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

Mean Iou: 0.4468
Mean Accuracy: 0.4980
Overall Accuracy: 0.7620
Iou Animal Skin: 0.2782
Accuracy Animal Skin: 0.3669
Iou Bone Teeth Horn: 0.3516
Accuracy Bone Teeth Horn: 0.3806
Iou Brickwork: 0.6013
Accuracy Brickwork: 0.6532
Iou Cardboard: 0.5444
Accuracy Cardboard: 0.5949
Iou Carpet Rug: 0.7696
Accuracy Carpet Rug: 0.8371
Iou Ceiling Tile: 0.7731
Accuracy Ceiling Tile: 0.8172
Iou Ceramic: 0.6828
Accuracy Ceramic: 0.7668
Iou Chalkboard Blackboard: 0.6688
Accuracy Chalkboard Blackboard: 0.7109
Iou Clutter: 0.0
Accuracy Clutter: 0.0
Iou Concrete: 0.3679
Accuracy Concrete: 0.4470
Iou Cork Corkboard: 0.0
Accuracy Cork Corkboard: 0.0
Iou Engineered Stone: 0.0
Accuracy Engineered Stone: 0.0
Iou Fabric Cloth: 0.8207
Accuracy Fabric Cloth: 0.8714
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.5244
Accuracy Fire: 0.5419
Iou Foliage: 0.8612
Accuracy Foliage: 0.9157
Iou Food: 0.8339
Accuracy Food: 0.9069
Iou Fur: 0.8275
Accuracy Fur: 0.8946
Iou Gemstone Quartz: 0.0058
Accuracy Gemstone Quartz: 0.0063
Iou Glass: 0.6220
Accuracy Glass: 0.6865
Iou Hair: 0.7757
Accuracy Hair: 0.8573
Iou Ice: 0.0
Accuracy Ice: 0.0
Iou Leather: 0.4593
Accuracy Leather: 0.5184
Iou Liquid Non-water: 0.0086
Accuracy Liquid Non-water: 0.0088
Iou Metal: 0.3786
Accuracy Metal: 0.4462
Iou Mirror: 0.5531
Accuracy Mirror: 0.6207
Iou Paint Plaster Enamel: 0.7352
Accuracy Paint Plaster Enamel: 0.7924
Iou Paper: 0.6283
Accuracy Paper: 0.7011
Iou Pearl: 0.0
Accuracy Pearl: 0.0
Iou Photograph Painting: 0.3018
Accuracy Photograph Painting: 0.3438
Iou Plastic Clear: 0.3244
Accuracy Plastic Clear: 0.3767
Iou Plastic Non-clear: 0.3700
Accuracy Plastic Non-clear: 0.4420
Iou Rubber Latex: 0.3300
Accuracy Rubber Latex: 0.3667
Iou Sand: 0.3527
Accuracy Sand: 0.3737
Iou Skin Lips: 0.7911
Accuracy Skin Lips: 0.8780
Iou Sky: 0.9400
Accuracy Sky: 0.9603
Iou Snow: 0.6050
Accuracy Snow: 0.7399
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.4265
Accuracy Soil Mud: 0.6304
Iou Sponge: 0.0
Accuracy Sponge: 0.0
Iou Stone Natural: 0.5347
Accuracy Stone Natural: 0.6035
Iou Stone Polished: 0.1553
Accuracy Stone Polished: 0.1618
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.6659
Accuracy Tile: 0.7333
Iou Wallpaper: 0.4696
Accuracy Wallpaper: 0.5588
Iou Water: 0.8496
Accuracy Water: 0.9329
Iou Wax: 0.2270
Accuracy Wax: 0.2572
Iou Whiteboard: 0.6993
Accuracy Whiteboard: 0.7630
Iou Wicker: 0.4585
Accuracy Wicker: 0.4913
Iou Wood: 0.7335
Accuracy Wood: 0.7927
Iou Wood Tree: 0.4152
Accuracy Wood Tree: 0.4921
Iou Asphalt: 0.5099
Accuracy Asphalt: 0.6563
Loss: 258.2648
Eval Runtime: 39.2199
Eval Samples Per Second: 30.189
Eval Steps Per Second: 15.094

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

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
299.5840	1.7045	150	0.2567	0.2970	0.6983	0.0000	0.0000	0.0222	0.0224	0.3400	0.4233	0.3128	0.3198	0.7021	0.8554	0.7589	0.8491	0.6248	0.6913	0.0036	0.0036	0.0111	0.0112	0.7983	0.8824	0.0	0.0	0.8305	0.9335	0.1855	0.1919	0.4690	0.8489	0.0	0.0	0.5660	0.6470	0.7348	0.8313	0.1573	0.1615	0.0	0.0	0.1541	0.1582	0.5614	0.7057	0.7090	0.7997	0.5197	0.5864	0.2181	0.2485	0.1523	0.1860	0.1909	0.2053	0.0119	0.0119	0.0	0.0	0.7210	0.8417	0.9087	0.9286	0.0	0.0	0.0754	0.0785	0.2027	0.2241	0.0547	0.0555	0.5975	0.7636	0.0018	0.0018	0.7666	0.9357	0.0	0.0	0.0658	0.0659	0.1783	0.1807	0.6935	0.7455	0.0434	0.0439	0.0027	0.0028	269.6789	45.7877	25.858	12.929
299.5840	1.7045	150	0.2567	0.2970	0.6983	0.0000	0.0000	0.0222	0.0224	0.3400	0.4233	0.3128	0.3198	0.7021	0.8554	0.7589	0.8491	0.6248	0.6913	0.0036	0.0036	0.0111	0.0112	0.7983	0.8824	0.0	0.0	0.8305	0.9335	0.1855	0.1919	0.4690	0.8489	0.0	0.0	0.5660	0.6470	0.7348	0.8313	0.1573	0.1615	0.0	0.0	0.1541	0.1582	0.5614	0.7057	0.7090	0.7997	0.5197	0.5864	0.2181	0.2485	0.1523	0.1860	0.1909	0.2053	0.0119	0.0119	0.0	0.0	0.7210	0.8417	0.9087	0.9286	0.0	0.0	0.0754	0.0785	0.2027	0.2241	0.0547	0.0555	0.5975	0.7636	0.0018	0.0018	0.7666	0.9357	0.0	0.0	0.0658	0.0659	0.1783	0.1807	0.6935	0.7455	0.0434	0.0439	0.0027	0.0028	269.6789	45.7877	25.858	12.929
262.1823	3.4091	300	0.3235	0.3615	0.7080	0.0189	0.0191	0.0653	0.0679	0.3584	0.3780	0.4711	0.5006	0.6836	0.7355	0.7591	0.7958	0.6609	0.7828	0.0571	0.0576	0.0241	0.0242	0.7943	0.8403	0.0	0.0	0.8352	0.9331	0.8166	0.8961	0.8022	0.9297	0.0	0.0	0.5474	0.5987	0.7504	0.8423	0.3957	0.4278	0.0	0.0	0.3643	0.4700	0.5687	0.7083	0.7156	0.7767	0.6168	0.6977	0.2102	0.2266	0.1605	0.1724	0.2831	0.3061	0.1473	0.1531	0.0	0.0	0.7394	0.8203	0.9077	0.9268	0.0	0.0	0.2836	0.4844	0.2620	0.2741	0.0444	0.0447	0.4695	0.4833	0.0539	0.0542	0.8373	0.9231	0.0089	0.0089	0.5345	0.7166	0.3433	0.3603	0.7403	0.8284	0.2860	0.3154	0.2062	0.2164	261.5837	42.1955	28.06	14.03
262.1823	3.4091	300	0.3235	0.3615	0.7080	0.0189	0.0191	0.0653	0.0679	0.3584	0.3780	0.4711	0.5006	0.6836	0.7355	0.7591	0.7958	0.6609	0.7828	0.0571	0.0576	0.0241	0.0242	0.7943	0.8403	0.0	0.0	0.8352	0.9331	0.8166	0.8961	0.8022	0.9297	0.0	0.0	0.5474	0.5987	0.7504	0.8423	0.3957	0.4278	0.0	0.0	0.3643	0.4700	0.5687	0.7083	0.7156	0.7767	0.6168	0.6977	0.2102	0.2266	0.1605	0.1724	0.2831	0.3061	0.1473	0.1531	0.0	0.0	0.7394	0.8203	0.9077	0.9268	0.0	0.0	0.2836	0.4844	0.2620	0.2741	0.0444	0.0447	0.4695	0.4833	0.0539	0.0542	0.8373	0.9231	0.0089	0.0089	0.5345	0.7166	0.3433	0.3603	0.7403	0.8284	0.2860	0.3154	0.2062	0.2164	261.5837	42.1955	28.06	14.03
245.5100	5.1136	450	0.3578	0.3961	0.7330	0.0046	0.0046	0.0166	0.0778	0.5553	0.6214	0.5589	0.6140	0.7322	0.8370	0.7770	0.8381	0.6793	0.7253	0.2954	0.2996	0.0626	0.0647	0.7921	0.8375	0.0	0.0	0.8570	0.9237	0.8076	0.8988	0.8179	0.9320	0.0	0.0	0.6015	0.6660	0.7570	0.8183	0.4219	0.4468	0.0	0.0	0.3432	0.3941	0.5581	0.6377	0.7252	0.7905	0.6301	0.7196	0.2388	0.2551	0.1562	0.1621	0.3735	0.4495	0.1932	0.2021	0.0478	0.0478	0.7502	0.8285	0.9263	0.9429	0.0	0.0	0.3734	0.5391	0.3938	0.4285	0.0631	0.0632	0.6361	0.7058	0.2263	0.2402	0.8345	0.9210	0.0849	0.0858	0.5102	0.6109	0.3885	0.4054	0.7282	0.7809	0.2846	0.3063	0.4031	0.4744	258.5378	39.8687	29.698	14.849
245.5100	5.1136	450	0.3578	0.3961	0.7330	0.0046	0.0046	0.0166	0.0778	0.5553	0.6214	0.5589	0.6140	0.7322	0.8370	0.7770	0.8381	0.6793	0.7253	0.2954	0.2996	0.0626	0.0647	0.7921	0.8375	0.0	0.0	0.8570	0.9237	0.8076	0.8988	0.8179	0.9320	0.0	0.0	0.6015	0.6660	0.7570	0.8183	0.4219	0.4468	0.0	0.0	0.3432	0.3941	0.5581	0.6377	0.7252	0.7905	0.6301	0.7196	0.2388	0.2551	0.1562	0.1621	0.3735	0.4495	0.1932	0.2021	0.0478	0.0478	0.7502	0.8285	0.9263	0.9429	0.0	0.0	0.3734	0.5391	0.3938	0.4285	0.0631	0.0632	0.6361	0.7058	0.2263	0.2402	0.8345	0.9210	0.0849	0.0858	0.5102	0.6109	0.3885	0.4054	0.7282	0.7809	0.2846	0.3063	0.4031	0.4744	258.5378	39.8687	29.698	14.849
230.4948	6.8182	600	0.3620	0.3922	0.7037	0.0257	0.0266	0.0694	0.0704	0.5852	0.6508	0.5136	0.5645	0.6928	0.7263	0.7307	0.7629	0.6108	0.6500	0.5778	0.5998	0.1732	0.1889	0.8070	0.8587	0.0	0.0	0.8352	0.8856	0.8041	0.8539	0.6928	0.7410	0.0	0.0	0.5672	0.6083	0.7616	0.8481	0.3028	0.3245	0.0	0.0	0.3581	0.4143	0.5521	0.6189	0.7083	0.7533	0.6197	0.7231	0.0617	0.0622	0.2678	0.3078	0.3092	0.3421	0.2475	0.2570	0.0486	0.0486	0.7648	0.8619	0.9243	0.9421	0.3533	0.3826	0.2399	0.2831	0.3391	0.3643	0.0000	0.0000	0.6454	0.7325	0.3439	0.3868	0.8433	0.9184	0.1708	0.1863	0.5959	0.6104	0.2604	0.2704	0.6645	0.6947	0.3391	0.3726	0.4148	0.5022	257.6033	38.7134	30.584	15.292
230.4948	6.8182	600	0.3620	0.3922	0.7037	0.0257	0.0266	0.0694	0.0704	0.5852	0.6508	0.5136	0.5645	0.6928	0.7263	0.7307	0.7629	0.6108	0.6500	0.5778	0.5998	0.1732	0.1889	0.8070	0.8587	0.0	0.0	0.8352	0.8856	0.8041	0.8539	0.6928	0.7410	0.0	0.0	0.5672	0.6083	0.7616	0.8481	0.3028	0.3245	0.0	0.0	0.3581	0.4143	0.5521	0.6189	0.7083	0.7533	0.6197	0.7231	0.0617	0.0622	0.2678	0.3078	0.3092	0.3421	0.2475	0.2570	0.0486	0.0486	0.7648	0.8619	0.9243	0.9421	0.3533	0.3826	0.2399	0.2831	0.3391	0.3643	0.0000	0.0000	0.6454	0.7325	0.3439	0.3868	0.8433	0.9184	0.1708	0.1863	0.5959	0.6104	0.2604	0.2704	0.6645	0.6947	0.3391	0.3726	0.4148	0.5022	257.6033	38.7134	30.584	15.292
217.1905	8.5227	750	0.3990	0.4417	0.7522	0.1874	0.3565	0.1023	0.1066	0.5474	0.5801	0.5372	0.6124	0.7040	0.7641	0.7820	0.8385	0.7092	0.7956	0.4965	0.5076	0.3089	0.4068	0.8187	0.8673	0.0004	0.0004	0.8546	0.9151	0.7961	0.8986	0.7517	0.8057	0.0	0.0	0.6299	0.7108	0.7679	0.8453	0.4606	0.5126	0.0010	0.0010	0.3604	0.4088	0.5343	0.6020	0.7352	0.8006	0.6323	0.7166	0.3153	0.3663	0.2762	0.3288	0.3476	0.3920	0.2936	0.3177	0.2462	0.2696	0.7648	0.8599	0.9352	0.9589	0.4354	0.4530	0.3382	0.4091	0.5225	0.6012	0.1067	0.1143	0.6140	0.6702	0.4164	0.4542	0.8522	0.9299	0.1794	0.1921	0.5854	0.6079	0.3114	0.3239	0.7394	0.8024	0.3682	0.4102	0.3806	0.4531	253.3867	38.0343	31.13	15.565
217.1905	8.5227	750	0.3990	0.4417	0.7522	0.1874	0.3565	0.1023	0.1066	0.5474	0.5801	0.5372	0.6124	0.7040	0.7641	0.7820	0.8385	0.7092	0.7956	0.4965	0.5076	0.3089	0.4068	0.8187	0.8673	0.0004	0.0004	0.8546	0.9151	0.7961	0.8986	0.7517	0.8057	0.0	0.0	0.6299	0.7108	0.7679	0.8453	0.4606	0.5126	0.0010	0.0010	0.3604	0.4088	0.5343	0.6020	0.7352	0.8006	0.6323	0.7166	0.3153	0.3663	0.2762	0.3288	0.3476	0.3920	0.2936	0.3177	0.2462	0.2696	0.7648	0.8599	0.9352	0.9589	0.4354	0.4530	0.3382	0.4091	0.5225	0.6012	0.1067	0.1143	0.6140	0.6702	0.4164	0.4542	0.8522	0.9299	0.1794	0.1921	0.5854	0.6079	0.3114	0.3239	0.7394	0.8024	0.3682	0.4102	0.3806	0.4531	253.3867	38.0343	31.13	15.565
206.5969	10.2273	900	0.4238	0.4792	0.7626	0.1961	0.3676	0.1957	0.2013	0.6270	0.6890	0.5686	0.6260	0.7765	0.8632	0.7765	0.8341	0.7001	0.7924	0.7037	0.7671	0.3038	0.3487	0.8227	0.8805	0.0175	0.0175	0.8553	0.9209	0.7864	0.9227	0.8134	0.8870	0.0	0.0	0.6061	0.6685	0.7685	0.8514	0.3511	0.3811	0.0642	0.0642	0.3722	0.4425	0.5096	0.5413	0.7331	0.7930	0.6442	0.7462	0.3402	0.4175	0.3202	0.3833	0.3791	0.4647	0.3212	0.3578	0.1464	0.1465	0.7796	0.8628	0.9322	0.9598	0.5956	0.6421	0.3687	0.6449	0.5577	0.6751	0.1506	0.1553	0.6471	0.7005	0.5168	0.6224	0.8423	0.9241	0.1916	0.2118	0.6644	0.6890	0.4302	0.4579	0.7374	0.8007	0.4155	0.5245	0.5096	0.6730	254.9079	39.4327	30.026	15.013
206.5969	10.2273	900	0.4238	0.4792	0.7626	0.1961	0.3676	0.1957	0.2013	0.6270	0.6890	0.5686	0.6260	0.7765	0.8632	0.7765	0.8341	0.7001	0.7924	0.7037	0.7671	0.3038	0.3487	0.8227	0.8805	0.0175	0.0175	0.8553	0.9209	0.7864	0.9227	0.8134	0.8870	0.0	0.0	0.6061	0.6685	0.7685	0.8514	0.3511	0.3811	0.0642	0.0642	0.3722	0.4425	0.5096	0.5413	0.7331	0.7930	0.6442	0.7462	0.3402	0.4175	0.3202	0.3833	0.3791	0.4647	0.3212	0.3578	0.1464	0.1465	0.7796	0.8628	0.9322	0.9598	0.5956	0.6421	0.3687	0.6449	0.5577	0.6751	0.1506	0.1553	0.6471	0.7005	0.5168	0.6224	0.8423	0.9241	0.1916	0.2118	0.6644	0.6890	0.4302	0.4579	0.7374	0.8007	0.4155	0.5245	0.5096	0.6730	254.9079	39.4327	30.026	15.013
197.8505	11.9318	1050	0.4307	0.4801	0.7598	0.2321	0.2607	0.2876	0.3045	0.6201	0.6951	0.5491	0.6086	0.7679	0.8291	0.7880	0.8394	0.6909	0.7748	0.6917	0.7174	0.3060	0.3543	0.8238	0.8893	0.0177	0.0177	0.8569	0.9276	0.7974	0.9247	0.8340	0.9129	0.0	0.0	0.6469	0.7392	0.7711	0.8569	0.4079	0.4388	0.0006	0.0006	0.3931	0.4888	0.5277	0.5762	0.7169	0.7611	0.6389	0.7224	0.2826	0.3101	0.2803	0.3283	0.3566	0.4158	0.3075	0.3383	0.2920	0.3322	0.7862	0.8642	0.9343	0.9573	0.6242	0.7233	0.4243	0.5841	0.5335	0.6184	0.1821	0.1900	0.6787	0.7603	0.4797	0.5912	0.8513	0.9471	0.2164	0.2399	0.7558	0.8097	0.4405	0.4688	0.7435	0.8095	0.4036	0.4691	0.4559	0.5656	255.5792	37.7301	31.381	15.69
197.8505	11.9318	1050	0.4307	0.4801	0.7598	0.2321	0.2607	0.2876	0.3045	0.6201	0.6951	0.5491	0.6086	0.7679	0.8291	0.7880	0.8394	0.6909	0.7748	0.6917	0.7174	0.3060	0.3543	0.8238	0.8893	0.0177	0.0177	0.8569	0.9276	0.7974	0.9247	0.8340	0.9129	0.0	0.0	0.6469	0.7392	0.7711	0.8569	0.4079	0.4388	0.0006	0.0006	0.3931	0.4888	0.5277	0.5762	0.7169	0.7611	0.6389	0.7224	0.2826	0.3101	0.2803	0.3283	0.3566	0.4158	0.3075	0.3383	0.2920	0.3322	0.7862	0.8642	0.9343	0.9573	0.6242	0.7233	0.4243	0.5841	0.5335	0.6184	0.1821	0.1900	0.6787	0.7603	0.4797	0.5912	0.8513	0.9471	0.2164	0.2399	0.7558	0.8097	0.4405	0.4688	0.7435	0.8095	0.4036	0.4691	0.4559	0.5656	255.5792	37.7301	31.381	15.69
188.3742	13.6364	1200	0.4361	0.4844	0.7645	0.2743	0.3552	0.3140	0.3377	0.5783	0.6529	0.5429	0.5939	0.7708	0.8395	0.7794	0.8209	0.6658	0.7397	0.6695	0.7239	0.3630	0.4513	0.8167	0.8718	0.1456	0.1491	0.8552	0.9105	0.8158	0.9068	0.8260	0.8823	0.0062	0.0100	0.6152	0.6778	0.7729	0.8510	0.4472	0.4882	0.0090	0.0090	0.3853	0.4532	0.5570	0.6117	0.7391	0.8017	0.6396	0.7248	0.3193	0.3710	0.3387	0.3857	0.3652	0.4267	0.3222	0.3568	0.3952	0.4193	0.7871	0.8763	0.9357	0.9612	0.6219	0.6950	0.4516	0.5946	0.5235	0.6066	0.1560	0.1632	0.6752	0.7585	0.4704	0.5785	0.8377	0.9309	0.2320	0.2668	0.6986	0.7350	0.3941	0.4141	0.7375	0.7971	0.3906	0.4667	0.4372	0.5206	257.2170	40.2906	29.386	14.693
188.3742	13.6364	1200	0.4361	0.4844	0.7645	0.2743	0.3552	0.3140	0.3377	0.5783	0.6529	0.5429	0.5939	0.7708	0.8395	0.7794	0.8209	0.6658	0.7397	0.6695	0.7239	0.3630	0.4513	0.8167	0.8718	0.1456	0.1491	0.8552	0.9105	0.8158	0.9068	0.8260	0.8823	0.0062	0.0100	0.6152	0.6778	0.7729	0.8510	0.4472	0.4882	0.0090	0.0090	0.3853	0.4532	0.5570	0.6117	0.7391	0.8017	0.6396	0.7248	0.3193	0.3710	0.3387	0.3857	0.3652	0.4267	0.3222	0.3568	0.3952	0.4193	0.7871	0.8763	0.9357	0.9612	0.6219	0.6950	0.4516	0.5946	0.5235	0.6066	0.1560	0.1632	0.6752	0.7585	0.4704	0.5785	0.8377	0.9309	0.2320	0.2668	0.6986	0.7350	0.3941	0.4141	0.7375	0.7971	0.3906	0.4667	0.4372	0.5206	257.2170	40.2906	29.386	14.693
180.9875	15.3409	1350	0.4402	0.4923	0.7599	0.2896	0.4062	0.3445	0.3714	0.5941	0.6684	0.5763	0.6222	0.7647	0.8306	0.7741	0.8141	0.6725	0.7490	0.6625	0.7058	0.3402	0.4030	0.8191	0.8685	0.1963	0.2000	0.8549	0.9113	0.8234	0.9080	0.8406	0.9128	0.0034	0.0054	0.6312	0.7013	0.7739	0.8536	0.4318	0.4762	0.0116	0.0118	0.3871	0.4678	0.5579	0.6212	0.7294	0.7824	0.6341	0.7171	0.2970	0.3496	0.2895	0.3309	0.3691	0.4421	0.3280	0.3703	0.3220	0.3403	0.7905	0.8730	0.9376	0.9606	0.6410	0.7715	0.4453	0.6679	0.5383	0.6130	0.1504	0.1562	0.6697	0.7411	0.4700	0.5626	0.8523	0.9324	0.2280	0.2600	0.7376	0.7950	0.4347	0.4657	0.7341	0.7947	0.4173	0.4981	0.5240	0.6678	257.7116	37.6537	31.444	15.722
180.9875	15.3409	1350	0.4402	0.4923	0.7599	0.2896	0.4062	0.3445	0.3714	0.5941	0.6684	0.5763	0.6222	0.7647	0.8306	0.7741	0.8141	0.6725	0.7490	0.6625	0.7058	0.3402	0.4030	0.8191	0.8685	0.1963	0.2000	0.8549	0.9113	0.8234	0.9080	0.8406	0.9128	0.0034	0.0054	0.6312	0.7013	0.7739	0.8536	0.4318	0.4762	0.0116	0.0118	0.3871	0.4678	0.5579	0.6212	0.7294	0.7824	0.6341	0.7171	0.2970	0.3496	0.2895	0.3309	0.3691	0.4421	0.3280	0.3703	0.3220	0.3403	0.7905	0.8730	0.9376	0.9606	0.6410	0.7715	0.4453	0.6679	0.5383	0.6130	0.1504	0.1562	0.6697	0.7411	0.4700	0.5626	0.8523	0.9324	0.2280	0.2600	0.7376	0.7950	0.4347	0.4657	0.7341	0.7947	0.4173	0.4981	0.5240	0.6678	257.7116	37.6537	31.444	15.722
176.1014	17.0455	1500	0.4479	0.4988	0.7606	0.3047	0.4263	0.3264	0.3534	0.6065	0.6706	0.5395	0.5847	0.7619	0.8238	0.7732	0.8161	0.6724	0.7522	0.6786	0.7230	0.3442	0.4123	0.8205	0.8689	0.5208	0.5377	0.8572	0.9120	0.8245	0.9012	0.8143	0.8854	0.0040	0.0044	0.6195	0.6819	0.7756	0.8633	0.4668	0.5301	0.0063	0.0064	0.3838	0.4525	0.5545	0.6195	0.7346	0.7903	0.6378	0.7153	0.3252	0.3863	0.3216	0.3805	0.3756	0.4546	0.3283	0.3627	0.4394	0.4488	0.7912	0.8748	0.9400	0.9610	0.6081	0.7360	0.4424	0.5896	0.5233	0.6134	0.1645	0.1731	0.6665	0.7364	0.4738	0.5632	0.8480	0.9335	0.2286	0.2627	0.7182	0.7785	0.4417	0.4700	0.7291	0.7881	0.4151	0.4922	0.4848	0.6007	257.8109	37.4023	31.656	15.828
176.1014	17.0455	1500	0.4479	0.4988	0.7606	0.3047	0.4263	0.3264	0.3534	0.6065	0.6706	0.5395	0.5847	0.7619	0.8238	0.7732	0.8161	0.6724	0.7522	0.6786	0.7230	0.3442	0.4123	0.8205	0.8689	0.5208	0.5377	0.8572	0.9120	0.8245	0.9012	0.8143	0.8854	0.0040	0.0044	0.6195	0.6819	0.7756	0.8633	0.4668	0.5301	0.0063	0.0064	0.3838	0.4525	0.5545	0.6195	0.7346	0.7903	0.6378	0.7153	0.3252	0.3863	0.3216	0.3805	0.3756	0.4546	0.3283	0.3627	0.4394	0.4488	0.7912	0.8748	0.9400	0.9610	0.6081	0.7360	0.4424	0.5896	0.5233	0.6134	0.1645	0.1731	0.6665	0.7364	0.4738	0.5632	0.8480	0.9335	0.2286	0.2627	0.7182	0.7785	0.4417	0.4700	0.7291	0.7881	0.4151	0.4922	0.4848	0.6007	257.8109	37.4023	31.656	15.828
172.7170	18.75	1650	0.4468	0.4980	0.7620	0.2782	0.3669	0.3516	0.3806	0.6013	0.6532	0.5444	0.5949	0.7696	0.8371	0.7731	0.8172	0.6828	0.7668	0.6688	0.7109	0.3679	0.4470	0.8207	0.8714	0.5244	0.5419	0.8612	0.9157	0.8339	0.9069	0.8275	0.8946	0.0058	0.0063	0.6220	0.6865	0.7757	0.8573	0.4593	0.5184	0.0086	0.0088	0.3786	0.4462	0.5531	0.6207	0.7352	0.7924	0.6283	0.7011	0.3018	0.3438	0.3244	0.3767	0.3700	0.4420	0.3300	0.3667	0.3527	0.3737	0.7911	0.8780	0.9400	0.9603	0.6050	0.7399	0.4265	0.6304	0.5347	0.6035	0.1553	0.1618	0.6659	0.7333	0.4696	0.5588	0.8496	0.9329	0.2270	0.2572	0.6993	0.7630	0.4585	0.4913	0.7335	0.7927	0.4152	0.4921	0.5099	0.6563	258.2648	39.2199	30.189	15.094
172.7170	18.75	1650	0.4468	0.4980	0.7620	0.2782	0.3669	0.3516	0.3806	0.6013	0.6532	0.5444	0.5949	0.7696	0.8371	0.7731	0.8172	0.6828	0.7668	0.6688	0.7109	0.3679	0.4470	0.8207	0.8714	0.5244	0.5419	0.8612	0.9157	0.8339	0.9069	0.8275	0.8946	0.0058	0.0063	0.6220	0.6865	0.7757	0.8573	0.4593	0.5184	0.0086	0.0088	0.3786	0.4462	0.5531	0.6207	0.7352	0.7924	0.6283	0.7011	0.3018	0.3438	0.3244	0.3767	0.3700	0.4420	0.3300	0.3667	0.3527	0.3737	0.7911	0.8780	0.9400	0.9603	0.6050	0.7399	0.4265	0.6304	0.5347	0.6035	0.1553	0.1618	0.6659	0.7333	0.4696	0.5588	0.8496	0.9329	0.2270	0.2572	0.6993	0.7630	0.4585	0.4913	0.7335	0.7927	0.4152	0.4921	0.5099	0.6563	258.2648	39.2199	30.189	15.094

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

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

mask2former-swin-large-apple-dms-run3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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