File size: 3,400 Bytes
743b3d0 11ec7bf 743b3d0 11ec7bf 743b3d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
---
license: apache-2.0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-sidewalk-50-epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-sidewalk-50-epochs
This model 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:
- Loss: 0.7794
- Mean Iou: 0.2323
- Mean Accuracy: 0.2967
- Overall Accuracy: 0.7880
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.7710
- Accuracy Flat-sidewalk: 0.9216
- Accuracy Flat-crosswalk: 0.2824
- Accuracy Flat-cyclinglane: 0.7632
- Accuracy Flat-parkingdriveway: 0.2202
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.5054
- Accuracy Human-person: 0.5200
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9194
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: nan
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8614
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.4325
- Accuracy Construction-fenceguardrail: 0.0652
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.2563
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9089
- Accuracy Nature-terrain: 0.8215
- Accuracy Sky: 0.8732
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0739
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.5975
- Iou Flat-sidewalk: 0.8299
- Iou Flat-crosswalk: 0.2624
- Iou Flat-cyclinglane: 0.5689
- Iou Flat-parkingdriveway: 0.1733
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.3084
- Iou Human-person: 0.3708
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.6164
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: nan
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.6305
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.3016
- Iou Construction-fenceguardrail: 0.0580
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.1310
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.7954
- Iou Nature-terrain: 0.6628
- Iou Sky: 0.8233
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0705
- Iou Void-unclear: 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: 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: 50.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|