--- license: other base_model: nvidia/mit-b0 tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-fingertip-10-steps results: [] --- # segformer-finetuned-fingertip-10-steps 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: 3.5204 - Mean Iou: 0.0132 - Mean Accuracy: 0.0688 - Overall Accuracy: 0.0956 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.0201 - Accuracy Flat-sidewalk: 0.1177 - Accuracy Flat-crosswalk: 0.0014 - Accuracy Flat-cyclinglane: 0.4426 - Accuracy Flat-parkingdriveway: 0.0021 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0008 - Accuracy Human-person: 0.0010 - Accuracy Human-rider: 0.0077 - Accuracy Vehicle-car: 0.1566 - Accuracy Vehicle-truck: 0.0040 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.5317 - Accuracy Vehicle-bicycle: 0.0892 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: nan - Accuracy Construction-building: 0.0578 - Accuracy Construction-door: 0.0682 - Accuracy Construction-wall: 0.0002 - Accuracy Construction-fenceguardrail: 0.0000 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0055 - Accuracy Object-pole: 0.0162 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.3811 - Accuracy Nature-vegetation: 0.0756 - Accuracy Nature-terrain: 0.0010 - Accuracy Sky: 0.0038 - Accuracy Void-ground: 0.0400 - Accuracy Void-dynamic: 0.0002 - Accuracy Void-static: 0.0386 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: 0.0 - Iou Flat-road: 0.0193 - Iou Flat-sidewalk: 0.1141 - Iou Flat-crosswalk: 0.0013 - Iou Flat-cyclinglane: 0.0702 - Iou Flat-parkingdriveway: 0.0019 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0007 - Iou Human-person: 0.0005 - Iou Human-rider: 0.0001 - Iou Vehicle-car: 0.1087 - Iou Vehicle-truck: 0.0003 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0004 - Iou Vehicle-bicycle: 0.0085 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.0413 - Iou Construction-door: 0.0067 - Iou Construction-wall: 0.0002 - Iou Construction-fenceguardrail: 0.0000 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0021 - Iou Object-pole: 0.0036 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0001 - Iou Nature-vegetation: 0.0663 - Iou Nature-terrain: 0.0009 - Iou Sky: 0.0038 - Iou Void-ground: 0.0049 - Iou Void-dynamic: 0.0000 - Iou Void-static: 0.0046 - 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | No log | 0.09 | 10 | 3.5204 | 0.0132 | 0.0688 | 0.0956 | nan | 0.0201 | 0.1177 | 0.0014 | 0.4426 | 0.0021 | nan | 0.0008 | 0.0010 | 0.0077 | 0.1566 | 0.0040 | 0.0 | nan | 0.5317 | 0.0892 | 0.0 | nan | 0.0578 | 0.0682 | 0.0002 | 0.0000 | 0.0 | nan | 0.0055 | 0.0162 | 0.0 | 0.3811 | 0.0756 | 0.0010 | 0.0038 | 0.0400 | 0.0002 | 0.0386 | 0.0 | 0.0 | 0.0193 | 0.1141 | 0.0013 | 0.0702 | 0.0019 | 0.0 | 0.0007 | 0.0005 | 0.0001 | 0.1087 | 0.0003 | 0.0 | 0.0 | 0.0004 | 0.0085 | 0.0 | 0.0 | 0.0413 | 0.0067 | 0.0002 | 0.0000 | 0.0 | 0.0 | 0.0021 | 0.0036 | 0.0 | 0.0001 | 0.0663 | 0.0009 | 0.0038 | 0.0049 | 0.0000 | 0.0046 | 0.0 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.16.0 - Tokenizers 0.15.0