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segformer-b0-finetuned-segments-sidewalk-oct-22

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  1. README.md +148 -196
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. runs/Jun13_05-56-01_e29e108630c4/events.out.tfevents.1718258212.e29e108630c4.6049.2 +3 -0
  5. runs/Jun13_05-57-53_e29e108630c4/events.out.tfevents.1718258287.e29e108630c4.6049.3 +3 -0
  6. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Direct Use
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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  ---
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+ license: other
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+ base_model: nvidia/mit-b0
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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: segformer-b0-finetuned-segments-sidewalk-oct-22
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+ results: []
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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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+ # segformer-b0-finetuned-segments-sidewalk-oct-22
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2519
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+ - Mean Iou: 0.1522
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+ - Mean Accuracy: 0.2003
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+ - Overall Accuracy: 0.7240
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.7728
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+ - Accuracy Flat-sidewalk: 0.9359
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.3852
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+ - Accuracy Flat-parkingdriveway: 0.0097
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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: 0.8668
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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: 0.0
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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: 0.8650
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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: 0.9477
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+ - Accuracy Nature-terrain: 0.7234
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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: 0.5016
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+ - Iou Flat-sidewalk: 0.7469
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.3601
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+ - Iou Flat-parkingdriveway: 0.0096
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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: 0.5774
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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: 0.0
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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: 0.5352
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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: 0.7277
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+ - Iou Nature-terrain: 0.5775
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+ - Iou Sky: 0.8350
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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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+ More information needed
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+ ## Intended uses & limitations
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+ More information needed
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+ ## Training and evaluation data
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+ More information needed
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+ ## Training procedure
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+ ### Training hyperparameters
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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: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 1
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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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+ | 2.4782 | 0.05 | 20 | 2.4249 | 0.0836 | 0.1376 | 0.6010 | nan | 0.6155 | 0.8719 | 0.0 | 0.0011 | 0.0002 | nan | 0.0003 | 0.0 | 0.0 | 0.8951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7118 | 0.0 | 0.0001 | 0.0016 | 0.0 | nan | 0.0 | 0.0072 | 0.0 | 0.0 | 0.9631 | 0.0000 | 0.3367 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3522 | 0.6415 | 0.0 | 0.0011 | 0.0002 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.4197 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4674 | 0.0 | 0.0001 | 0.0015 | 0.0 | nan | 0.0 | 0.0065 | 0.0 | 0.0 | 0.5431 | 0.0000 | 0.3265 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7849 | 0.1 | 40 | 2.0726 | 0.0987 | 0.1471 | 0.6229 | nan | 0.5815 | 0.8992 | 0.0 | 0.0002 | 0.0003 | nan | 0.0007 | 0.0 | 0.0 | 0.8199 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7954 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9628 | 0.0000 | 0.6476 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3595 | 0.6501 | 0.0 | 0.0002 | 0.0003 | nan | 0.0007 | 0.0 | 0.0 | 0.4671 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4738 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.5760 | 0.0000 | 0.6303 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7025 | 0.15 | 60 | 1.8102 | 0.1062 | 0.1576 | 0.6393 | nan | 0.6636 | 0.8973 | 0.0 | 0.0002 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8097 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9416 | 0.0000 | 0.8286 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3808 | 0.6706 | 0.0 | 0.0002 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.4459 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4857 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6293 | 0.0000 | 0.7845 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7812 | 0.2 | 80 | 1.8452 | 0.1090 | 0.1569 | 0.6450 | nan | 0.7383 | 0.8884 | 0.0 | 0.0005 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8358 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9661 | 0.0067 | 0.7820 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3851 | 0.6991 | 0.0 | 0.0005 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5586 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4803 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6058 | 0.0067 | 0.7522 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9004 | 0.25 | 100 | 1.6849 | 0.1120 | 0.1600 | 0.6526 | nan | 0.7163 | 0.9044 | 0.0 | 0.0257 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8225 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8449 | 0.0 | 0.0010 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9620 | 0.0294 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3964 | 0.6985 | 0.0 | 0.0257 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5494 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4895 | 0.0 | 0.0010 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6192 | 0.0292 | 0.7757 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.6287 | 0.3 | 120 | 1.6376 | 0.1174 | 0.1684 | 0.6591 | nan | 0.7113 | 0.9101 | 0.0 | 0.0529 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.9426 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7552 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9463 | 0.2590 | 0.8098 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3969 | 0.6959 | 0.0 | 0.0527 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.4075 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5041 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6711 | 0.2464 | 0.7814 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.624 | 0.35 | 140 | 1.5312 | 0.1173 | 0.1652 | 0.6629 | nan | 0.7561 | 0.9049 | 0.0 | 0.0484 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.8051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9487 | 0.0819 | 0.8608 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4224 | 0.7010 | 0.0 | 0.0482 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.5536 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4958 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6442 | 0.0799 | 0.8072 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4764 | 0.4 | 160 | 1.5197 | 0.1251 | 0.1754 | 0.6818 | nan | 0.7557 | 0.9231 | 0.0 | 0.2734 | 0.0010 | nan | 0.0 | 0.0 | 0.0 | 0.9198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8331 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9396 | 0.1118 | 0.8534 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4637 | 0.7207 | 0.0 | 0.2624 | 0.0010 | nan | 0.0 | 0.0 | 0.0 | 0.4747 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5139 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6528 | 0.1079 | 0.8056 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8363 | 0.45 | 180 | 1.4539 | 0.1238 | 0.1687 | 0.6751 | nan | 0.6197 | 0.9574 | 0.0 | 0.0978 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.8247 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8816 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9511 | 0.1872 | 0.8765 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4542 | 0.6816 | 0.0 | 0.0952 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.5643 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5131 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6549 | 0.1782 | 0.8178 | 0.0 | 0.0 | 0.0 | 0.0 |
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+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.41.2
149
+ - Pytorch 2.3.0+cu121
150
+ - Datasets 2.19.2
151
+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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