README.md

metadata

license: other
tags:
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-toolwear
    results: []

segformer-b0-finetuned-segments-toolwear

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

• Loss: 0.0236
• Mean Iou: 0.4952
• Mean Accuracy: 0.9904
• Overall Accuracy: 0.9904
• Accuracy Unlabeled: nan
• Accuracy Tool: 0.9904
• Iou Unlabeled: 0.0
• Iou Tool: 0.9904

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 Tool	Iou Unlabeled	Iou Tool
0.1696	1.18	20	0.3490	0.4962	0.9924	0.9924	nan	0.9924	0.0	0.9924
0.1045	2.35	40	0.0977	0.4878	0.9755	0.9755	nan	0.9755	0.0	0.9755
0.0871	3.53	60	0.0650	0.4952	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.0542	4.71	80	0.0652	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0507	5.88	100	0.0573	0.4952	0.9905	0.9905	nan	0.9905	0.0	0.9905
0.0708	7.06	120	0.0510	0.4941	0.9883	0.9883	nan	0.9883	0.0	0.9883
0.0455	8.24	140	0.0487	0.4892	0.9784	0.9784	nan	0.9784	0.0	0.9784
0.0489	9.41	160	0.0430	0.4934	0.9867	0.9867	nan	0.9867	0.0	0.9867
0.048	10.59	180	0.0409	0.4940	0.9880	0.9880	nan	0.9880	0.0	0.9880
0.0476	11.76	200	0.0347	0.4965	0.9931	0.9931	nan	0.9931	0.0	0.9931
0.048	12.94	220	0.0366	0.4972	0.9944	0.9944	nan	0.9944	0.0	0.9944
0.0242	14.12	240	0.0342	0.4963	0.9926	0.9926	nan	0.9926	0.0	0.9926
0.0276	15.29	260	0.0305	0.4967	0.9934	0.9934	nan	0.9934	0.0	0.9934
0.0192	16.47	280	0.0318	0.4956	0.9913	0.9913	nan	0.9913	0.0	0.9913
0.0382	17.65	300	0.0285	0.4964	0.9929	0.9929	nan	0.9929	0.0	0.9929
0.0244	18.82	320	0.0282	0.4952	0.9904	0.9904	nan	0.9904	0.0	0.9904
0.0272	20.0	340	0.0269	0.4955	0.9911	0.9911	nan	0.9911	0.0	0.9911
0.0174	21.18	360	0.0281	0.4955	0.9910	0.9910	nan	0.9910	0.0	0.9910
0.0276	22.35	380	0.0270	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0268	23.53	400	0.0272	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0373	24.71	420	0.0253	0.4939	0.9878	0.9878	nan	0.9878	0.0	0.9878
0.023	25.88	440	0.0264	0.4935	0.9869	0.9869	nan	0.9869	0.0	0.9869
0.0183	27.06	460	0.0257	0.4960	0.9919	0.9919	nan	0.9919	0.0	0.9919
0.024	28.24	480	0.0256	0.4950	0.9900	0.9900	nan	0.9900	0.0	0.9900
0.0145	29.41	500	0.0246	0.4956	0.9911	0.9911	nan	0.9911	0.0	0.9911
0.0158	30.59	520	0.0251	0.4947	0.9894	0.9894	nan	0.9894	0.0	0.9894
0.017	31.76	540	0.0247	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.018	32.94	560	0.0237	0.4965	0.9930	0.9930	nan	0.9930	0.0	0.9930
0.0161	34.12	580	0.0238	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0191	35.29	600	0.0241	0.4950	0.9901	0.9901	nan	0.9901	0.0	0.9901
0.0133	36.47	620	0.0240	0.4956	0.9912	0.9912	nan	0.9912	0.0	0.9912
0.0118	37.65	640	0.0244	0.4949	0.9897	0.9897	nan	0.9897	0.0	0.9897
0.0133	38.82	660	0.0229	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0199	40.0	680	0.0236	0.4958	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0167	41.18	700	0.0234	0.4961	0.9922	0.9922	nan	0.9922	0.0	0.9922
0.0119	42.35	720	0.0234	0.4957	0.9915	0.9915	nan	0.9915	0.0	0.9915
0.0154	43.53	740	0.0243	0.4950	0.9901	0.9901	nan	0.9901	0.0	0.9901
0.0126	44.71	760	0.0242	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.0128	45.88	780	0.0243	0.4955	0.9911	0.9911	nan	0.9911	0.0	0.9911
0.0116	47.06	800	0.0239	0.4953	0.9907	0.9907	nan	0.9907	0.0	0.9907
0.0121	48.24	820	0.0239	0.4954	0.9909	0.9909	nan	0.9909	0.0	0.9909
0.0165	49.41	840	0.0236	0.4952	0.9904	0.9904	nan	0.9904	0.0	0.9904

Framework versions

• Transformers 4.28.0
• Pytorch 2.1.0+cu121
• Datasets 2.16.0
• Tokenizers 0.13.3