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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_03-49-10_jupyter-demo04/events.out.tfevents.1737431456.jupyter-demo04.150.0 +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: 1.6073
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+ - Mean Iou: 0.1725
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+ - Mean Accuracy: 0.2142
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+ - Overall Accuracy: 0.7712
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8994
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+ - Accuracy Flat-sidewalk: 0.9458
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.5523
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+ - Accuracy Flat-parkingdriveway: 0.0046
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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: 0.8918
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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: 0.8587
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0000
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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: 0.9487
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+ - Accuracy Nature-terrain: 0.6545
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+ - Accuracy Sky: 0.8834
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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: 0.6288
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+ - Iou Flat-sidewalk: 0.7990
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.4898
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+ - Iou Flat-parkingdriveway: 0.0046
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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: 0.7296
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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: 0.5826
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0000
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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: 0.7290
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+ - Iou Nature-terrain: 0.5528
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+ - Iou Sky: 0.8323
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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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+ | 2.9867 | 0.2 | 20 | 2.7695 | 0.0608 | 0.1215 | 0.5176 | nan | 0.1535 | 0.9545 | 0.0 | 0.4276 | 0.0007 | nan | 0.0011 | 0.0313 | 0.0316 | 0.4054 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0123 | 0.0 | 0.0474 | 0.4432 | 0.0 | 0.2061 | 0.0 | nan | 0.0126 | 0.0026 | 0.0 | 0.0834 | 0.9202 | 0.0051 | 0.0216 | 0.0 | 0.0054 | 0.0016 | 0.0 | 0.0 | 0.1444 | 0.6958 | 0.0 | 0.1556 | 0.0006 | 0.0 | 0.0010 | 0.0307 | 0.0041 | 0.4029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0019 | 0.0 | 0.0469 | 0.0110 | 0.0 | 0.0341 | 0.0 | 0.0 | 0.0023 | 0.0022 | 0.0 | 0.0004 | 0.5623 | 0.0050 | 0.0191 | 0.0 | 0.0045 | 0.0016 | 0.0 |
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+ | 2.5329 | 0.4 | 40 | 2.4300 | 0.1145 | 0.1755 | 0.6737 | nan | 0.6841 | 0.9241 | 0.0 | 0.4931 | 0.0019 | nan | 0.0008 | 0.0042 | 0.0 | 0.8445 | 0.0003 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4625 | 0.0759 | 0.0002 | 0.1086 | 0.0 | nan | 0.0307 | 0.0 | 0.0 | 0.0 | 0.9880 | 0.0139 | 0.8085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5074 | 0.7430 | 0.0 | 0.3076 | 0.0019 | 0.0 | 0.0008 | 0.0042 | 0.0 | 0.7114 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4046 | 0.0092 | 0.0002 | 0.0473 | 0.0 | 0.0 | 0.0080 | 0.0 | 0.0 | 0.0 | 0.5680 | 0.0137 | 0.6814 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 2.3589 | 0.6 | 60 | 2.1477 | 0.1380 | 0.1895 | 0.7251 | nan | 0.7858 | 0.9356 | 0.0 | 0.5233 | 0.0064 | nan | 0.0010 | 0.0005 | 0.0 | 0.8708 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7632 | 0.0005 | 0.0000 | 0.0098 | 0.0 | nan | 0.0008 | 0.0 | 0.0 | 0.0 | 0.9782 | 0.1240 | 0.8748 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5672 | 0.7591 | 0.0 | 0.3992 | 0.0064 | 0.0 | 0.0010 | 0.0005 | 0.0 | 0.7148 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5655 | 0.0004 | 0.0000 | 0.0086 | 0.0 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.6252 | 0.1144 | 0.7905 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 2.0019 | 0.8 | 80 | 1.9809 | 0.1509 | 0.1995 | 0.7427 | nan | 0.8556 | 0.9209 | 0.0 | 0.5549 | 0.0048 | nan | 0.0000 | 0.0 | 0.0 | 0.8962 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8344 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9686 | 0.2662 | 0.8840 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5881 | 0.7793 | 0.0 | 0.4554 | 0.0048 | nan | 0.0000 | 0.0 | 0.0 | 0.7039 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5763 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6579 | 0.2418 | 0.8194 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9575 | 1.0 | 100 | 1.8123 | 0.1525 | 0.2015 | 0.7465 | nan | 0.8756 | 0.9349 | 0.0 | 0.4741 | 0.0042 | nan | 0.0 | 0.0 | 0.0 | 0.9205 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7650 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9646 | 0.4191 | 0.8883 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5946 | 0.7767 | 0.0 | 0.4219 | 0.0042 | nan | 0.0 | 0.0 | 0.0 | 0.6575 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5673 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6760 | 0.3757 | 0.8070 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9557 | 1.2 | 120 | 1.7501 | 0.1667 | 0.2075 | 0.7580 | nan | 0.8464 | 0.9461 | 0.0 | 0.5353 | 0.0052 | nan | 0.0 | 0.0 | 0.0 | 0.8843 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8335 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9601 | 0.5422 | 0.8780 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6091 | 0.7752 | 0.0 | 0.4861 | 0.0052 | nan | 0.0 | 0.0 | 0.0 | 0.7197 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5675 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7028 | 0.4764 | 0.8254 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 2.0378 | 1.4 | 140 | 1.6594 | 0.1692 | 0.2095 | 0.7627 | nan | 0.8560 | 0.9518 | 0.0 | 0.5336 | 0.0036 | nan | 0.0 | 0.0 | 0.0 | 0.8866 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8332 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9562 | 0.6099 | 0.8640 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6235 | 0.7756 | 0.0 | 0.4882 | 0.0036 | nan | 0.0 | 0.0 | 0.0 | 0.7252 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5733 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7120 | 0.5260 | 0.8185 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8032 | 1.6 | 160 | 1.6499 | 0.1715 | 0.2141 | 0.7687 | nan | 0.8843 | 0.9420 | 0.0 | 0.5999 | 0.0053 | nan | 0.0 | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8384 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9560 | 0.6342 | 0.8765 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6322 | 0.7961 | 0.0 | 0.5077 | 0.0053 | nan | 0.0 | 0.0 | 0.0 | 0.7154 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5802 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7138 | 0.5379 | 0.8264 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7118 | 1.8 | 180 | 1.6190 | 0.1711 | 0.2138 | 0.7682 | nan | 0.9177 | 0.9317 | 0.0 | 0.5441 | 0.0058 | nan | 0.0 | 0.0 | 0.0 | 0.8963 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8505 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9522 | 0.6567 | 0.8742 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5990 | 0.8121 | 0.0 | 0.4792 | 0.0058 | nan | 0.0 | 0.0 | 0.0 | 0.7249 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5825 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7221 | 0.5512 | 0.8274 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.5752 | 2.0 | 200 | 1.6073 | 0.1725 | 0.2142 | 0.7712 | nan | 0.8994 | 0.9458 | 0.0 | 0.5523 | 0.0046 | nan | 0.0 | 0.0 | 0.0 | 0.8918 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8587 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9487 | 0.6545 | 0.8834 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6288 | 0.7990 | 0.0 | 0.4898 | 0.0046 | nan | 0.0 | 0.0 | 0.0 | 0.7296 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5826 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7290 | 0.5528 | 0.8323 | 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": [
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",
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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",
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",
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,
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,
77
+ "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
105
+ },
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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": [
123
+ 7,
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+ 3,
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+ 3,
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+ 3
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