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  2. config.json +98 -0
  3. model.safetensors +3 -0
  4. 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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  ---
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- # Model Card for Model ID
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-
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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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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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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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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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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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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- ## Citation [optional]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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+ license: other
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+ base_model: nvidia/mit-b3
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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-b2-seed-67-v1
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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-b2-seed-67-v1
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+
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+ This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the unreal-hug/REAL_DATASET_SEG_331 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4746
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+ - Mean Iou: 0.2841
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+ - Mean Accuracy: 0.3507
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+ - Overall Accuracy: 0.6084
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Lv: 0.7915
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+ - Accuracy Rv: 0.4646
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+ - Accuracy Ra: 0.4834
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+ - Accuracy La: 0.6858
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+ - Accuracy Vs: 0.0
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+ - Accuracy As: 0.0
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+ - Accuracy Mk: 0.0
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+ - Accuracy Tk: nan
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+ - Accuracy Asd: 0.3160
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+ - Accuracy Vsd: 0.2747
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+ - Accuracy Ak: 0.4910
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+ - Iou Unlabeled: 0.0
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+ - Iou Lv: 0.7252
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+ - Iou Rv: 0.4232
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+ - Iou Ra: 0.4411
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+ - Iou La: 0.5427
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+ - Iou Vs: 0.0
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+ - Iou As: 0.0
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+ - Iou Mk: 0.0
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+ - Iou Tk: nan
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+ - Iou Asd: 0.2832
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+ - Iou Vsd: 0.2342
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+ - Iou Ak: 0.4759
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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: 1e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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: cosine
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - training_steps: 1000
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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 Lv | Accuracy Rv | Accuracy Ra | Accuracy La | Accuracy Vs | Accuracy As | Accuracy Mk | Accuracy Tk | Accuracy Asd | Accuracy Vsd | Accuracy Ak | Iou Unlabeled | Iou Lv | Iou Rv | Iou Ra | Iou La | Iou Vs | Iou As | Iou Mk | Iou Tk | Iou Asd | Iou Vsd | Iou Ak |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-------:|:-------:|:------:|
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+ | 1.2449 | 5.88 | 100 | 1.1508 | 0.1187 | 0.1954 | 0.4575 | nan | 0.8193 | 0.0533 | 0.1371 | 0.5424 | 0.0 | 0.0 | 0.0 | nan | 0.0171 | 0.0155 | 0.3697 | 0.0 | 0.5501 | 0.0518 | 0.1253 | 0.3509 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0170 | 0.0148 | 0.3145 |
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+ | 0.7118 | 11.76 | 200 | 0.7012 | 0.1534 | 0.2007 | 0.4466 | nan | 0.7352 | 0.1138 | 0.2300 | 0.5548 | 0.0 | 0.0 | 0.0 | nan | 0.0168 | 0.0284 | 0.3280 | 0.0 | 0.6079 | 0.1081 | 0.2084 | 0.4120 | 0.0 | 0.0 | 0.0 | nan | 0.0167 | 0.0276 | 0.3064 |
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+ | 0.5567 | 17.65 | 300 | 0.5686 | 0.1896 | 0.2372 | 0.4810 | nan | 0.6994 | 0.2332 | 0.3522 | 0.5913 | 0.0 | 0.0 | 0.0 | nan | 0.0389 | 0.0765 | 0.3806 | 0.0 | 0.6382 | 0.2142 | 0.3023 | 0.4563 | 0.0 | 0.0 | 0.0 | nan | 0.0386 | 0.0714 | 0.3649 |
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+ | 0.5054 | 23.53 | 400 | 0.5441 | 0.2473 | 0.3075 | 0.5803 | nan | 0.7991 | 0.4241 | 0.4885 | 0.5970 | 0.0 | 0.0 | 0.0 | nan | 0.1535 | 0.1388 | 0.4745 | 0.0 | 0.7215 | 0.3725 | 0.4107 | 0.4908 | 0.0 | 0.0 | 0.0 | nan | 0.1486 | 0.1228 | 0.4537 |
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+ | 0.4344 | 29.41 | 500 | 0.5188 | 0.2706 | 0.3382 | 0.5967 | nan | 0.7810 | 0.4337 | 0.4668 | 0.7031 | 0.0 | 0.0 | 0.0 | nan | 0.2612 | 0.2644 | 0.4721 | 0.0 | 0.7121 | 0.3916 | 0.4164 | 0.5372 | 0.0 | 0.0 | 0.0 | nan | 0.2398 | 0.2236 | 0.4558 |
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+ | 0.3796 | 35.29 | 600 | 0.5032 | 0.2669 | 0.3315 | 0.5911 | nan | 0.7953 | 0.4343 | 0.4050 | 0.6920 | 0.0 | 0.0 | 0.0 | nan | 0.2841 | 0.2321 | 0.4717 | 0.0 | 0.7196 | 0.3965 | 0.3778 | 0.5273 | 0.0 | 0.0 | 0.0 | nan | 0.2589 | 0.1996 | 0.4568 |
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+ | 0.3888 | 41.18 | 700 | 0.4801 | 0.2798 | 0.3461 | 0.6037 | nan | 0.7862 | 0.4532 | 0.4667 | 0.6983 | 0.0 | 0.0 | 0.0 | nan | 0.3065 | 0.2590 | 0.4908 | 0.0 | 0.7192 | 0.4127 | 0.4292 | 0.5444 | 0.0 | 0.0 | 0.0 | nan | 0.2756 | 0.2216 | 0.4746 |
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+ | 0.3467 | 47.06 | 800 | 0.4753 | 0.2822 | 0.3478 | 0.6061 | nan | 0.7919 | 0.4585 | 0.4857 | 0.6814 | 0.0 | 0.0 | 0.0 | nan | 0.3131 | 0.2640 | 0.4831 | 0.0 | 0.7259 | 0.4196 | 0.4424 | 0.5402 | 0.0 | 0.0 | 0.0 | nan | 0.2813 | 0.2262 | 0.4685 |
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+ | 0.3757 | 52.94 | 900 | 0.4746 | 0.2841 | 0.3507 | 0.6084 | nan | 0.7915 | 0.4646 | 0.4834 | 0.6858 | 0.0 | 0.0 | 0.0 | nan | 0.3160 | 0.2747 | 0.4910 | 0.0 | 0.7252 | 0.4232 | 0.4411 | 0.5427 | 0.0 | 0.0 | 0.0 | nan | 0.2832 | 0.2342 | 0.4759 |
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+ | 0.3616 | 58.82 | 1000 | 0.4788 | 0.2860 | 0.3537 | 0.6116 | nan | 0.7931 | 0.4687 | 0.4837 | 0.6922 | 0.0 | 0.0 | 0.0 | nan | 0.3193 | 0.2830 | 0.4970 | 0.0 | 0.7262 | 0.4259 | 0.4411 | 0.5449 | 0.0 | 0.0 | 0.0 | nan | 0.2856 | 0.2407 | 0.4817 |
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.2+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "nvidia/mit-b3",
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+ "architectures": [
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+ "SegformerForSemanticSegmentation"
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.2"
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+ }
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