segformer-b0-drivable-area_segmentation
This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2781
- Mean Iou: 0.3551
- Mean Accuracy: 0.4409
- Overall Accuracy: 0.9059
- Per Category Iou: [0.16086782865321247, 0.904459670467452, 0.0]
- Per Category Accuracy: [0.39100126742712293, 0.9316527965687352, 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|---|---|---|---|---|---|---|---|---|
| 0.0717 | 2.44 | 100 | 0.3113 | 0.3302 | 0.3801 | 0.9033 | [0.08799146351473365, 0.902610436143179, 0.0] | [0.2023568751179786, 0.9379215030926394, 0.0] |
| 0.0079 | 4.88 | 200 | 0.2781 | 0.3551 | 0.4409 | 0.9059 | [0.16086782865321247, 0.904459670467452, 0.0] | [0.39100126742712293, 0.9316527965687352, 0.0] |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2
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