mobilevit-small-10k-steps

This model is a fine-tuned version of apple/deeplabv3-mobilevit-small on the Efferbach/lane_master2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0821
• Mean Iou: 0.0
• Mean Accuracy: 0.0
• Overall Accuracy: 0.0
• Accuracy Background: nan
• Accuracy Left: 0.0
• Accuracy Right: 0.0
• Iou Background: 0.0
• Iou Left: 0.0
• Iou Right: 0.0

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: polynomial
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Left	Accuracy Right	Iou Background	Iou Left	Iou Right
0.5041	1.0	385	0.3382	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.1553	2.0	770	0.1387	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.1019	3.0	1155	0.1037	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0882	4.0	1540	0.0883	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0828	5.0	1925	0.0823	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0807	6.0	2310	0.0820	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0795	7.0	2695	0.0804	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0786	8.0	3080	0.0784	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0777	9.0	3465	0.0786	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0771	10.0	3850	0.0774	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0773	11.0	4235	0.0775	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0765	12.0	4620	0.0782	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0757	13.0	5005	0.0775	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0756	14.0	5390	0.0774	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0754	15.0	5775	0.0775	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0746	16.0	6160	0.0775	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.074	17.0	6545	0.0779	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0736	18.0	6930	0.0792	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0737	19.0	7315	0.0801	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.073	20.0	7700	0.0804	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0729	21.0	8085	0.0805	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0734	22.0	8470	0.0804	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0726	23.0	8855	0.0811	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0726	24.0	9240	0.0816	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0721	25.0	9625	0.0822	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0
0.0727	25.97	10000	0.0821	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0

Framework versions

• Transformers 4.28.0.dev0
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3