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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/Jul31_13-38-05_fca287a342ce/events.out.tfevents.1722433383.fca287a342ce.420.0 +3 -0
  5. runs/Jul31_14-32-30_fca287a342ce/events.out.tfevents.1722436438.fca287a342ce.420.1 +3 -0
  6. runs/Jul31_14-34-50_fca287a342ce/events.out.tfevents.1722436495.fca287a342ce.420.2 +3 -0
  7. runs/Jul31_14-37-06_fca287a342ce/events.out.tfevents.1722436637.fca287a342ce.420.3 +3 -0
  8. 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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- ## Bias, Risks, and Limitations
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- ### Recommendations
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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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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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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: 1.1574
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+ - Mean Iou: 0.1657
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+ - Mean Accuracy: 0.2143
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+ - Overall Accuracy: 0.7508
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8145
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+ - Accuracy Flat-sidewalk: 0.9504
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.5828
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+ - Accuracy Flat-parkingdriveway: 0.0118
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.0001
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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: 0.8895
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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: 0.8937
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0389
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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: 0.9244
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+ - Accuracy Nature-terrain: 0.8287
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+ - Accuracy Sky: 0.9224
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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: 0.5381
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+ - Iou Flat-sidewalk: 0.7939
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.5124
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+ - Iou Flat-parkingdriveway: 0.0117
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.0001
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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: 0.6117
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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: 0.5570
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0365
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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: 0.7504
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+ - Iou Nature-terrain: 0.6347
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+ - Iou Sky: 0.8566
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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: 10
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+ - eval_batch_size: 10
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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: 5
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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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+ | 2.6835 | 0.25 | 20 | 2.9300 | 0.0679 | 0.1189 | 0.5663 | nan | 0.0864 | 0.9582 | 0.0005 | 0.0171 | 0.0000 | nan | 0.0049 | 0.0042 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7913 | 0.0 | 0.0535 | 0.0 | 0.0 | nan | 0.0029 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.0016 | 0.1138 | 0.0168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0824 | 0.5815 | 0.0005 | 0.0168 | 0.0000 | 0.0 | 0.0045 | 0.0040 | 0.0 | 0.5349 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4154 | 0.0 | 0.0414 | 0.0 | 0.0 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.5765 | 0.0016 | 0.1137 | 0.0025 | 0.0 | 0.0 | 0.0 |
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+ | 2.3707 | 0.5 | 40 | 2.1968 | 0.0875 | 0.1387 | 0.6231 | nan | 0.5613 | 0.9361 | 0.0 | 0.0210 | 0.0003 | nan | 0.0019 | 0.0 | 0.0 | 0.7965 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8356 | 0.0 | 0.0281 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9513 | 0.0032 | 0.3006 | 0.0011 | 0.0 | 0.0 | 0.0 | nan | 0.3878 | 0.6646 | 0.0 | 0.0208 | 0.0003 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.5587 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4439 | 0.0 | 0.0249 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5675 | 0.0032 | 0.3003 | 0.0009 | 0.0 | 0.0 | 0.0 |
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+ | 2.0797 | 0.75 | 60 | 1.9742 | 0.1106 | 0.1579 | 0.6543 | nan | 0.7248 | 0.9240 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.8368 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8428 | 0.0 | 0.0254 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9498 | 0.0444 | 0.7003 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4154 | 0.7036 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.5498 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4939 | 0.0 | 0.0240 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6151 | 0.0426 | 0.6908 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9067 | 1.0 | 80 | 1.7288 | 0.1234 | 0.1699 | 0.6749 | nan | 0.7502 | 0.9269 | 0.0 | 0.0766 | 0.0014 | nan | 0.0000 | 0.0 | 0.0 | 0.8214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.0111 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9451 | 0.2194 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4498 | 0.7152 | 0.0 | 0.0761 | 0.0013 | nan | 0.0000 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5007 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6444 | 0.1954 | 0.7863 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.6673 | 1.25 | 100 | 1.6680 | 0.1258 | 0.1752 | 0.6789 | nan | 0.8230 | 0.9033 | 0.0 | 0.0493 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.8676 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8389 | 0.0 | 0.0075 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9609 | 0.2915 | 0.8618 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4254 | 0.7481 | 0.0 | 0.0488 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.5539 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5266 | 0.0 | 0.0074 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6400 | 0.2581 | 0.8161 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7134 | 1.5 | 120 | 1.5470 | 0.1376 | 0.1870 | 0.6997 | nan | 0.7864 | 0.9287 | 0.0 | 0.1234 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.8766 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8540 | 0.0 | 0.0060 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9433 | 0.5618 | 0.9008 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4735 | 0.7320 | 0.0 | 0.1216 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.5599 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5319 | 0.0 | 0.0059 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6897 | 0.4601 | 0.8255 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4384 | 1.75 | 140 | 1.4997 | 0.1458 | 0.1931 | 0.7143 | nan | 0.7973 | 0.9446 | 0.0 | 0.2050 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.8691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9304 | 0.6705 | 0.8889 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4842 | 0.7479 | 0.0 | 0.1960 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.5860 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5294 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7244 | 0.5600 | 0.8314 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4101 | 2.0 | 160 | 1.4325 | 0.1485 | 0.1990 | 0.7167 | nan | 0.8247 | 0.9212 | 0.0 | 0.2410 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8896 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9131 | 0.7902 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4675 | 0.7648 | 0.0 | 0.2283 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.5777 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5259 | 0.0 | 0.0041 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7388 | 0.6051 | 0.8352 | 0.0 | 0.0 | 0.0 | 0.0 |
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+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.42.4
149
+ - Pytorch 2.3.1+cu121
150
+ - Datasets 2.20.0
151
+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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