parking-utcustom-train-SF-RGB-b5_2

This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/parking-utcustom-train dataset. It achieves the following results on the evaluation set:

• Loss: 0.1577
• Mean Iou: 0.4996
• Mean Accuracy: 0.9992
• Overall Accuracy: 0.9992
• Accuracy Unlabeled: nan
• Accuracy Parking: nan
• Accuracy Unparking: 0.9992
• Iou Unlabeled: nan
• Iou Parking: 0.0
• Iou Unparking: 0.9992

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Parking	Accuracy Unparking	Iou Unlabeled	Iou Parking	Iou Unparking
0.767	20.0	20	0.6634	0.3299	0.9898	0.9898	nan	nan	0.9898	0.0	0.0	0.9898
0.5036	40.0	40	0.3752	0.3316	0.9949	0.9949	nan	nan	0.9949	0.0	0.0	0.9949
0.3486	60.0	60	0.2976	0.3319	0.9958	0.9958	nan	nan	0.9958	0.0	0.0	0.9958
0.2729	80.0	80	0.2355	0.3326	0.9978	0.9978	nan	nan	0.9978	0.0	0.0	0.9978
0.2246	100.0	100	0.1822	0.4983	0.9966	0.9966	nan	nan	0.9966	nan	0.0	0.9966
0.2131	120.0	120	0.1577	0.4996	0.9992	0.9992	nan	nan	0.9992	nan	0.0	0.9992

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3