segformer-b5-finetuned-apple-dms-v2-run1

This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the AllanK24/apple-dms-materials-v2 dataset. It achieves the following results on the evaluation set:

Loss: 1.2754
Mean Iou: 0.5006
Mean Accuracy: 0.6100
Overall Accuracy: 0.8397
Accuracy Animal Skin: 0.8005
Iou Animal Skin: 0.4862
Accuracy Bone Teeth Horn: 0.1387
Iou Bone Teeth Horn: 0.1204
Accuracy Brickwork: 0.8096
Iou Brickwork: 0.6848
Accuracy Cardboard: 0.6607
Iou Cardboard: 0.5516
Accuracy Carpet Rug: 0.8598
Iou Carpet Rug: 0.7556
Accuracy Ceiling Tile: 0.8983
Iou Ceiling Tile: 0.8209
Accuracy Ceramic: 0.7842
Iou Ceramic: 0.6694
Accuracy Chalkboard Blackboard: 0.8636
Iou Chalkboard Blackboard: 0.7234
Accuracy Clutter: 0.5984
Iou Clutter: 0.4229
Accuracy Concrete: 0.5865
Iou Concrete: 0.4138
Accuracy Cork Corkboard: 0.2056
Iou Cork Corkboard: 0.1051
Accuracy Engineered Stone: 0.4230
Iou Engineered Stone: 0.2020
Accuracy Fabric Cloth: 0.9106
Iou Fabric Cloth: 0.8243
Accuracy Fiberglass Wool: 0.0271
Iou Fiberglass Wool: 0.0268
Accuracy Fire: 0.7920
Iou Fire: 0.5373
Accuracy Foliage: 0.9280
Iou Foliage: 0.8490
Accuracy Food: 0.9232
Iou Food: 0.8393
Accuracy Fur: 0.9405
Iou Fur: 0.8882
Accuracy Gemstone Quartz: 0.3523
Iou Gemstone Quartz: 0.3354
Accuracy Glass: 0.7808
Iou Glass: 0.6677
Accuracy Hair: 0.8621
Iou Hair: 0.7719
Accuracy Ice: 0.2156
Iou Ice: 0.1253
Accuracy Leather: 0.6739
Iou Leather: 0.5558
Accuracy Liquid Non-water: 0.2593
Iou Liquid Non-water: 0.2250
Accuracy Metal: 0.5549
Iou Metal: 0.4091
Accuracy Mirror: 0.6199
Iou Mirror: 0.5209
Accuracy Paint Plaster Enamel: 0.8861
Iou Paint Plaster Enamel: 0.7778
Accuracy Paper: 0.7714
Iou Paper: 0.6499
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.5282
Iou Photograph Painting: 0.4005
Accuracy Plastic Clear: 0.4393
Iou Plastic Clear: 0.3276
Accuracy Plastic Non-clear: 0.6138
Iou Plastic Non-clear: 0.4600
Accuracy Rubber Latex: 0.4728
Iou Rubber Latex: 0.3846
Accuracy Sand: 0.5452
Iou Sand: 0.3426
Accuracy Skin Lips: 0.8902
Iou Skin Lips: 0.7880
Accuracy Sky: 0.9877
Iou Sky: 0.9391
Accuracy Snow: 0.8017
Iou Snow: 0.6026
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.6541
Iou Soil Mud: 0.4607
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.7723
Iou Stone Natural: 0.6161
Accuracy Stone Polished: 0.3786
Iou Stone Polished: 0.3020
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8121
Iou Tile: 0.6983
Accuracy Wallpaper: 0.6859
Iou Wallpaper: 0.4537
Accuracy Water: 0.8886
Iou Water: 0.8101
Accuracy Wax: 0.6979
Iou Wax: 0.5940
Accuracy Whiteboard: 0.8540
Iou Whiteboard: 0.7524
Accuracy Wicker: 0.5916
Iou Wicker: 0.5057
Accuracy Wood: 0.8780
Iou Wood: 0.7615
Accuracy Wood Tree: 0.4560
Iou Wood Tree: 0.3458
Accuracy Asphalt: 0.6458
Iou Asphalt: 0.5250

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: 40
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 Bone Teeth Horn	Iou Bone Teeth Horn	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 Fiberglass Wool	Iou Fiberglass Wool	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
2.0218	2.6923	350	1.2875	0.3751	0.4555	0.7968	0.0639	0.0632	0.0	0.0	0.6612	0.5462	0.5668	0.4804	0.8298	0.6941	0.8161	0.7582	0.7719	0.5863	0.7409	0.6508	0.0	0.0	0.6129	0.3495	0.0	0.0	0.0	0.0	0.8863	0.7723	0.0	0.0	0.0	0.0	0.9123	0.8136	0.8804	0.7853	0.8918	0.7595	0.0	0.0	0.7912	0.5739	0.8006	0.6879	0.0	0.0	0.4375	0.3880	0.0	0.0	0.4070	0.3133	0.5998	0.4987	0.8757	0.7327	0.7172	0.5690	0.5014	0.3580	0.2002	0.1526	0.4134	0.3277	0.0305	0.0301	0.4554	0.2960	0.8106	0.6951	0.9783	0.9317	0.9007	0.5026	0.4719	0.3300	0.6779	0.5251	0.1706	0.1583	0.7402	0.6226	0.4350	0.3521	0.8818	0.7808	0.0	0.0	0.6261	0.5819	0.4228	0.3712	0.8380	0.7086	0.2769	0.2508	0.5901	0.5056
1.1527	5.3846	700	1.2283	0.4439	0.5438	0.8194	0.7534	0.5451	0.0	0.0	0.7830	0.6225	0.6992	0.5202	0.8407	0.7294	0.9048	0.7682	0.7080	0.6110	0.8964	0.7260	0.0320	0.0308	0.4545	0.3475	0.0	0.0	0.0066	0.0066	0.8986	0.7972	0.0	0.0	0.6102	0.4012	0.9272	0.8219	0.8950	0.8090	0.9112	0.8390	0.0046	0.0046	0.7633	0.6267	0.8327	0.7275	0.5577	0.3200	0.6376	0.5398	0.1576	0.1565	0.4763	0.3636	0.5915	0.4882	0.8686	0.7558	0.7605	0.5886	0.5006	0.3788	0.4544	0.2629	0.5760	0.4244	0.3167	0.2888	0.5125	0.4312	0.8495	0.7385	0.9784	0.9340	0.7873	0.5824	0.5935	0.4218	0.7652	0.5779	0.3872	0.3138	0.8424	0.6600	0.6682	0.4264	0.8426	0.7719	0.0	0.0	0.8495	0.7286	0.5414	0.4668	0.8479	0.7322	0.3432	0.2468	0.6485	0.5474
1.0306	8.0769	1050	1.2373	0.4778	0.5835	0.8259	0.7637	0.5607	0.0	0.0	0.7995	0.6601	0.6911	0.5189	0.8487	0.7373	0.9026	0.7872	0.7499	0.6475	0.8925	0.7731	0.4930	0.3073	0.5245	0.3648	0.0	0.0	0.1802	0.1427	0.8959	0.8073	0.0	0.0	0.5135	0.3544	0.9315	0.8377	0.9221	0.8233	0.9403	0.8834	0.3472	0.3025	0.7562	0.6417	0.8479	0.7419	0.5338	0.2518	0.6645	0.5387	0.3634	0.3373	0.4991	0.3761	0.6004	0.4983	0.8767	0.7602	0.7459	0.6229	0.5432	0.4080	0.3388	0.2634	0.5659	0.4309	0.4032	0.3089	0.5708	0.3323	0.8689	0.7540	0.9807	0.9224	0.8098	0.6605	0.5377	0.3829	0.7587	0.5579	0.3974	0.3241	0.8100	0.6812	0.6984	0.4348	0.8556	0.7931	0.5547	0.5177	0.8250	0.7303	0.5936	0.4941	0.8765	0.7391	0.4286	0.3074	0.6411	0.5278
0.9703	10.7692	1400	1.2464	0.4907	0.6008	0.8294	0.7739	0.5549	0.0232	0.0232	0.8002	0.6520	0.6555	0.5485	0.8509	0.7462	0.8805	0.8024	0.7402	0.6242	0.8725	0.7398	0.4837	0.3257	0.5342	0.3800	0.0251	0.0187	0.4129	0.2118	0.9116	0.8098	0.0017	0.0017	0.8061	0.5785	0.9298	0.8464	0.9143	0.8406	0.9060	0.8483	0.0008	0.0008	0.7784	0.6434	0.8416	0.7472	0.6776	0.3659	0.6780	0.5241	0.4670	0.4288	0.5144	0.3854	0.6176	0.5324	0.8611	0.7652	0.7686	0.6403	0.5015	0.3688	0.4062	0.3156	0.6383	0.4484	0.4252	0.3270	0.5193	0.4086	0.8717	0.7611	0.9805	0.9372	0.7638	0.6919	0.5908	0.4164	0.7841	0.5591	0.4039	0.3255	0.8182	0.6797	0.7690	0.4500	0.8982	0.8017	0.6472	0.5221	0.8367	0.7554	0.6684	0.5360	0.8682	0.7441	0.4531	0.3349	0.6723	0.5489
0.9414	13.4615	1750	1.2509	0.4970	0.6058	0.8326	0.8012	0.5465	0.0794	0.0779	0.7838	0.6671	0.6953	0.5630	0.8502	0.7479	0.8810	0.7963	0.7789	0.6695	0.9206	0.7961	0.5850	0.4092	0.6083	0.3953	0.0914	0.0625	0.4195	0.2155	0.9055	0.8151	0.0078	0.0076	0.7318	0.4900	0.9282	0.8452	0.9177	0.8226	0.9509	0.8949	0.1713	0.1677	0.7878	0.6476	0.8497	0.7546	0.4791	0.2873	0.6926	0.5563	0.5018	0.4204	0.5405	0.3955	0.6375	0.5246	0.8797	0.7700	0.7613	0.6453	0.4686	0.3447	0.3749	0.2643	0.5743	0.4398	0.4413	0.3659	0.5196	0.3504	0.8786	0.7673	0.9831	0.9337	0.8460	0.6832	0.6506	0.4606	0.7314	0.5884	0.3570	0.2849	0.7877	0.6788	0.7179	0.4785	0.9001	0.8069	0.6680	0.5715	0.8470	0.7308	0.5698	0.4912	0.8698	0.7481	0.4420	0.3319	0.6334	0.5295
0.9197	16.1538	2100	1.2600	0.4981	0.6054	0.8322	0.7937	0.5361	0.1152	0.1122	0.8297	0.6824	0.7165	0.5820	0.8595	0.7502	0.8725	0.8040	0.7681	0.6631	0.9130	0.7795	0.5125	0.3129	0.5094	0.3725	0.1220	0.0938	0.4091	0.2477	0.9099	0.8133	0.0065	0.0065	0.8062	0.5320	0.9319	0.8400	0.9169	0.8146	0.8980	0.8465	0.3166	0.3091	0.7604	0.6501	0.8480	0.7590	0.6513	0.3434	0.6196	0.5385	0.2534	0.2242	0.4667	0.3769	0.6198	0.5394	0.8910	0.7679	0.7721	0.6417	0.5200	0.3969	0.3961	0.2743	0.6209	0.4413	0.4675	0.3924	0.5365	0.3644	0.8834	0.7759	0.9828	0.9328	0.7952	0.6939	0.6206	0.4192	0.7487	0.6078	0.3604	0.3082	0.7910	0.6795	0.7086	0.4392	0.8900	0.8037	0.6683	0.5607	0.8140	0.7218	0.6558	0.5536	0.8504	0.7468	0.4210	0.3305	0.6609	0.5197
0.9026	18.8462	2450	1.2574	0.5045	0.6150	0.8369	0.7980	0.5480	0.1304	0.1206	0.7914	0.7011	0.6699	0.5428	0.8575	0.7499	0.8974	0.8051	0.7769	0.6746	0.8688	0.7437	0.6134	0.3840	0.5852	0.4129	0.1929	0.1382	0.2605	0.1470	0.9039	0.8204	0.0014	0.0014	0.9502	0.6383	0.9295	0.8450	0.9196	0.8362	0.9134	0.8602	0.3919	0.3501	0.7691	0.6580	0.8539	0.7625	0.4220	0.2592	0.6974	0.5588	0.3573	0.3037	0.5522	0.4104	0.6475	0.5242	0.8852	0.7753	0.7657	0.6363	0.5590	0.3980	0.4234	0.3029	0.6011	0.4592	0.4294	0.3832	0.5505	0.3498	0.8883	0.7793	0.9869	0.9343	0.8372	0.6736	0.6068	0.4441	0.7807	0.6037	0.4122	0.3131	0.7965	0.6916	0.6886	0.4565	0.8831	0.7967	0.6761	0.5862	0.8454	0.7372	0.6385	0.5515	0.8802	0.7534	0.4353	0.3395	0.6573	0.4702
0.8895	21.5385	2800	1.2615	0.4994	0.6078	0.8370	0.7963	0.5425	0.1362	0.1210	0.7963	0.6561	0.6519	0.5517	0.8776	0.7490	0.8728	0.8117	0.7818	0.6620	0.8939	0.7461	0.6116	0.4114	0.5705	0.4056	0.1901	0.1352	0.4111	0.2298	0.9125	0.8207	0.0066	0.0066	0.8147	0.5561	0.9306	0.8468	0.9180	0.8297	0.9045	0.8537	0.3056	0.3012	0.7826	0.6628	0.8607	0.7672	0.4122	0.2360	0.6811	0.5543	0.2012	0.1849	0.5331	0.4034	0.6161	0.5343	0.8849	0.7751	0.7668	0.6419	0.5409	0.3953	0.3757	0.2925	0.6066	0.4561	0.4667	0.4043	0.5522	0.3176	0.8840	0.7811	0.9857	0.9400	0.8114	0.6441	0.6082	0.4403	0.7709	0.6092	0.4145	0.3318	0.8077	0.6940	0.7093	0.4429	0.8803	0.8020	0.6825	0.5925	0.8440	0.7501	0.5915	0.5052	0.8662	0.7535	0.4444	0.3372	0.6413	0.4838
0.8814	24.2308	3150	1.2769	0.5004	0.6148	0.8362	0.7903	0.5246	0.1365	0.1213	0.7691	0.6640	0.6856	0.5571	0.8557	0.7517	0.9043	0.8267	0.7855	0.6665	0.8670	0.7491	0.6039	0.4065	0.5531	0.4103	0.2147	0.1531	0.5806	0.2800	0.9060	0.8209	0.0039	0.0039	0.6857	0.4510	0.9256	0.8469	0.9203	0.8341	0.9108	0.8605	0.3482	0.3367	0.7850	0.6640	0.8552	0.7692	0.6409	0.3152	0.6517	0.5456	0.2289	0.2031	0.4966	0.3928	0.6263	0.5160	0.8900	0.7738	0.7566	0.6415	0.5661	0.4047	0.4364	0.3082	0.5801	0.4540	0.4623	0.3866	0.5538	0.3129	0.8884	0.7848	0.9844	0.9394	0.7624	0.6490	0.5922	0.4256	0.7840	0.5914	0.3905	0.3103	0.8410	0.6809	0.7842	0.4394	0.8697	0.7928	0.6988	0.5976	0.8499	0.7601	0.5767	0.4925	0.8596	0.7549	0.4680	0.3447	0.6406	0.5035
0.8748	26.9231	3500	1.2760	0.4994	0.6058	0.8380	0.7936	0.5127	0.1346	0.1187	0.7994	0.6642	0.6683	0.5547	0.8410	0.7536	0.8850	0.8154	0.7613	0.6586	0.8725	0.7361	0.5808	0.4131	0.5719	0.4142	0.2386	0.1447	0.3731	0.1814	0.9030	0.8238	0.0151	0.0150	0.7026	0.4812	0.9251	0.8473	0.9234	0.8315	0.9519	0.8996	0.3482	0.3333	0.7818	0.6628	0.8686	0.7701	0.3911	0.2235	0.6951	0.5605	0.2293	0.2062	0.5471	0.4048	0.6143	0.5218	0.8957	0.7738	0.7577	0.6453	0.5162	0.3995	0.4205	0.3128	0.5957	0.4549	0.4553	0.3654	0.5553	0.3680	0.8853	0.7861	0.9862	0.9349	0.8039	0.6324	0.6196	0.4339	0.7564	0.6048	0.3741	0.3031	0.8041	0.6976	0.6565	0.4383	0.8822	0.8075	0.6928	0.6042	0.8485	0.7452	0.5884	0.5032	0.8732	0.7596	0.4756	0.3392	0.6396	0.5113
0.8688	29.6154	3850	1.2755	0.4991	0.6114	0.8385	0.7997	0.5135	0.1397	0.1200	0.8078	0.6807	0.7024	0.5551	0.8590	0.7567	0.8942	0.8169	0.7874	0.6648	0.8574	0.7368	0.6243	0.4175	0.5846	0.4126	0.2692	0.1368	0.4074	0.1866	0.9145	0.8228	0.0129	0.0128	0.7673	0.4960	0.9264	0.8490	0.9095	0.8308	0.9274	0.8773	0.3618	0.3430	0.7585	0.6602	0.8624	0.7710	0.1998	0.1200	0.6486	0.5406	0.2555	0.2270	0.5602	0.4119	0.5922	0.5204	0.8851	0.7793	0.7619	0.6333	0.5317	0.3940	0.4587	0.3357	0.6230	0.4552	0.4793	0.4042	0.5455	0.3369	0.8908	0.7868	0.9863	0.9349	0.8560	0.6147	0.6274	0.4427	0.7838	0.6027	0.4211	0.3301	0.8070	0.6995	0.6733	0.4539	0.8847	0.8076	0.6996	0.5975	0.8452	0.7372	0.6077	0.5155	0.8811	0.7603	0.4632	0.3477	0.6485	0.5020
0.8655	32.3077	4200	1.2777	0.5026	0.6105	0.8394	0.7983	0.4889	0.1378	0.1201	0.8001	0.6866	0.6541	0.5537	0.8623	0.7619	0.8922	0.8168	0.7815	0.6680	0.8659	0.7296	0.5974	0.4076	0.5853	0.4132	0.2198	0.1205	0.3961	0.2063	0.9069	0.8249	0.0488	0.0481	0.7511	0.5107	0.9299	0.8481	0.9236	0.8394	0.9421	0.8876	0.3404	0.3263	0.7707	0.6644	0.8675	0.7705	0.3468	0.1995	0.6941	0.5609	0.2458	0.2136	0.5530	0.4072	0.6218	0.5270	0.8908	0.7767	0.7638	0.6464	0.5463	0.3996	0.4478	0.3272	0.6136	0.4571	0.4689	0.3903	0.5416	0.3641	0.8894	0.7874	0.9876	0.9362	0.7992	0.6272	0.6530	0.4658	0.7788	0.6123	0.3747	0.3087	0.8116	0.6974	0.6432	0.4469	0.8923	0.8112	0.7048	0.6017	0.8518	0.7520	0.5928	0.5025	0.8726	0.7601	0.4423	0.3451	0.6455	0.5179
0.8633	35.0	4550	1.2765	0.5009	0.6106	0.8394	0.8021	0.5070	0.1368	0.1193	0.8058	0.6864	0.6598	0.5546	0.8603	0.7578	0.8906	0.8162	0.7791	0.6669	0.8670	0.7344	0.6104	0.4213	0.5825	0.4130	0.2112	0.1126	0.4175	0.1773	0.9087	0.8244	0.0188	0.0186	0.8058	0.5454	0.9284	0.8493	0.9231	0.8390	0.9339	0.8824	0.3466	0.3317	0.7784	0.6660	0.8635	0.7714	0.2592	0.1469	0.6818	0.5593	0.2614	0.2267	0.5563	0.4095	0.6200	0.5235	0.8880	0.7774	0.7715	0.6504	0.5255	0.4005	0.4462	0.3285	0.6111	0.4572	0.4681	0.3950	0.5440	0.3335	0.8926	0.7874	0.9871	0.9382	0.7891	0.6033	0.6414	0.4548	0.7737	0.6170	0.3949	0.3128	0.8107	0.6989	0.6834	0.4486	0.8897	0.8062	0.6985	0.5933	0.8533	0.7524	0.5880	0.5043	0.8748	0.7607	0.4628	0.3472	0.6495	0.5174
0.8616	37.6923	4900	1.2754	0.5006	0.6100	0.8397	0.8005	0.4862	0.1387	0.1204	0.8096	0.6848	0.6607	0.5516	0.8598	0.7556	0.8983	0.8209	0.7842	0.6694	0.8636	0.7234	0.5984	0.4229	0.5865	0.4138	0.2056	0.1051	0.4230	0.2020	0.9106	0.8243	0.0271	0.0268	0.7920	0.5373	0.9280	0.8490	0.9232	0.8393	0.9405	0.8882	0.3523	0.3354	0.7808	0.6677	0.8621	0.7719	0.2156	0.1253	0.6739	0.5558	0.2593	0.2250	0.5549	0.4091	0.6199	0.5209	0.8861	0.7778	0.7714	0.6499	0.5282	0.4005	0.4393	0.3276	0.6138	0.4600	0.4728	0.3846	0.5452	0.3426	0.8902	0.7880	0.9877	0.9391	0.8017	0.6026	0.6541	0.4607	0.7723	0.6161	0.3786	0.3020	0.8121	0.6983	0.6859	0.4537	0.8886	0.8101	0.6979	0.5940	0.8540	0.7524	0.5916	0.5057	0.8780	0.7615	0.4560	0.3458	0.6458	0.5250

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 9

Safetensors

Model size

84.6M params

Tensor type

F32

Model tree for AllanK24/segformer-b5-finetuned-apple-dms-v2-run1

Base model

nvidia/segformer-b5-finetuned-ade-640-640

Finetuned

(22)

this model