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my-fine-tuned-model

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  1. README.md +142 -0
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +23 -0
  5. runs/Jan21_03-49-11_jupyter-demo05/events.out.tfevents.1737431456.jupyter-demo05.168.0 +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: 1.6885
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+ - Mean Iou: 0.1644
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+ - Mean Accuracy: 0.2067
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+ - Overall Accuracy: 0.7604
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.9243
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+ - Accuracy Flat-sidewalk: 0.9308
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.3658
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+ - Accuracy Flat-parkingdriveway: 0.0
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.0
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+ - Accuracy Human-person: 0.0
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.8785
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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: nan
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.0
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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: 0.8929
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0
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+ - Accuracy Construction-fenceguardrail: 0.0
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+ - Accuracy Construction-bridge: 0.0
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+ - Accuracy Construction-tunnel: nan
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+ - Accuracy Construction-stairs: 0.0
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+ - Accuracy Object-pole: 0.0
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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: 0.9517
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+ - Accuracy Nature-terrain: 0.5597
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+ - Accuracy Sky: 0.9033
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+ - Accuracy Void-ground: 0.0
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+ - Accuracy Void-dynamic: 0.0
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+ - Accuracy Void-static: 0.0
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.5694
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+ - Iou Flat-sidewalk: 0.7974
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.3462
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+ - Iou Flat-parkingdriveway: 0.0
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.0
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+ - Iou Human-person: 0.0
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.6909
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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: nan
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.0
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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: 0.5876
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0
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+ - Iou Construction-fenceguardrail: 0.0
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+ - Iou Construction-bridge: 0.0
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+ - Iou Construction-tunnel: nan
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+ - Iou Construction-stairs: 0.0
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+ - Iou Object-pole: 0.0
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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: 0.7378
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+ - Iou Nature-terrain: 0.5221
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+ - Iou Sky: 0.8453
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+ - Iou Void-ground: 0.0
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+ - Iou Void-dynamic: 0.0
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+ - Iou Void-static: 0.0
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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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+ | 2.9927 | 0.2 | 20 | 2.8323 | 0.0790 | 0.1384 | 0.6032 | nan | 0.7510 | 0.9017 | 0.0000 | 0.0002 | 0.0000 | nan | 0.0000 | 0.4465 | 0.0 | 0.3131 | 0.0304 | 0.0 | nan | 0.0007 | 0.0 | 0.0 | 0.0776 | 0.4690 | 0.0051 | 0.0233 | 0.0 | 0.0 | nan | 0.0 | 0.0102 | 0.0 | 0.0 | 0.9462 | 0.0001 | 0.2894 | 0.0 | 0.0006 | 0.0253 | 0.0 | 0.0 | 0.4130 | 0.7047 | 0.0000 | 0.0002 | 0.0000 | 0.0 | 0.0000 | 0.0393 | 0.0 | 0.2934 | 0.0044 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0062 | 0.3769 | 0.0005 | 0.0210 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0088 | 0.0 | 0.0 | 0.5968 | 0.0001 | 0.2841 | 0.0 | 0.0004 | 0.0145 | 0.0 |
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+ | 2.7054 | 0.4 | 40 | 2.4661 | 0.1140 | 0.1677 | 0.6892 | nan | 0.8508 | 0.9033 | 0.0 | 0.0000 | 0.0 | nan | 0.0 | 0.1089 | 0.0 | 0.7446 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7878 | 0.0 | 0.0152 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9695 | 0.0012 | 0.7824 | 0.0 | 0.0 | 0.0353 | 0.0 | 0.0 | 0.4629 | 0.7392 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0480 | 0.0 | 0.6429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5762 | 0.0 | 0.0150 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6004 | 0.0012 | 0.7581 | 0.0 | 0.0 | 0.0316 | 0.0 |
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+ | 2.3703 | 0.6 | 60 | 2.2220 | 0.1307 | 0.1726 | 0.7091 | nan | 0.8602 | 0.9235 | 0.0 | 0.0087 | 0.0 | nan | 0.0 | 0.0078 | 0.0 | 0.8029 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8432 | 0.0 | 0.0032 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9634 | 0.0720 | 0.8469 | 0.0 | 0.0 | 0.0175 | 0.0 | nan | 0.4991 | 0.7506 | 0.0 | 0.0087 | 0.0 | nan | 0.0 | 0.0066 | 0.0 | 0.6632 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5803 | 0.0 | 0.0032 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6381 | 0.0711 | 0.8138 | 0.0 | 0.0 | 0.0169 | 0.0 |
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+ | 2.1115 | 0.8 | 80 | 2.0338 | 0.1408 | 0.1832 | 0.7268 | nan | 0.8785 | 0.9295 | 0.0 | 0.0351 | 0.0 | nan | 0.0000 | 0.0009 | 0.0 | 0.8476 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8966 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9472 | 0.2668 | 0.8725 | 0.0 | 0.0 | 0.0061 | 0.0 | nan | 0.5161 | 0.7578 | 0.0 | 0.0350 | 0.0 | nan | 0.0000 | 0.0008 | 0.0 | 0.6755 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5838 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7029 | 0.2587 | 0.8285 | 0.0 | 0.0 | 0.0061 | 0.0 |
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+ | 2.0943 | 1.0 | 100 | 1.9439 | 0.1409 | 0.1883 | 0.7309 | nan | 0.9316 | 0.9111 | 0.0 | 0.0606 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.8286 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9048 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9431 | 0.3701 | 0.8879 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.4925 | 0.7839 | 0.0 | 0.0605 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.6885 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5688 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7221 | 0.3542 | 0.8377 | 0.0 | 0.0 | 0.0005 | 0.0 |
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+ | 1.962 | 1.2 | 120 | 1.8278 | 0.1523 | 0.1943 | 0.7457 | nan | 0.8894 | 0.9411 | 0.0 | 0.2402 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.8845 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8780 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9544 | 0.3342 | 0.9011 | 0.0 | 0.0 | 0.0003 | 0.0 | nan | 0.5553 | 0.7789 | 0.0 | 0.2357 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6855 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5929 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7078 | 0.3194 | 0.8442 | 0.0 | 0.0 | 0.0003 | 0.0 |
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+ | 1.8545 | 1.4 | 140 | 1.7513 | 0.1615 | 0.2032 | 0.7568 | nan | 0.9153 | 0.9350 | 0.0 | 0.3471 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.8702 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8852 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9552 | 0.4967 | 0.8943 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5723 | 0.7897 | 0.0 | 0.3290 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6881 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5908 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7264 | 0.4707 | 0.8396 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.8784 | 1.6 | 160 | 1.7246 | 0.1600 | 0.2014 | 0.7559 | nan | 0.9107 | 0.9375 | 0.0 | 0.3514 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.8730 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9522 | 0.4351 | 0.8847 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5759 | 0.7914 | 0.0 | 0.3336 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6908 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5874 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7221 | 0.4148 | 0.8429 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9631 | 1.8 | 180 | 1.7066 | 0.1627 | 0.2047 | 0.7573 | nan | 0.9303 | 0.9268 | 0.0 | 0.3374 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.8747 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9549 | 0.5359 | 0.8947 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5559 | 0.7977 | 0.0 | 0.3210 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6966 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5896 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7321 | 0.5057 | 0.8436 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8769 | 2.0 | 200 | 1.6885 | 0.1644 | 0.2067 | 0.7604 | nan | 0.9243 | 0.9308 | 0.0 | 0.3658 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.8785 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8929 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9517 | 0.5597 | 0.9033 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5694 | 0.7974 | 0.0 | 0.3462 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6909 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5876 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7378 | 0.5221 | 0.8453 | 0.0 | 0.0 | 0.0 | 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",
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+ "architectures": [
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+ "SegformerForSemanticSegmentation"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "classifier_dropout_prob": 0.1,
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+ "decoder_hidden_size": 256,
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+ "depths": [
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+ 2,
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+ 2,
12
+ 2,
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+ 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",
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+ "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": "unlabeled",
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+ "1": "flat-road",
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+ "2": "flat-sidewalk",
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+ "3": "flat-crosswalk",
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+ "4": "flat-cyclinglane",
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+ "5": "flat-parkingdriveway",
37
+ "6": "flat-railtrack",
38
+ "7": "flat-curb",
39
+ "8": "human-person",
40
+ "9": "human-rider",
41
+ "10": "vehicle-car",
42
+ "11": "vehicle-truck",
43
+ "12": "vehicle-bus",
44
+ "13": "vehicle-tramtrain",
45
+ "14": "vehicle-motorcycle",
46
+ "15": "vehicle-bicycle",
47
+ "16": "vehicle-caravan",
48
+ "17": "vehicle-cartrailer",
49
+ "18": "construction-building",
50
+ "19": "construction-door",
51
+ "20": "construction-wall",
52
+ "21": "construction-fenceguardrail",
53
+ "22": "construction-bridge",
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+ "23": "construction-tunnel",
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+ "24": "construction-stairs",
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+ "25": "object-pole",
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+ "26": "object-trafficsign",
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+ "27": "object-trafficlight",
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+ "28": "nature-vegetation",
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+ "29": "nature-terrain",
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+ "30": "sky",
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+ "31": "void-ground",
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+ "32": "void-dynamic",
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+ "33": "void-static",
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+ "34": "void-unclear"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "construction-bridge": 22,
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+ "construction-building": 18,
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+ "construction-door": 19,
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+ "construction-fenceguardrail": 21,
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+ "construction-stairs": 24,
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+ "construction-tunnel": 23,
76
+ "construction-wall": 20,
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+ "flat-crosswalk": 3,
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+ "flat-curb": 7,
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+ "flat-cyclinglane": 4,
80
+ "flat-parkingdriveway": 5,
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+ "flat-railtrack": 6,
82
+ "flat-road": 1,
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+ "flat-sidewalk": 2,
84
+ "human-person": 8,
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+ "human-rider": 9,
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+ "nature-terrain": 29,
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+ "nature-vegetation": 28,
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+ "object-pole": 25,
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+ "object-trafficlight": 27,
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+ "object-trafficsign": 26,
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+ "sky": 30,
92
+ "unlabeled": 0,
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+ "vehicle-bicycle": 15,
94
+ "vehicle-bus": 12,
95
+ "vehicle-car": 10,
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+ "vehicle-caravan": 16,
97
+ "vehicle-cartrailer": 17,
98
+ "vehicle-motorcycle": 14,
99
+ "vehicle-tramtrain": 13,
100
+ "vehicle-truck": 11,
101
+ "void-dynamic": 32,
102
+ "void-ground": 31,
103
+ "void-static": 33,
104
+ "void-unclear": 34
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+ },
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+ "layer_norm_eps": 1e-06,
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+ "mlp_ratios": [
108
+ 4,
109
+ 4,
110
+ 4,
111
+ 4
112
+ ],
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+ "model_type": "segformer",
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+ "num_attention_heads": [
115
+ 1,
116
+ 2,
117
+ 5,
118
+ 8
119
+ ],
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+ "num_channels": 3,
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+ "num_encoder_blocks": 4,
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+ "patch_sizes": [
123
+ 7,
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+ 3,
125
+ 3,
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+ 3
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+ ],
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