segformer-b5-finetuned-apple-dms-v2-run2

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: 1.1816
Mean Iou: 0.5168
Mean Accuracy: 0.6165
Overall Accuracy: 0.8368
Accuracy Animal Skin: 0.8455
Iou Animal Skin: 0.6188
Accuracy Bone Teeth Horn: 0.0931
Iou Bone Teeth Horn: 0.0892
Accuracy Brickwork: 0.7976
Iou Brickwork: 0.6377
Accuracy Cardboard: 0.7038
Iou Cardboard: 0.5829
Accuracy Carpet Rug: 0.8714
Iou Carpet Rug: 0.7699
Accuracy Ceiling Tile: 0.8745
Iou Ceiling Tile: 0.7971
Accuracy Ceramic: 0.7644
Iou Ceramic: 0.6403
Accuracy Chalkboard Blackboard: 0.8354
Iou Chalkboard Blackboard: 0.7216
Accuracy Clutter: 0.6474
Iou Clutter: 0.4418
Accuracy Concrete: 0.5971
Iou Concrete: 0.4432
Accuracy Cork Corkboard: 0.1812
Iou Cork Corkboard: 0.1587
Accuracy Engineered Stone: 0.1564
Iou Engineered Stone: 0.1235
Accuracy Fabric Cloth: 0.9101
Iou Fabric Cloth: 0.8157
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.8079
Iou Fire: 0.5896
Accuracy Foliage: 0.9207
Iou Foliage: 0.8361
Accuracy Food: 0.9126
Iou Food: 0.8083
Accuracy Fur: 0.9457
Iou Fur: 0.9007
Accuracy Gemstone Quartz: 0.4169
Iou Gemstone Quartz: 0.3774
Accuracy Glass: 0.7759
Iou Glass: 0.6581
Accuracy Hair: 0.8560
Iou Hair: 0.7465
Accuracy Ice: 0.7561
Iou Ice: 0.5848
Accuracy Leather: 0.6776
Iou Leather: 0.5701
Accuracy Liquid Non-water: 0.3126
Iou Liquid Non-water: 0.2938
Accuracy Metal: 0.5541
Iou Metal: 0.4088
Accuracy Mirror: 0.6526
Iou Mirror: 0.5701
Accuracy Paint Plaster Enamel: 0.8851
Iou Paint Plaster Enamel: 0.7697
Accuracy Paper: 0.7511
Iou Paper: 0.6369
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.5312
Iou Photograph Painting: 0.3893
Accuracy Plastic Clear: 0.3886
Iou Plastic Clear: 0.2666
Accuracy Plastic Non-clear: 0.5812
Iou Plastic Non-clear: 0.4455
Accuracy Rubber Latex: 0.4111
Iou Rubber Latex: 0.3570
Accuracy Sand: 0.6490
Iou Sand: 0.4828
Accuracy Skin Lips: 0.8694
Iou Skin Lips: 0.7614
Accuracy Sky: 0.9828
Iou Sky: 0.9506
Accuracy Snow: 0.7912
Iou Snow: 0.7119
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.6244
Iou Soil Mud: 0.4812
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.7767
Iou Stone Natural: 0.5933
Accuracy Stone Polished: 0.4189
Iou Stone Polished: 0.3580
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8246
Iou Tile: 0.6969
Accuracy Wallpaper: 0.6797
Iou Wallpaper: 0.4607
Accuracy Water: 0.9246
Iou Water: 0.8367
Accuracy Wax: 0.6517
Iou Wax: 0.5691
Accuracy Whiteboard: 0.8631
Iou Whiteboard: 0.7922
Accuracy Wicker: 0.5960
Iou Wicker: 0.5027
Accuracy Wood: 0.8617
Iou Wood: 0.7449
Accuracy Wood Tree: 0.4590
Iou Wood Tree: 0.3484
Accuracy Asphalt: 0.6704
Iou Asphalt: 0.5317

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 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
2.1540	2.6923	350	1.3162	0.3595	0.4276	0.7877	0.1907	0.1823	0.0	0.0	0.6163	0.5204	0.5324	0.4513	0.7533	0.6621	0.8424	0.7453	0.7311	0.5837	0.5353	0.5026	0.0	0.0	0.4276	0.3011	0.0	0.0	0.0	0.0	0.8814	0.7562	0.0	0.0	0.9171	0.8019	0.9017	0.7750	0.8738	0.7639	0.0	0.0	0.7236	0.5678	0.7914	0.6708	0.0002	0.0002	0.3705	0.3342	0.0	0.0	0.3668	0.2942	0.4737	0.4373	0.8892	0.7202	0.7236	0.5531	0.4786	0.3219	0.1402	0.1159	0.4622	0.3456	0.0032	0.0032	0.4118	0.3285	0.7996	0.6751	0.9608	0.9184	0.4316	0.3699	0.4846	0.3333	0.6693	0.4964	0.1121	0.1081	0.7456	0.6003	0.3334	0.2932	0.8733	0.7836	0.0	0.0	0.7114	0.5961	0.4763	0.4145	0.8312	0.6889	0.2100	0.1966	0.5560	0.4809
1.3332	5.3846	700	1.2432	0.4272	0.5210	0.8081	0.7385	0.5663	0.0	0.0	0.6838	0.5890	0.6093	0.5297	0.8407	0.7188	0.8815	0.7504	0.7226	0.5979	0.8751	0.7465	0.0	0.0	0.5194	0.3764	0.0	0.0	0.0	0.0	0.8717	0.7776	0.2800	0.2734	0.9182	0.8176	0.8973	0.7755	0.9095	0.8295	0.0	0.0	0.7590	0.5868	0.8331	0.7058	0.3851	0.2892	0.7456	0.4475	0.0386	0.0386	0.4501	0.3505	0.6224	0.4357	0.8632	0.7441	0.7539	0.5753	0.5015	0.3547	0.3100	0.2030	0.5046	0.3816	0.3568	0.3134	0.7259	0.4003	0.8335	0.7078	0.9660	0.9360	0.7374	0.6092	0.4403	0.3770	0.7546	0.5854	0.3130	0.2619	0.7804	0.6455	0.5522	0.4201	0.9055	0.7897	0.0	0.0	0.8648	0.7462	0.5154	0.4297	0.8691	0.7187	0.3404	0.2821	0.6197	0.5280
1.2446	8.0769	1050	1.2176	0.4600	0.5508	0.8177	0.7691	0.5561	0.0	0.0	0.7348	0.6067	0.6546	0.5594	0.8130	0.7269	0.8462	0.7781	0.7535	0.6005	0.8812	0.7698	0.0503	0.0499	0.5072	0.3876	0.0	0.0	0.0000	0.0000	0.8860	0.7949	0.5071	0.4357	0.9258	0.8278	0.8784	0.7995	0.9201	0.8670	0.1267	0.1236	0.7415	0.6113	0.8461	0.7107	0.7660	0.4239	0.6834	0.5215	0.3542	0.3517	0.5494	0.3815	0.5424	0.4851	0.8850	0.7499	0.7395	0.6073	0.5071	0.3431	0.3162	0.2301	0.5082	0.4007	0.3566	0.3175	0.6144	0.4928	0.8551	0.7265	0.9581	0.9360	0.5541	0.5444	0.5442	0.4209	0.7566	0.5170	0.3190	0.2647	0.7862	0.6606	0.6297	0.4668	0.9033	0.7883	0.3082	0.3039	0.8525	0.7220	0.5388	0.4584	0.8635	0.7275	0.4613	0.3274	0.6492	0.5474
1.1962	10.7692	1400	1.2087	0.4609	0.5526	0.8209	0.7662	0.5936	0.0157	0.0157	0.7968	0.5810	0.7614	0.5146	0.8574	0.7357	0.8145	0.7642	0.7353	0.6262	0.8395	0.7272	0.2780	0.2579	0.5620	0.4071	0.0	0.0	0.0145	0.0135	0.8881	0.7989	0.6578	0.5183	0.9244	0.8362	0.9066	0.8334	0.9391	0.8840	0.0040	0.0031	0.7256	0.6132	0.8246	0.7256	0.3107	0.2797	0.6827	0.5386	0.0726	0.0726	0.4962	0.3822	0.6112	0.5226	0.8964	0.7524	0.7012	0.6031	0.5205	0.3632	0.2621	0.2034	0.5234	0.4011	0.4051	0.3347	0.4488	0.3675	0.8548	0.7364	0.9660	0.9357	0.8406	0.5291	0.6723	0.4766	0.7643	0.5567	0.2803	0.2599	0.7920	0.6709	0.5039	0.4021	0.8852	0.8169	0.6481	0.5788	0.8535	0.7485	0.5423	0.4384	0.8589	0.7268	0.4263	0.2966	0.6057	0.5250
1.1603	13.4615	1750	1.2011	0.4909	0.5964	0.8238	0.7953	0.5342	0.0321	0.0321	0.7265	0.6229	0.7325	0.5730	0.8569	0.7450	0.8510	0.7804	0.7798	0.6398	0.8942	0.7608	0.4514	0.3626	0.6380	0.4009	0.0	0.0	0.0967	0.0854	0.9026	0.8050	0.8603	0.7023	0.9052	0.8304	0.9164	0.7901	0.9510	0.8785	0.3379	0.3019	0.7031	0.6162	0.8447	0.7281	0.3785	0.3610	0.6847	0.5490	0.4601	0.4221	0.5223	0.3902	0.6297	0.4905	0.8702	0.7588	0.7382	0.6161	0.5703	0.3819	0.2965	0.2193	0.5728	0.4236	0.3979	0.3549	0.5852	0.3792	0.8696	0.7431	0.9644	0.9415	0.8767	0.5709	0.6020	0.4383	0.7840	0.5777	0.4429	0.3536	0.7768	0.6741	0.7169	0.4403	0.8977	0.8112	0.6558	0.5476	0.8368	0.7794	0.6201	0.5309	0.8912	0.7275	0.4325	0.3025	0.6655	0.5530
1.1330	16.1538	2100	1.1925	0.5014	0.5943	0.8285	0.8487	0.5849	0.0468	0.0467	0.7237	0.6457	0.6999	0.5574	0.8479	0.7573	0.8587	0.7952	0.7076	0.6161	0.8383	0.7077	0.4581	0.3441	0.6075	0.4254	0.0	0.0	0.1523	0.1170	0.8962	0.8102	0.8098	0.6989	0.9002	0.8291	0.9183	0.8210	0.9244	0.8805	0.1041	0.0718	0.7568	0.6294	0.8416	0.7347	0.6916	0.5570	0.6831	0.5560	0.6546	0.6268	0.5135	0.3859	0.5856	0.5099	0.8971	0.7597	0.7605	0.6284	0.5210	0.3822	0.2945	0.2218	0.5288	0.4169	0.4050	0.3628	0.6957	0.4805	0.8612	0.7473	0.9749	0.9495	0.7626	0.7120	0.6029	0.4524	0.7640	0.5758	0.3573	0.3194	0.7939	0.6815	0.6475	0.4695	0.9313	0.8202	0.6150	0.5379	0.8044	0.7629	0.6261	0.5230	0.8592	0.7335	0.4318	0.2913	0.7022	0.5363
1.1093	18.8462	2450	1.1892	0.5004	0.5979	0.8284	0.8219	0.5991	0.0520	0.0515	0.7571	0.6304	0.7172	0.5623	0.8513	0.7524	0.8610	0.7849	0.7374	0.6401	0.9167	0.7344	0.5957	0.4430	0.5425	0.3965	0.1178	0.1178	0.1275	0.1093	0.9103	0.8051	0.8223	0.6600	0.9154	0.8271	0.9049	0.8036	0.9424	0.8934	0.2846	0.2533	0.7648	0.6419	0.8403	0.7346	0.6323	0.4918	0.5781	0.5375	0.5029	0.4697	0.5113	0.3895	0.5953	0.5397	0.8847	0.7619	0.7672	0.6236	0.4817	0.3665	0.4045	0.2530	0.5788	0.4368	0.4139	0.3635	0.7256	0.4134	0.8687	0.7491	0.9792	0.9467	0.7850	0.6874	0.5376	0.4250	0.6434	0.5046	0.4103	0.3425	0.8258	0.6778	0.6264	0.4468	0.9067	0.8253	0.6500	0.5771	0.8600	0.7475	0.5512	0.4707	0.8536	0.7392	0.3468	0.2770	0.6892	0.5185
1.0900	21.5385	2800	1.1883	0.4960	0.5990	0.8312	0.8118	0.5922	0.0640	0.0617	0.8030	0.6400	0.7190	0.5750	0.8756	0.7472	0.8821	0.7946	0.7587	0.6438	0.9066	0.7319	0.6375	0.4475	0.5662	0.4217	0.1494	0.1357	0.1296	0.1081	0.9118	0.8063	0.8497	0.6169	0.9168	0.8310	0.9137	0.8065	0.9468	0.8953	0.3993	0.2981	0.7640	0.6452	0.8618	0.7399	0.6067	0.5008	0.6812	0.5719	0.2382	0.2248	0.5463	0.4041	0.5805	0.5147	0.8757	0.7692	0.7566	0.6282	0.5390	0.3694	0.3934	0.2519	0.5778	0.4369	0.4266	0.3451	0.5398	0.4058	0.8530	0.7510	0.9787	0.9454	0.7872	0.6761	0.6355	0.4499	0.7958	0.5624	0.4390	0.3639	0.7877	0.6881	0.6974	0.4749	0.8888	0.8117	0.3381	0.3014	0.8437	0.7633	0.5867	0.4596	0.8750	0.7378	0.3958	0.3115	0.6153	0.5322
1.0743	24.2308	3150	1.1888	0.5138	0.6143	0.8321	0.8854	0.6357	0.0698	0.0677	0.7679	0.6615	0.6769	0.5662	0.8400	0.7498	0.8891	0.7868	0.7627	0.6284	0.8264	0.7359	0.6769	0.4466	0.5356	0.4145	0.1509	0.1509	0.1337	0.1108	0.9020	0.8112	0.8954	0.5948	0.9182	0.8350	0.9059	0.8065	0.9325	0.8929	0.2664	0.2189	0.7877	0.6529	0.8616	0.7416	0.7692	0.5749	0.6694	0.5624	0.7389	0.7040	0.5169	0.3959	0.6576	0.5387	0.8841	0.7663	0.7427	0.6246	0.5264	0.3863	0.3738	0.2454	0.5840	0.4337	0.4150	0.3509	0.6353	0.5022	0.8554	0.7548	0.9818	0.9446	0.7618	0.7092	0.5604	0.4303	0.8187	0.5886	0.4089	0.3302	0.8430	0.6842	0.6717	0.4638	0.9269	0.8359	0.5963	0.5195	0.8243	0.7786	0.5441	0.4740	0.8481	0.7405	0.4014	0.3130	0.7023	0.5540
1.0588	26.9231	3500	1.1853	0.5067	0.6022	0.8342	0.8444	0.6675	0.0691	0.0681	0.7944	0.6676	0.7074	0.5604	0.8630	0.7555	0.8814	0.7957	0.7624	0.6461	0.8593	0.7227	0.6232	0.4798	0.6358	0.4509	0.1709	0.1708	0.1639	0.1294	0.8981	0.8139	0.8497	0.5912	0.9177	0.8340	0.9149	0.7852	0.9464	0.9012	0.2581	0.2423	0.7787	0.6505	0.8496	0.7444	0.4549	0.3838	0.6908	0.5722	0.3312	0.3109	0.5384	0.4013	0.6608	0.5628	0.8918	0.7672	0.7400	0.6291	0.4774	0.3811	0.3675	0.2602	0.5878	0.4418	0.4079	0.3608	0.6530	0.4832	0.8710	0.7584	0.9823	0.9476	0.8005	0.6360	0.5631	0.4470	0.8015	0.5818	0.3620	0.3253	0.7901	0.6896	0.6149	0.4504	0.9355	0.8523	0.6599	0.5825	0.8505	0.7616	0.5463	0.4587	0.8598	0.7433	0.4279	0.3369	0.6607	0.5436
1.0469	29.6154	3850	1.1874	0.5111	0.6127	0.8357	0.8812	0.6225	0.0831	0.0807	0.7840	0.6413	0.6960	0.5684	0.8470	0.7538	0.8871	0.7985	0.7695	0.6377	0.8224	0.7288	0.6314	0.4397	0.5878	0.4405	0.1738	0.1627	0.1410	0.1201	0.9128	0.8125	0.8426	0.5515	0.9184	0.8367	0.8998	0.8011	0.9370	0.8961	0.4115	0.3027	0.7730	0.6545	0.8508	0.7453	0.7684	0.5643	0.6687	0.5764	0.3822	0.3648	0.5432	0.4094	0.6069	0.5445	0.8853	0.7694	0.7698	0.6355	0.5152	0.3898	0.3558	0.2529	0.5692	0.4435	0.4231	0.3498	0.6746	0.5000	0.8748	0.7576	0.9807	0.9508	0.7596	0.6925	0.6116	0.4758	0.7968	0.5894	0.3960	0.3497	0.8336	0.6859	0.6922	0.4616	0.9334	0.8495	0.5224	0.4512	0.8497	0.7821	0.5815	0.4960	0.8610	0.7457	0.4960	0.3649	0.6564	0.5279
1.0386	32.3077	4200	1.1855	0.5119	0.6130	0.8361	0.8635	0.6096	0.0924	0.0895	0.7936	0.6379	0.7006	0.5757	0.8687	0.7631	0.8811	0.7987	0.7746	0.6407	0.8653	0.7398	0.6436	0.4440	0.5965	0.4505	0.2007	0.1640	0.1289	0.1046	0.9095	0.8143	0.8485	0.5926	0.9212	0.8365	0.9105	0.8074	0.9433	0.8987	0.3076	0.2946	0.7672	0.6542	0.8576	0.7453	0.7719	0.5424	0.6766	0.5670	0.2437	0.2309	0.5345	0.4032	0.6260	0.5583	0.8881	0.7698	0.7460	0.6341	0.5150	0.3879	0.3941	0.2641	0.5975	0.4480	0.4259	0.3603	0.6352	0.4894	0.8723	0.7602	0.9813	0.9514	0.7410	0.6706	0.5851	0.4609	0.7717	0.5922	0.4056	0.3521	0.8379	0.6933	0.6815	0.4682	0.9325	0.8464	0.6420	0.5631	0.8722	0.7798	0.5860	0.4960	0.8501	0.7427	0.4686	0.3513	0.7167	0.5723
1.0339	35.0	4550	1.1825	0.5160	0.6146	0.8369	0.8430	0.6195	0.0890	0.0862	0.7965	0.6339	0.7122	0.5851	0.8715	0.7673	0.8753	0.7968	0.7658	0.6385	0.8347	0.7257	0.6331	0.4511	0.5791	0.4444	0.1756	0.1488	0.1943	0.1407	0.9053	0.8158	0.8101	0.6117	0.9196	0.8380	0.9113	0.8075	0.9468	0.9007	0.3452	0.3211	0.7852	0.6572	0.8617	0.7447	0.7177	0.5538	0.6860	0.5713	0.3150	0.2997	0.5468	0.4070	0.6485	0.5694	0.8876	0.7699	0.7518	0.6364	0.5294	0.3850	0.3767	0.2599	0.5779	0.4433	0.4158	0.3581	0.6537	0.4845	0.8712	0.7610	0.9812	0.9506	0.7928	0.6925	0.6122	0.4806	0.7706	0.5900	0.3966	0.3397	0.8209	0.6969	0.6764	0.4633	0.9293	0.8421	0.6442	0.5668	0.8602	0.7953	0.5887	0.5007	0.8630	0.7469	0.4509	0.3498	0.7382	0.5806
1.0281	37.6923	4900	1.1816	0.5168	0.6165	0.8368	0.8455	0.6188	0.0931	0.0892	0.7976	0.6377	0.7038	0.5829	0.8714	0.7699	0.8745	0.7971	0.7644	0.6403	0.8354	0.7216	0.6474	0.4418	0.5971	0.4432	0.1812	0.1587	0.1564	0.1235	0.9101	0.8157	0.8079	0.5896	0.9207	0.8361	0.9126	0.8083	0.9457	0.9007	0.4169	0.3774	0.7759	0.6581	0.8560	0.7465	0.7561	0.5848	0.6776	0.5701	0.3126	0.2938	0.5541	0.4088	0.6526	0.5701	0.8851	0.7697	0.7511	0.6369	0.5312	0.3893	0.3886	0.2666	0.5812	0.4455	0.4111	0.3570	0.6490	0.4828	0.8694	0.7614	0.9828	0.9506	0.7912	0.7119	0.6244	0.4812	0.7767	0.5933	0.4189	0.3580	0.8246	0.6969	0.6797	0.4607	0.9246	0.8367	0.6517	0.5691	0.8631	0.7922	0.5960	0.5027	0.8617	0.7449	0.4590	0.3484	0.6704	0.5317

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 8

Safetensors

Model size

84.6M params

Tensor type

F32

Model tree for AllanK24/segformer-b5-finetuned-apple-dms-v2-run2

Base model

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

Finetuned

(22)

this model