177	0.0970	0.1027	0.4031	0.4319	0.4332	0.4707	0.6502	0.6834	0.7271	0.7624	0.6117	0.7097	0.4543	0.4716	0.0872	0.0940	0.7793	0.8516	0.0	0.0	0.8402	0.9010	0.7756	0.8417	0.8023	0.8929	0.0	0.0	0.5819	0.6892	0.7348	0.8024	0.4064	0.4350	0.0	0.0	0.2974	0.3458	0.4461	0.4729	0.7118	0.8044	0.5731	0.6437	0.1389	0.1436	0.1583	0.1797	0.2511	0.2724	0.2062	0.2215	0.0121	0.0121	0.7291	0.8328	0.9191	0.9364	0.0608	0.0608	0.3750	0.5687	0.3882	0.4414	0.0206	0.0206	0.5869	0.6600	0.1148	0.1163	0.8138	0.8746	0.0192	0.0193	0.5996	0.7349	0.3965	0.4299	0.7229	0.8147	0.3473	0.4139	0.3010	0.3459	267.4137	35.7406	33.128	8.282
222.0586	13.6364	600	0.3698	0.4166	0.7196	0.0861	0.0934	0.0360	0.0896	0.5652	0.6291	0.4632	0.5218	0.7187	0.7899	0.7206	0.7589	0.6431	0.7102	0.4651	0.5623	0.2383	0.2929	0.7820	0.8664	0.0175	0.0209	0.8470	0.9256	0.7711	0.8989	0.7846	0.8931	0.0	0.0	0.5551	0.6232	0.7423	0.8347	0.3623	0.3839	0.0	0.0	0.3158	0.3872	0.4609	0.5365	0.7046	0.7662	0.5729	0.6426	0.2096	0.2288	0.1805	0.2238	0.3135	0.3662	0.2329	0.2539	0.2801	0.2984	0.7432	0.8178	0.9200	0.9356	0.4617	0.5381	0.4072	0.5424	0.4545	0.5667	0.0835	0.0849	0.6026	0.6611	0.3280	0.3462	0.8343	0.9188	0.0601	0.0620	0.4934	0.5363	0.3332	0.3892	0.6804	0.7272	0.3693	0.4344	0.3891	0.5055	266.5801	38.0796	31.093	7.773
222.0586	13.6364	600	0.3698	0.4166	0.7196	0.0861	0.0934	0.0360	0.0896	0.5652	0.6291	0.4632	0.5218	0.7187	0.7899	0.7206	0.7589	0.6431	0.7102	0.4651	0.5623	0.2383	0.2929	0.7820	0.8664	0.0175	0.0209	0.8470	0.9256	0.7711	0.8989	0.7846	0.8931	0.0	0.0	0.5551	0.6232	0.7423	0.8347	0.3623	0.3839	0.0	0.0	0.3158	0.3872	0.4609	0.5365	0.7046	0.7662	0.5729	0.6426	0.2096	0.2288	0.1805	0.2238	0.3135	0.3662	0.2329	0.2539	0.2801	0.2984	0.7432	0.8178	0.9200	0.9356	0.4617	0.5381	0.4072	0.5424	0.4545	0.5667	0.0835	0.0849	0.6026	0.6611	0.3280	0.3462	0.8343	0.9188	0.0601	0.0620	0.4934	0.5363	0.3332	0.3892	0.6804	0.7272	0.3693	0.4344	0.3891	0.5055	266.5801	38.0796	31.093	7.773
206.5963	17.0455	750	0.3955	0.4451	0.7415	0.1809	0.1993	0.1401	0.1529	0.5513	0.5990	0.4418	0.4746	0.7173	0.8341	0.7474	0.8369	0.6442	0.7204	0.5513	0.5735	0.2801	0.3337	0.7832	0.8633	0.0207	0.0207	0.8465	0.9249	0.7911	0.8821	0.8109	0.9107	0.0	0.0	0.5829	0.6868	0.7461	0.8428	0.4063	0.4521	0.0007	0.0007	0.3139	0.3716	0.4426	0.4710	0.7191	0.8007	0.5785	0.6441	0.2647	0.3080	0.2321	0.2820	0.2952	0.3358	0.2362	0.2581	0.2754	0.2931	0.7447	0.8239	0.9270	0.9498	0.4887	0.5510	0.4002	0.5975	0.4574	0.5504	0.0919	0.0939	0.6200	0.6975	0.3868	0.4270	0.8199	0.9360	0.4531	0.4773	0.6314	0.6898	0.4100	0.4456	0.6974	0.7542	0.4040	0.4736	0.4340	0.6047	271.1522	38.0305	31.133	7.783
206.5963	17.0455	750	0.3955	0.4451	0.7415	0.1809	0.1993	0.1401	0.1529	0.5513	0.5990	0.4418	0.4746	0.7173	0.8341	0.7474	0.8369	0.6442	0.7204	0.5513	0.5735	0.2801	0.3337	0.7832	0.8633	0.0207	0.0207	0.8465	0.9249	0.7911	0.8821	0.8109	0.9107	0.0	0.0	0.5829	0.6868	0.7461	0.8428	0.4063	0.4521	0.0007	0.0007	0.3139	0.3716	0.4426	0.4710	0.7191	0.8007	0.5785	0.6441	0.2647	0.3080	0.2321	0.2820	0.2952	0.3358	0.2362	0.2581	0.2754	0.2931	0.7447	0.8239	0.9270	0.9498	0.4887	0.5510	0.4002	0.5975	0.4574	0.5504	0.0919	0.0939	0.6200	0.6975	0.3868	0.4270	0.8199	0.9360	0.4531	0.4773	0.6314	0.6898	0.4100	0.4456	0.6974	0.7542	0.4040	0.4736	0.4340	0.6047	271.1522	38.0305	31.133	7.783
193.3702	20.4545	900	0.3967	0.4483	0.7399	0.1524	0.1694	0.1103	0.1139	0.5465	0.6060	0.4627	0.5054	0.7191	0.7981	0.7483	0.8114	0.6407	0.7360	0.5216	0.5561	0.3076	0.3667	0.7798	0.8437	0.0254	0.0292	0.8480	0.9116	0.8056	0.8972	0.8055	0.9046	0.0	0.0	0.5787	0.6535	0.7495	0.8437	0.4007	0.4610	0.0112	0.0113	0.3172	0.3811	0.4648	0.5211	0.7213	0.8010	0.5872	0.6782	0.2742	0.3268	0.1982	0.2305	0.3129	0.3650	0.2635	0.3019	0.4267	0.4793	0.7579	0.8575	0.9318	0.9566	0.5354	0.6342	0.4134	0.6399	0.4724	0.5782	0.1126	0.1160	0.6013	0.6618	0.3741	0.4238	0.8299	0.9158	0.2934	0.3186	0.6068	0.6560	0.4164	0.4620	0.7014	0.7663	0.3946	0.5005	0.4057	0.5231	271.1607	39.0896	30.289	7.572
193.3702	20.4545	900	0.3967	0.4483	0.7399	0.1524	0.1694	0.1103	0.1139	0.5465	0.6060	0.4627	0.5054	0.7191	0.7981	0.7483	0.8114	0.6407	0.7360	0.5216	0.5561	0.3076	0.3667	0.7798	0.8437	0.0254	0.0292	0.8480	0.9116	0.8056	0.8972	0.8055	0.9046	0.0	0.0	0.5787	0.6535	0.7495	0.8437	0.4007	0.4610	0.0112	0.0113	0.3172	0.3811	0.4648	0.5211	0.7213	0.8010	0.5872	0.6782	0.2742	0.3268	0.1982	0.2305	0.3129	0.3650	0.2635	0.3019	0.4267	0.4793	0.7579	0.8575	0.9318	0.9566	0.5354	0.6342	0.4134	0.6399	0.4724	0.5782	0.1126	0.1160	0.6013	0.6618	0.3741	0.4238	0.8299	0.9158	0.2934	0.3186	0.6068	0.6560	0.4164	0.4620	0.7014	0.7663	0.3946	0.5005	0.4057	0.5231	271.1607	39.0896	30.289	7.572
183.5936	23.8636	1050	0.4069	0.4618	0.7354	0.2576	0.2970	0.1262	0.1346	0.5613	0.6330	0.4349	0.4705	0.7276	0.8065	0.7327	0.7982	0.6408	0.7402	0.6337	0.6707	0.3091	0.3823	0.7847	0.8541	0.0452	0.0474	0.8452	0.9112	0.8090	0.8999	0.8066	0.9083	0.0000	0.0000	0.5808	0.6554	0.7493	0.8395	0.4007	0.4618	0.0044	0.0045	0.3328	0.4191	0.4676	0.5116	0.7034	0.7648	0.5888	0.6803	0.2512	0.2936	0.2021	0.2331	0.3276	0.3952	0.2244	0.2472	0.3855	0.4641	0.7585	0.8530	0.9263	0.9485	0.5453	0.6536	0.4139	0.6280	0.4845	0.6076	0.1648	0.1725	0.6241	0.7026	0.4330	0.5357	0.8294	0.9230	0.5167	0.5681	0.6474	0.7164	0.4152	0.4517	0.7004	0.7620	0.3544	0.4362	0.4133	0.5285	273.2441	38.3911	30.84	7.71
183.5936	23.8636	1050	0.4069	0.4618	0.7354	0.2576	0.2970	0.1262	0.1346	0.5613	0.6330	0.4349	0.4705	0.7276	0.8065	0.7327	0.7982	0.6408	0.7402	0.6337	0.6707	0.3091	0.3823	0.7847	0.8541	0.0452	0.0474	0.8452	0.9112	0.8090	0.8999	0.8066	0.9083	0.0000	0.0000	0.5808	0.6554	0.7493	0.8395	0.4007	0.4618	0.0044	0.0045	0.3328	0.4191	0.4676	0.5116	0.7034	0.7648	0.5888	0.6803	0.2512	0.2936	0.2021	0.2331	0.3276	0.3952	0.2244	0.2472	0.3855	0.4641	0.7585	0.8530	0.9263	0.9485	0.5453	0.6536	0.4139	0.6280	0.4845	0.6076	0.1648	0.1725	0.6241	0.7026	0.4330	0.5357	0.8294	0.9230	0.5167	0.5681	0.6474	0.7164	0.4152	0.4517	0.7004	0.7620	0.3544	0.4362	0.4133	0.5285	273.2441	38.3911	30.84	7.71
177.4213	27.2727	1200	0.4055	0.4571	0.7261	0.2844	0.3396	0.1287	0.1353	0.5506	0.6007	0.4753	0.5163	0.7248	0.7993	0.7318	0.7822	0.6421	0.7324	0.6229	0.6613	0.2897	0.3533	0.7774	0.8431	0.0462	0.0485	0.8463	0.9090	0.8038	0.8915	0.8120	0.9058	0.0	0.0	0.5701	0.6385	0.7468	0.8342	0.4000	0.4492	0.0044	0.0045	0.3217	0.3957	0.4412	0.4830	0.6995	0.7585	0.5897	0.6750	0.2360	0.2656	0.2166	0.2648	0.3274	0.3924	0.2258	0.2529	0.4271	0.5089	0.7549	0.8461	0.9267	0.9488	0.5493	0.6568	0.4185	0.6073	0.4712	0.5925	0.1409	0.1469	0.6098	0.6803	0.4314	0.5003	0.8358	0.9263	0.5110	0.5657	0.6395	0.7026	0.4063	0.4495	0.6898	0.7487	0.3589	0.4629	0.4000	0.4936	275.7852	36.0974	32.8	8.2
177.4213	27.2727	1200	0.4055	0.4571	0.7261	0.2844	0.3396	0.1287	0.1353	0.5506	0.6007	0.4753	0.5163	0.7248	0.7993	0.7318	0.7822	0.6421	0.7324	0.6229	0.6613	0.2897	0.3533	0.7774	0.8431	0.0462	0.0485	0.8463	0.9090	0.8038	0.8915	0.8120	0.9058	0.0	0.0	0.5701	0.6385	0.7468	0.8342	0.4000	0.4492	0.0044	0.0045	0.3217	0.3957	0.4412	0.4830	0.6995	0.7585	0.5897	0.6750	0.2360	0.2656	0.2166	0.2648	0.3274	0.3924	0.2258	0.2529	0.4271	0.5089	0.7549	0.8461	0.9267	0.9488	0.5493	0.6568	0.4185	0.6073	0.4712	0.5925	0.1409	0.1469	0.6098	0.6803	0.4314	0.5003	0.8358	0.9263	0.5110	0.5657	0.6395	0.7026	0.4063	0.4495	0.6898	0.7487	0.3589	0.4629	0.4000	0.4936	275.7852	36.0974	32.8	8.2

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

AllanK24/mask2former-swin-base-apple-dms-run3

mask2former-swin-base-apple-dms-run3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AllanK24/
mask2former-swin-base-apple-dms-run3