segformer-b0-scene-parse-150-50

This model is a fine-tuned version of klentree/segformer-b0-scene-parse-150-20 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.9992
• Mean Iou: 0.1414
• Mean Accuracy: 0.5623
• Overall Accuracy: 0.2681
• Per Category Iou: [0.03415709043952773, 0.2486167920519975]
• Per Category Accuracy: [0.8749923648579864, 0.24956422933712585]

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
No log	1.0	133	1.3809	0.1831	0.6100	0.3452	[0.038710505981588275, 0.3274145847863719]	[0.8914028300926398, 0.32849851170741207]
No log	2.0	266	3.1399	0.0278	0.5071	0.0541	[0.02998980464207664, 0.02561876180112447]	[0.9885405001866368, 0.025627711476269972]
No log	3.0	399	1.4873	0.1612	0.5930	0.3045	[0.036857115032605504, 0.28551314243518766]	[0.8996386032780209, 0.28638666931766954]
0.131	4.0	532	1.8598	0.0884	0.5443	0.1683	[0.03248204249077747, 0.14439283966619093]	[0.9438817061997353, 0.14463986050373331]
0.131	5.0	665	1.1300	0.2009	0.6049	0.3790	[0.03869589759308316, 0.3630560542281187]	[0.8450235841053311, 0.3647712858322449]
0.131	6.0	798	3.4677	0.0281	0.5067	0.0547	[0.029968966006210712, 0.026287373046977498]	[0.9871950184940107, 0.02629763451787782]
0.131	7.0	931	0.8035	0.3046	0.6395	0.5715	[0.0468379925244376, 0.5622884059713553]	[0.7117632087956836, 0.567229146162601]
0.1093	8.0	1064	3.0148	0.0372	0.5090	0.0717	[0.03010087836424667, 0.04420013882895897]	[0.9738284298754624, 0.0442354032603611]
0.1093	9.0	1197	1.3918	0.1977	0.6147	0.3726	[0.03949337936321034, 0.35595848950467285]	[0.8720570769282975, 0.35734683993080635]
0.1093	10.0	1330	0.8904	0.3048	0.6555	0.5705	[0.04885666809307599, 0.5607852599315016]	[0.7458023685907225, 0.5651308780936614]
0.1093	11.0	1463	2.1101	0.1091	0.5544	0.2072	[0.03330984189332642, 0.18492787307291492]	[0.9234432793783298, 0.18535946088367358]
0.0978	12.0	1596	0.3487	0.4522	0.6510	0.8320	[0.07471728817756591, 0.8296494449285194]	[0.4586853982150735, 0.8433402139408958]
0.0978	13.0	1729	1.0341	0.2902	0.6500	0.5437	[0.0471396698931736, 0.5331783341429923]	[0.7630459126539754, 0.5370297478519739]
0.0978	14.0	1862	8.8510	0.0191	0.5026	0.0378	[0.029729543963973613, 0.008555530995168384]	[0.9966099969459432, 0.008556415154495053]
0.0978	15.0	1995	1.5468	0.2099	0.6104	0.3958	[0.039432935991880434, 0.38042719301704037]	[0.8384437883877973, 0.38230080082492807]
0.0938	16.0	2128	0.3316	0.4622	0.6499	0.8478	[0.07871638020749482, 0.8457624711227079]	[0.43967728799755673, 0.8602092195727595]
0.0938	17.0	2261	6.8985	0.0200	0.5030	0.0394	[0.02975190949797357, 0.010246433961607446]	[0.9957090501883336, 0.01024777428517577]
0.0938	18.0	2394	3.6067	0.0460	0.5068	0.0885	[0.02995386117285437, 0.06208246891194747]	[0.9514455868879161, 0.062174361643823145]
0.092	19.0	2527	0.2255	0.4956	0.6091	0.9069	[0.08506947955817622, 0.9060937507594746]	[0.29259221554854253, 0.9256338355411345]
0.092	20.0	2660	1.9992	0.1414	0.5623	0.2681	[0.03415709043952773, 0.2486167920519975]	[0.8749923648579864, 0.24956422933712585]

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0