đ segformer-trainer-test-bis
This model is a fine - tuned version of [nvidia/mit - b0](https://huggingface.co/nvidia/mit - b0) for image segmentation, offering specific performance metrics on the evaluation set.
đ Quick Start
This model, segformer - trainer - test - bis
, is a fine - tuned version of [nvidia/mit - b0](https://huggingface.co/nvidia/mit - b0) on the segments/sidewalk - semantic dataset. It achieves the following results on the evaluation set:
Metric |
Value |
Loss |
1.3784 |
Mean Iou |
0.1424 |
Mean Accuracy |
0.1896 |
Overall Accuracy |
0.7288 |
Accuracy Unlabeled |
nan |
Accuracy Flat - road |
0.6651 |
Accuracy Flat - sidewalk |
0.9129 |
Accuracy Flat - crosswalk |
0.0 |
Accuracy Flat - cyclinglane |
0.5829 |
Accuracy Flat - parkingdriveway |
0.0184 |
Accuracy Flat - railtrack |
0.0 |
Accuracy Flat - curb |
0.0 |
Accuracy Human - person |
0.0 |
Accuracy Human - rider |
0.0 |
Accuracy Vehicle - car |
0.8322 |
Accuracy Vehicle - truck |
0.0 |
Accuracy Vehicle - bus |
0.0 |
Accuracy Vehicle - tramtrain |
0.0 |
Accuracy Vehicle - motorcycle |
0.0 |
Accuracy Vehicle - bicycle |
0.0 |
Accuracy Vehicle - caravan |
0.0 |
Accuracy Vehicle - cartrailer |
0.0 |
Accuracy Construction - building |
0.8930 |
Accuracy Construction - door |
0.0 |
Accuracy Construction - wall |
0.0025 |
Accuracy Construction - fenceguardrail |
0.0 |
Accuracy Construction - bridge |
0.0 |
Accuracy Construction - tunnel |
0.0 |
Accuracy Construction - stairs |
0.0 |
Accuracy Object - pole |
0.0008 |
Accuracy Object - trafficsign |
0.0 |
Accuracy Object - trafficlight |
0.0 |
Accuracy Nature - vegetation |
0.8552 |
Accuracy Nature - terrain |
0.8507 |
Accuracy Sky |
0.8336 |
Accuracy Void - ground |
0.0 |
Accuracy Void - dynamic |
0.0 |
Accuracy Void - static |
0.0 |
Accuracy Void - unclear |
0.0 |
Iou Unlabeled |
nan |
Iou Flat - road |
0.4712 |
Iou Flat - sidewalk |
0.7651 |
Iou Flat - crosswalk |
0.0 |
Iou Flat - cyclinglane |
0.5216 |
Iou Flat - parkingdriveway |
0.0178 |
Iou Flat - railtrack |
0.0 |
Iou Flat - curb |
0.0 |
Iou Human - person |
0.0 |
Iou Human - rider |
0.0 |
Iou Vehicle - car |
0.5696 |
Iou Vehicle - truck |
0.0 |
Iou Vehicle - bus |
0.0 |
Iou Vehicle - tramtrain |
0.0 |
Iou Vehicle - motorcycle |
0.0 |
Iou Vehicle - bicycle |
0.0 |
Iou Vehicle - caravan |
0.0 |
Iou Vehicle - cartrailer |
0.0 |
Iou Construction - building |
0.4716 |
Iou Construction - door |
0.0 |
Iou Construction - wall |
0.0024 |
Iou Construction - fenceguardrail |
0.0 |
Iou Construction - bridge |
0.0 |
Iou Construction - tunnel |
0.0 |
Iou Construction - stairs |
0.0 |
Iou Object - pole |
0.0008 |
Iou Object - trafficsign |
0.0 |
Iou Object - trafficlight |
0.0 |
Iou Nature - vegetation |
0.6813 |
Iou Nature - terrain |
0.5513 |
Iou Sky |
0.7873 |
Iou Void - ground |
0.0 |
Iou Void - dynamic |
0.0 |
Iou Void - static |
0.0 |
Iou Void - unclear |
0.0 |
đ Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
đ§ Technical Details
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 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
- num_epochs: 10.0
Training results
More information needed
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
đ License
This model is licensed under the Apache 2.0 license.