segformer-b0-finetuned-segments-ECHO-dev-05-v1

This model is a fine-tuned version of nvidia/mit-b0 on the unreal-hug/REAL_DATASET_SEG dataset. It achieves the following results on the evaluation set:

• Loss: 0.4592
• Mean Iou: 0.3826
• Mean Accuracy: 0.5892
• Overall Accuracy: 0.5467
• Accuracy Unlabeled: nan
• Accuracy Lv: 0.7143
• Accuracy Lr: 0.4323
• Accuracy Ra: 0.7629
• Accuracy La: 0.4472
• Iou Unlabeled: 0.0
• Iou Lv: 0.7065
• Iou Lr: 0.4317
• Iou Ra: 0.4223
• Iou La: 0.3527

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: 6e-05
• 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: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Lv	Accuracy Lr	Accuracy Ra	Accuracy La	Iou Unlabeled	Iou Lv	Iou Lr	Iou Ra	Iou La
1.1252	2.86	20	1.3259	0.1971	0.3379	0.4375	nan	0.0	0.6365	0.5291	0.1860	0.0	0.0	0.4923	0.3492	0.1439
0.9104	5.71	40	0.9589	0.1818	0.3421	0.3596	nan	0.0145	0.3590	0.7644	0.2304	0.0	0.0144	0.3436	0.3778	0.1731
0.7567	8.57	60	0.7761	0.2203	0.3739	0.3852	nan	0.0808	0.3882	0.6422	0.3844	0.0	0.0803	0.3778	0.4073	0.2360
0.7035	11.43	80	0.7442	0.2729	0.4718	0.4941	nan	0.2145	0.5077	0.8370	0.3279	0.0	0.2134	0.4817	0.4073	0.2619
0.5781	14.29	100	0.6260	0.2876	0.4446	0.4279	nan	0.4235	0.3777	0.5787	0.3986	0.0	0.3683	0.3761	0.4063	0.2873
0.5438	17.14	120	0.5559	0.3877	0.5412	0.5761	nan	0.5803	0.6504	0.5190	0.4149	0.0	0.5671	0.6193	0.4171	0.3352
0.5198	20.0	140	0.5617	0.3724	0.5617	0.5335	nan	0.6661	0.4532	0.7059	0.4216	0.0	0.6419	0.4532	0.4129	0.3540
0.4435	22.86	160	0.5393	0.4160	0.6198	0.6126	nan	0.7555	0.5832	0.6962	0.4442	0.0	0.7000	0.5705	0.4873	0.3221
0.5002	25.71	180	0.5126	0.4094	0.6080	0.6043	nan	0.6854	0.5833	0.6945	0.4687	0.0	0.6771	0.5761	0.4762	0.3176
0.4142	28.57	200	0.4874	0.3503	0.5361	0.4949	nan	0.6967	0.3895	0.6436	0.4147	0.0	0.6287	0.3895	0.4106	0.3228
0.3092	31.43	220	0.4819	0.3857	0.6001	0.5534	nan	0.7296	0.4267	0.8020	0.4423	0.0	0.7157	0.4267	0.4266	0.3595
0.2895	34.29	240	0.4969	0.3983	0.6220	0.5809	nan	0.7353	0.4689	0.8050	0.4787	0.0	0.7265	0.4677	0.4474	0.3498
0.3046	37.14	260	0.4767	0.4248	0.6412	0.6115	nan	0.7853	0.5270	0.7814	0.4711	0.0	0.7712	0.5199	0.4587	0.3742
0.3514	40.0	280	0.4531	0.3978	0.5989	0.5767	nan	0.7112	0.5082	0.7478	0.4282	0.0	0.6979	0.5024	0.4353	0.3537
0.2891	42.86	300	0.4629	0.3842	0.5885	0.5488	nan	0.7046	0.4397	0.7693	0.4403	0.0	0.6982	0.4366	0.4237	0.3623
0.2512	45.71	320	0.4584	0.3783	0.5794	0.5357	nan	0.7144	0.4199	0.7390	0.4443	0.0	0.7016	0.4196	0.4134	0.3568
0.2695	48.57	340	0.4592	0.3826	0.5892	0.5467	nan	0.7143	0.4323	0.7629	0.4472	0.0	0.7065	0.4317	0.4223	0.3527

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0