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Kohei3
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my-fine-tuned-model

Image Segmentation
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  1. README.md +142 -0
  2. config.json +374 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +23 -0
  5. runs/Jan21_04-13-25_jupyter-demo07/events.out.tfevents.1737432877.jupyter-demo07.166.0 +3 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ base_model: nvidia/segformer-b1-finetuned-ade-512-512
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: my-fine-tuned-model
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # my-fine-tuned-model
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+
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+ This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b1-finetuned-ade-512-512) on the segments/sidewalk-semantic dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8650
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+ - Mean Iou: 0.1979
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+ - Mean Accuracy: 0.2600
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+ - Overall Accuracy: 0.7844
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.7636
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+ - Accuracy Flat-sidewalk: 0.9369
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+ - Accuracy Flat-crosswalk: 0.6233
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+ - Accuracy Flat-cyclinglane: 0.5039
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+ - Accuracy Flat-parkingdriveway: 0.3350
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.3609
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+ - Accuracy Human-person: 0.0041
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.9008
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+ - Accuracy Vehicle-truck: 0.0
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+ - Accuracy Vehicle-bus: 0.0
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+ - Accuracy Vehicle-tramtrain: 0.0
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.0005
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+ - Accuracy Vehicle-caravan: 0.0
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+ - Accuracy Vehicle-cartrailer: 0.0
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+ - Accuracy Construction-building: 0.8702
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.3334
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+ - Accuracy Construction-fenceguardrail: 0.1194
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+ - Accuracy Construction-bridge: 0.0
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+ - Accuracy Construction-tunnel: 0.0
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+ - Accuracy Construction-stairs: 0.0
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+ - Accuracy Object-pole: 0.0637
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+ - Accuracy Object-trafficsign: 0.0
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+ - Accuracy Object-trafficlight: 0.0
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+ - Accuracy Nature-vegetation: 0.9057
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+ - Accuracy Nature-terrain: 0.8349
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+ - Accuracy Sky: 0.9210
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+ - Accuracy Void-ground: 0.0339
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+ - Accuracy Void-dynamic: 0.0032
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+ - Accuracy Void-static: 0.0657
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: 0.0
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+ - Iou Flat-road: 0.6053
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+ - Iou Flat-sidewalk: 0.7972
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+ - Iou Flat-crosswalk: 0.4947
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+ - Iou Flat-cyclinglane: 0.4639
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+ - Iou Flat-parkingdriveway: 0.2544
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+ - Iou Flat-railtrack: 0.0
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+ - Iou Flat-curb: 0.2330
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+ - Iou Human-person: 0.0041
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.6748
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+ - Iou Vehicle-truck: 0.0
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+ - Iou Vehicle-bus: 0.0
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+ - Iou Vehicle-tramtrain: 0.0
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.0005
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+ - Iou Vehicle-caravan: 0.0
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+ - Iou Vehicle-cartrailer: 0.0
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+ - Iou Construction-building: 0.6517
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.2393
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+ - Iou Construction-fenceguardrail: 0.0990
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+ - Iou Construction-bridge: 0.0
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+ - Iou Construction-tunnel: 0.0
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+ - Iou Construction-stairs: 0.0
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+ - Iou Object-pole: 0.0522
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+ - Iou Object-trafficsign: 0.0
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+ - Iou Object-trafficlight: 0.0
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+ - Iou Nature-vegetation: 0.7891
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+ - Iou Nature-terrain: 0.6367
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+ - Iou Sky: 0.8417
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+ - Iou Void-ground: 0.0306
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+ - Iou Void-dynamic: 0.0031
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+ - Iou Void-static: 0.0567
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+ - Iou Void-unclear: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
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+ | 2.5085 | 0.2 | 20 | 2.7797 | 0.0732 | 0.1076 | 0.4652 | nan | 0.5841 | 0.7791 | 0.0055 | 0.2836 | 0.0034 | nan | 0.0 | 0.0 | 0.0 | 0.5620 | 0.0 | 0.0198 | 0.0 | 0.0004 | 0.0004 | 0.0 | 0.0535 | 0.0891 | 0.0012 | 0.0003 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3935 | 0.6589 | 0.1063 | 0.0 | 0.0081 | 0.0 | 0.0 | 0.0 | 0.3763 | 0.6424 | 0.0019 | 0.2150 | 0.0033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2835 | 0.0 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 0.0 | 0.0000 | 0.0859 | 0.0011 | 0.0002 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3843 | 0.4577 | 0.1061 | 0.0 | 0.0024 | 0.0 | 0.0 |
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+ | 2.4875 | 0.4 | 40 | 1.7587 | 0.1295 | 0.1722 | 0.6580 | nan | 0.7729 | 0.8313 | 0.0254 | 0.3069 | 0.0016 | nan | 0.0005 | 0.0 | 0.0 | 0.7856 | 0.0 | 0.0017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0044 | 0.6115 | 0.0009 | 0.0148 | 0.0122 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.8782 | 0.6807 | 0.7454 | 0.0 | 0.0067 | 0.0 | 0.0 | 0.0 | 0.4677 | 0.7337 | 0.0129 | 0.2895 | 0.0016 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.5336 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.4970 | 0.0009 | 0.0130 | 0.0114 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.7164 | 0.5249 | 0.7231 | 0.0 | 0.0030 | 0.0 | 0.0 |
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+ | 1.4979 | 0.6 | 60 | 1.3253 | 0.1478 | 0.2057 | 0.6899 | nan | 0.8068 | 0.8066 | 0.1982 | 0.4657 | 0.2635 | nan | 0.0285 | 0.0 | 0.0 | 0.8709 | 0.0 | 0.0017 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.6424 | 0.0000 | 0.1611 | 0.0051 | 0.0 | 0.0 | 0.0 | 0.0138 | 0.0 | 0.0 | 0.8751 | 0.7601 | 0.8505 | 0.0261 | 0.0108 | 0.0001 | 0.0 | 0.0 | 0.4953 | 0.7315 | 0.1383 | 0.3556 | 0.1491 | 0.0 | 0.0266 | 0.0 | 0.0 | 0.4975 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.5333 | 0.0000 | 0.1061 | 0.0049 | 0.0 | 0.0 | 0.0 | 0.0122 | 0.0 | 0.0 | 0.7534 | 0.5484 | 0.7903 | 0.0243 | 0.0045 | 0.0001 | 0.0 |
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+ | 1.2341 | 0.8 | 80 | 1.2097 | 0.1612 | 0.2131 | 0.7259 | nan | 0.5034 | 0.9552 | 0.5156 | 0.4170 | 0.1216 | nan | 0.1390 | 0.0 | 0.0003 | 0.8090 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8904 | 0.0 | 0.1239 | 0.0350 | 0.0 | 0.0 | 0.0 | 0.0102 | 0.0 | 0.0 | 0.8813 | 0.7535 | 0.8759 | 0.0 | 0.0013 | 0.0003 | 0.0 | 0.0 | 0.4375 | 0.7135 | 0.3661 | 0.3521 | 0.1009 | 0.0 | 0.1176 | 0.0 | 0.0000 | 0.6389 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5992 | 0.0 | 0.0962 | 0.0306 | 0.0 | 0.0 | 0.0 | 0.0092 | 0.0 | 0.0 | 0.7711 | 0.5926 | 0.8134 | 0.0 | 0.0011 | 0.0003 | 0.0 |
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+ | 1.594 | 1.0 | 100 | 1.0384 | 0.1687 | 0.2260 | 0.7523 | nan | 0.8702 | 0.8883 | 0.3212 | 0.4611 | 0.1767 | nan | 0.1078 | 0.0 | 0.0013 | 0.8716 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0 | 0.0 | 0.8338 | 0.0 | 0.2006 | 0.0366 | 0.0 | 0.0 | 0.0 | 0.0374 | 0.0 | 0.0 | 0.8996 | 0.8280 | 0.8915 | 0.0 | 0.0008 | 0.0306 | 0.0 | 0.0 | 0.5099 | 0.7920 | 0.2474 | 0.4321 | 0.1507 | 0.0 | 0.0913 | 0.0 | 0.0003 | 0.6235 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.6274 | 0.0 | 0.1603 | 0.0337 | 0.0 | 0.0 | 0.0 | 0.0300 | 0.0 | 0.0 | 0.7659 | 0.5944 | 0.8171 | 0.0 | 0.0008 | 0.0260 | 0.0 |
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+ | 0.7624 | 1.2 | 120 | 0.9402 | 0.1852 | 0.2479 | 0.7620 | nan | 0.6183 | 0.9445 | 0.6022 | 0.5182 | 0.2131 | nan | 0.3901 | 0.0 | 0.0008 | 0.8980 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8681 | 0.0000 | 0.2897 | 0.1158 | 0.0 | 0.0 | 0.0 | 0.0470 | 0.0 | 0.0 | 0.8860 | 0.8323 | 0.9143 | 0.0032 | 0.0017 | 0.0376 | 0.0 | 0.0 | 0.5142 | 0.7737 | 0.4531 | 0.4391 | 0.1737 | 0.0 | 0.2176 | 0.0 | 0.0002 | 0.6419 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6411 | 0.0000 | 0.2041 | 0.0941 | 0.0 | 0.0 | 0.0 | 0.0376 | 0.0 | 0.0 | 0.7853 | 0.6371 | 0.8329 | 0.0029 | 0.0016 | 0.0328 | 0.0 |
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+ | 1.1404 | 1.4 | 140 | 0.9037 | 0.1937 | 0.2559 | 0.7761 | nan | 0.7507 | 0.9337 | 0.6121 | 0.5261 | 0.2748 | nan | 0.2728 | 0.0007 | 0.0 | 0.8756 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8461 | 0.0000 | 0.4180 | 0.1162 | 0.0 | 0.0 | 0.0 | 0.0773 | 0.0 | 0.0 | 0.9055 | 0.8279 | 0.9024 | 0.0404 | 0.0019 | 0.0623 | 0.0 | 0.0 | 0.5682 | 0.7933 | 0.4733 | 0.4630 | 0.2328 | 0.0 | 0.1898 | 0.0007 | 0.0 | 0.6841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6414 | 0.0000 | 0.2357 | 0.0930 | 0.0 | 0.0 | 0.0 | 0.0561 | 0.0 | 0.0 | 0.7862 | 0.6368 | 0.8350 | 0.0348 | 0.0018 | 0.0532 | 0.0 |
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+ | 0.7949 | 1.6 | 160 | 0.8834 | 0.1959 | 0.2559 | 0.7833 | nan | 0.7455 | 0.9427 | 0.6186 | 0.5220 | 0.3557 | nan | 0.3018 | 0.0044 | 0.0 | 0.9011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.3135 | 0.1094 | 0.0 | 0.0 | 0.0 | 0.0602 | 0.0 | 0.0 | 0.9121 | 0.7871 | 0.9168 | 0.0254 | 0.0012 | 0.0554 | 0.0 | 0.0 | 0.5954 | 0.7952 | 0.4864 | 0.4753 | 0.2686 | 0.0 | 0.2131 | 0.0044 | 0.0 | 0.6642 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.0 | 0.0 | 0.6427 | 0.0 | 0.2290 | 0.0919 | 0.0 | 0.0 | 0.0 | 0.0486 | 0.0 | 0.0 | 0.7904 | 0.6367 | 0.8404 | 0.0234 | 0.0011 | 0.0493 | 0.0 |
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+ | 1.1158 | 1.8 | 180 | 0.8739 | 0.1967 | 0.2579 | 0.7829 | nan | 0.7531 | 0.9429 | 0.6044 | 0.4876 | 0.3491 | nan | 0.3338 | 0.0041 | 0.0 | 0.8976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.8643 | 0.0 | 0.3477 | 0.1201 | 0.0 | 0.0 | 0.0 | 0.0602 | 0.0 | 0.0 | 0.9064 | 0.8280 | 0.9161 | 0.0301 | 0.0027 | 0.0612 | 0.0 | 0.0 | 0.6023 | 0.7929 | 0.4754 | 0.4542 | 0.2581 | 0.0 | 0.2261 | 0.0041 | 0.0 | 0.6759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.6505 | 0.0 | 0.2436 | 0.0991 | 0.0 | 0.0 | 0.0 | 0.0493 | 0.0 | 0.0 | 0.7883 | 0.6363 | 0.8424 | 0.0275 | 0.0026 | 0.0538 | 0.0 |
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+ | 0.9007 | 2.0 | 200 | 0.8650 | 0.1979 | 0.2600 | 0.7844 | nan | 0.7636 | 0.9369 | 0.6233 | 0.5039 | 0.3350 | nan | 0.3609 | 0.0041 | 0.0 | 0.9008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.8702 | 0.0 | 0.3334 | 0.1194 | 0.0 | 0.0 | 0.0 | 0.0637 | 0.0 | 0.0 | 0.9057 | 0.8349 | 0.9210 | 0.0339 | 0.0032 | 0.0657 | 0.0 | 0.0 | 0.6053 | 0.7972 | 0.4947 | 0.4639 | 0.2544 | 0.0 | 0.2330 | 0.0041 | 0.0 | 0.6748 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.6517 | 0.0 | 0.2393 | 0.0990 | 0.0 | 0.0 | 0.0 | 0.0522 | 0.0 | 0.0 | 0.7891 | 0.6367 | 0.8417 | 0.0306 | 0.0031 | 0.0567 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.0
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+ - Pytorch 2.1.1+cu118
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
config.json ADDED
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+ {
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+ "_name_or_path": "nvidia/segformer-b1-finetuned-ade-512-512",
3
+ "architectures": [
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+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
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+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
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+ ],
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+ "downsampling_rates": [
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+ 1,
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+ 4,
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+ 8,
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+ 16
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+ ],
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+ "drop_path_rate": 0.1,
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+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
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+ "hidden_sizes": [
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+ 64,
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+ 128,
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+ 320,
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+ 512
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+ ],
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+ "id2label": {
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+ "0": "wall",
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+ "1": "building",
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+ "2": "sky",
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+ "3": "floor",
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+ "4": "tree",
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+ "5": "ceiling",
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+ "6": "road",
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+ "7": "bed ",
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+ "8": "windowpane",
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+ "9": "grass",
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+ "10": "cabinet",
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+ "11": "sidewalk",
43
+ "12": "person",
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+ "13": "earth",
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+ "14": "door",
46
+ "15": "table",
47
+ "16": "mountain",
48
+ "17": "plant",
49
+ "18": "curtain",
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+ "19": "chair",
51
+ "20": "car",
52
+ "21": "water",
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+ "22": "painting",
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+ "23": "sofa",
55
+ "24": "shelf",
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+ "25": "house",
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+ "26": "sea",
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+ "27": "mirror",
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+ "28": "rug",
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+ "29": "field",
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+ "30": "armchair",
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+ "31": "seat",
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+ "32": "fence",
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+ "33": "desk",
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+ "34": "rock",
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+ "35": "wardrobe",
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+ "36": "lamp",
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+ "37": "bathtub",
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+ "38": "railing",
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+ "39": "cushion",
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+ "40": "base",
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+ "41": "box",
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+ "42": "column",
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+ "43": "signboard",
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+ "44": "chest of drawers",
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+ "45": "counter",
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+ "46": "sand",
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+ "47": "sink",
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+ "48": "skyscraper",
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+ "49": "fireplace",
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+ "50": "refrigerator",
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+ "51": "grandstand",
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+ "52": "path",
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+ "53": "stairs",
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+ "54": "runway",
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+ "55": "case",
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+ "56": "pool table",
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+ "57": "pillow",
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+ "58": "screen door",
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+ "59": "stairway",
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+ "60": "river",
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+ "61": "bridge",
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+ "62": "bookcase",
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+ "63": "blind",
95
+ "64": "coffee table",
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