segformer-b5-seed42-outputs

This model is a fine-tuned version of nvidia/mit-b5 on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset. It achieves the following results on the evaluation set:

• Loss: 0.2833
• Mean Iou: 0.3430
• Mean Accuracy: 0.4050
• Overall Accuracy: 0.6546
• Accuracy Unlabeled: nan
• Accuracy Lv: 0.7625
• Accuracy Rv: 0.6171
• Accuracy Ra: 0.7072
• Accuracy La: 0.6623
• Accuracy Vs: 0.0
• Accuracy As: 0.0
• Accuracy Mk: 0.0227
• Accuracy Tk: nan
• Accuracy Asd: 0.3003
• Accuracy Vsd: 0.4268
• Accuracy Ak: 0.5517
• Iou Unlabeled: 0.0
• Iou Lv: 0.7175
• Iou Rv: 0.5629
• Iou Ra: 0.6665
• Iou La: 0.5980
• Iou Vs: 0.0
• Iou As: 0.0
• Iou Mk: 0.0207
• Iou Tk: nan
• Iou Asd: 0.2802
• Iou Vsd: 0.3970
• Iou Ak: 0.5307

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Lv	Accuracy Rv	Accuracy Ra	Accuracy La	Accuracy Vs	Accuracy As	Accuracy Mk	Accuracy Tk	Accuracy Asd	Accuracy Vsd	Accuracy Ak	Iou Unlabeled	Iou Lv	Iou Rv	Iou Ra	Iou La	Iou Vs	Iou As	Iou Mk	Iou Tk	Iou Asd	Iou Vsd	Iou Ak
0.4921	0.62	100	0.4897	0.0906	0.1245	0.3551	nan	0.7217	0.0098	0.0751	0.4344	0.0	0.0	0.0	nan	0.0	0.0	0.0043	0.0	0.5846	0.0097	0.0746	0.3230	0.0	0.0	0.0	nan	0.0	0.0	0.0043
0.3534	1.25	200	0.3565	0.2420	0.3017	0.5592	nan	0.7947	0.4293	0.5941	0.3492	0.0	0.0	0.0	nan	0.1737	0.1500	0.5262	0.0	0.7343	0.3850	0.3997	0.3289	0.0	0.0	0.0	nan	0.1681	0.1456	0.5007
0.4663	1.88	300	0.3434	0.3620	0.4497	0.7082	nan	0.8026	0.7564	0.6551	0.7392	0.0	0.0	0.0	nan	0.3118	0.5764	0.6560	0.0	0.7545	0.6572	0.6039	0.5921	0.0	0.0	0.0	nan	0.2819	0.4908	0.6019
0.1737	2.5	400	0.3055	0.3331	0.4090	0.6394	nan	0.7469	0.6281	0.5765	0.6122	0.0	0.0	0.0004	nan	0.2401	0.6135	0.6724	0.0	0.7075	0.5704	0.5194	0.5310	0.0	0.0	0.0003	nan	0.2292	0.5279	0.5789
0.1954	3.12	500	0.3052	0.2570	0.2980	0.5174	nan	0.6624	0.4973	0.4223	0.5361	0.0	0.0	0.0022	nan	0.1117	0.3193	0.4284	0.0	0.6289	0.4592	0.4113	0.4902	0.0	0.0	0.0022	nan	0.1107	0.3024	0.4216
0.2666	3.75	600	0.3177	0.3808	0.4720	0.7175	nan	0.7675	0.7191	0.8483	0.7341	0.0	0.0	0.0950	nan	0.3086	0.6065	0.6405	0.0	0.7200	0.6353	0.6912	0.6409	0.0	0.0	0.0845	nan	0.2905	0.5245	0.6024
0.2214	4.38	700	0.2971	0.3748	0.4463	0.7178	nan	0.8524	0.6207	0.7488	0.7353	0.0	0.0	0.025	nan	0.3236	0.5440	0.6130	0.0	0.7909	0.5707	0.6987	0.6564	0.0	0.0	0.0235	nan	0.3015	0.4902	0.5907
0.2624	5.0	800	0.2833	0.3430	0.4050	0.6546	nan	0.7625	0.6171	0.7072	0.6623	0.0	0.0	0.0227	nan	0.3003	0.4268	0.5517	0.0	0.7175	0.5629	0.6665	0.5980	0.0	0.0	0.0207	nan	0.2802	0.3970	0.5307
0.3578	5.62	900	0.2847	0.3329	0.3926	0.6257	nan	0.7276	0.5712	0.6573	0.6410	0.0016	0.0	0.0227	nan	0.3125	0.4450	0.5470	0.0	0.6860	0.5210	0.6234	0.5790	0.0015	0.0	0.0210	nan	0.2906	0.4122	0.5275
0.2736	6.25	1000	0.2861	0.3393	0.4010	0.6425	nan	0.7587	0.5808	0.6702	0.6477	0.0014	0.0	0.0244	nan	0.3087	0.4702	0.5477	0.0	0.7133	0.5292	0.6319	0.5844	0.0014	0.0	0.0225	nan	0.2877	0.4328	0.5295

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0