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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_03-46-54_jupyter-demo02/events.out.tfevents.1737431443.jupyter-demo02.123.0 +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: 1.6767
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+ - Mean Iou: 0.1588
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+ - Mean Accuracy: 0.2007
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+ - Overall Accuracy: 0.7509
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8733
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+ - Accuracy Flat-sidewalk: 0.9442
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.3911
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+ - Accuracy Flat-parkingdriveway: 0.0002
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+ - Accuracy Flat-railtrack: 0.0
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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: 0.8925
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+ - Accuracy Vehicle-truck: 0.0
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+ - Accuracy Vehicle-bus: nan
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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: 0.0
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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: 0.8894
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0009
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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: 0.9669
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+ - Accuracy Nature-terrain: 0.6018
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+ - Accuracy Sky: 0.8632
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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: 0.0000
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.5360
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+ - Iou Flat-sidewalk: 0.7953
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.3852
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+ - Iou Flat-parkingdriveway: 0.0002
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+ - Iou Flat-railtrack: 0.0
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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: 0.6843
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+ - Iou Vehicle-truck: 0.0
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+ - Iou Vehicle-bus: nan
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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: 0.0
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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: 0.5805
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0009
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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: 0.7337
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+ - Iou Nature-terrain: 0.5424
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+ - Iou Sky: 0.8220
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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: 0.0000
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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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+ | 2.9696 | 0.2 | 20 | 2.8275 | 0.0948 | 0.1415 | 0.6309 | nan | 0.6575 | 0.9107 | 0.0063 | 0.1826 | 0.0104 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.7030 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7348 | 0.0 | 0.0526 | 0.0017 | 0.0 | nan | 0.0004 | 0.0002 | 0.0 | 0.0 | 0.9372 | 0.0527 | 0.1780 | 0.0 | 0.0000 | 0.0984 | 0.0 | 0.0 | 0.4367 | 0.7060 | 0.0047 | 0.1714 | 0.0091 | 0.0 | 0.0018 | 0.0 | 0.0 | 0.5700 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4605 | 0.0 | 0.0337 | 0.0015 | 0.0 | nan | 0.0003 | 0.0002 | 0.0 | 0.0 | 0.5601 | 0.0441 | 0.1768 | 0.0 | 0.0000 | 0.0466 | 0.0 |
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+ | 2.5415 | 0.4 | 40 | 2.4456 | 0.1126 | 0.1620 | 0.6774 | nan | 0.8004 | 0.9103 | 0.0018 | 0.0850 | 0.0035 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.7352 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8639 | 0.0 | 0.0178 | 0.0000 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9712 | 0.0748 | 0.7159 | 0.0 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.4600 | 0.7407 | 0.0017 | 0.0827 | 0.0034 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.6149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5326 | 0.0 | 0.0170 | 0.0000 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6032 | 0.0721 | 0.6944 | 0.0 | 0.0 | 0.0028 | 0.0 |
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+ | 2.3174 | 0.6 | 60 | 2.2279 | 0.1232 | 0.1695 | 0.6931 | nan | 0.8442 | 0.9167 | 0.0 | 0.0799 | 0.0010 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8018 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8769 | 0.0 | 0.0051 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9726 | 0.1943 | 0.7298 | 0.0 | 0.0 | 0.0008 | 0.0 | nan | 0.4596 | 0.7613 | 0.0 | 0.0787 | 0.0009 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6721 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5541 | 0.0 | 0.0051 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6358 | 0.1838 | 0.7134 | 0.0 | 0.0 | 0.0008 | 0.0 |
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+ | 2.2451 | 0.8 | 80 | 2.0572 | 0.1302 | 0.1737 | 0.6974 | nan | 0.8615 | 0.9134 | 0.0 | 0.0646 | 0.0000 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8375 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8514 | 0.0 | 0.0031 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9760 | 0.2230 | 0.8279 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.4523 | 0.7700 | 0.0 | 0.0640 | 0.0000 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6731 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5634 | 0.0 | 0.0031 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6439 | 0.2111 | 0.7855 | 0.0 | 0.0 | 0.0002 | 0.0 |
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+ | 2.1085 | 1.0 | 100 | 1.9511 | 0.1458 | 0.1905 | 0.7229 | nan | 0.8854 | 0.9090 | 0.0 | 0.2045 | 0.0001 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8710 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8821 | 0.0 | 0.0021 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9619 | 0.5303 | 0.8495 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.4688 | 0.7849 | 0.0 | 0.2007 | 0.0001 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6649 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5579 | 0.0 | 0.0021 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7228 | 0.4830 | 0.7802 | 0.0 | 0.0 | 0.0002 | 0.0 |
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+ | 1.922 | 1.2 | 120 | 1.8311 | 0.1530 | 0.1956 | 0.7400 | nan | 0.8766 | 0.9349 | 0.0 | 0.3273 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8934 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8877 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9635 | 0.5479 | 0.8259 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.5080 | 0.7901 | 0.0 | 0.3245 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6769 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5783 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7257 | 0.4967 | 0.7942 | 0.0 | 0.0 | 0.0002 | 0.0 |
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+ | 1.8359 | 1.4 | 140 | 1.7618 | 0.1532 | 0.1953 | 0.7410 | nan | 0.8565 | 0.9426 | 0.0 | 0.3500 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8806 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8729 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9731 | 0.5240 | 0.8473 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.5324 | 0.7846 | 0.0 | 0.3453 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6844 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5774 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7026 | 0.4645 | 0.8113 | 0.0 | 0.0 | 0.0001 | 0.0 |
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+ | 2.0133 | 1.6 | 160 | 1.7325 | 0.1600 | 0.2023 | 0.7521 | nan | 0.8717 | 0.9398 | 0.0 | 0.4123 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8818 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8966 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9644 | 0.6293 | 0.8755 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5406 | 0.7934 | 0.0 | 0.4029 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6887 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5760 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7380 | 0.5522 | 0.8273 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.6493 | 1.8 | 180 | 1.6853 | 0.1595 | 0.2015 | 0.7505 | nan | 0.8856 | 0.9408 | 0.0 | 0.3517 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8888 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8978 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9610 | 0.6444 | 0.8762 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.5191 | 0.7991 | 0.0 | 0.3470 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6906 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5799 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7506 | 0.5869 | 0.8285 | 0.0 | 0.0 | 0.0001 | 0.0 |
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+ | 1.8097 | 2.0 | 200 | 1.6767 | 0.1588 | 0.2007 | 0.7509 | nan | 0.8733 | 0.9442 | 0.0 | 0.3911 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8925 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8894 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9669 | 0.6018 | 0.8632 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5360 | 0.7953 | 0.0 | 0.3852 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6843 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5805 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7337 | 0.5424 | 0.8220 | 0.0 | 0.0 | 0.0000 | 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": [
10
+ 2,
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+ 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": "unlabeled",
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+ "1": "flat-road",
33
+ "2": "flat-sidewalk",
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+ "3": "flat-crosswalk",
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+ "4": "flat-cyclinglane",
36
+ "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",
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+ "21": "construction-fenceguardrail",
53
+ "22": "construction-bridge",
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+ "23": "construction-tunnel",
55
+ "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,
72
+ "construction-door": 19,
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+ "construction-fenceguardrail": 21,
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+ "construction-stairs": 24,
75
+ "construction-tunnel": 23,
76
+ "construction-wall": 20,
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+ "flat-crosswalk": 3,
78
+ "flat-curb": 7,
79
+ "flat-cyclinglane": 4,
80
+ "flat-parkingdriveway": 5,
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+ "flat-railtrack": 6,
82
+ "flat-road": 1,
83
+ "flat-sidewalk": 2,
84
+ "human-person": 8,
85
+ "human-rider": 9,
86
+ "nature-terrain": 29,
87
+ "nature-vegetation": 28,
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+ "object-pole": 25,
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+ "object-trafficlight": 27,
90
+ "object-trafficsign": 26,
91
+ "sky": 30,
92
+ "unlabeled": 0,
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+ "vehicle-bicycle": 15,
94
+ "vehicle-bus": 12,
95
+ "vehicle-car": 10,
96
+ "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
+ ],
113
+ "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,
121
+ "num_encoder_blocks": 4,
122
+ "patch_sizes": [
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+ 7,
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+ 3,
125
+ 3,
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+ 3
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