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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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- - **Language(s) (NLP):** [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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-
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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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-
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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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- ### 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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- ### 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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- [More Information Needed]
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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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- [More Information Needed]
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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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-seed63-apr-13-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-seed63-apr-13-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_401_6_lbls dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7138
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+ - Mean Iou: 0.1266
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+ - Mean Accuracy: 0.2136
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+ - Overall Accuracy: 0.4273
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Lv: 0.6939
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+ - Accuracy Rv: 0.0982
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+ - Accuracy Ra: 0.1706
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+ - Accuracy La: 0.5041
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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: 0.0557
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+ - Accuracy Vsd: 0.2283
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+ - Accuracy Ak: 0.3849
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+ - Iou Unlabeled: 0.0
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+ - Iou Lv: 0.4965
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+ - Iou Rv: 0.0899
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+ - Iou Ra: 0.1288
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+ - Iou La: 0.2845
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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: 0.0
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+ - Iou Asd: 0.0462
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+ - Iou Vsd: 0.1513
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+ - Iou Ak: 0.3225
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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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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-06
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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: 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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+ | 2.5423 | 2.5 | 100 | 2.6367 | 0.0332 | 0.0976 | 0.0951 | nan | 0.0612 | 0.0642 | 0.0301 | 0.1898 | 0.0 | 0.0 | 0.0086 | nan | 0.0495 | 0.4697 | 0.1033 | 0.0 | 0.0573 | 0.0485 | 0.0262 | 0.1021 | 0.0 | 0.0 | 0.0019 | 0.0 | 0.0204 | 0.0612 | 0.0812 |
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+ | 2.3042 | 5.0 | 200 | 2.3925 | 0.0604 | 0.1412 | 0.1975 | nan | 0.2435 | 0.0655 | 0.1292 | 0.2869 | 0.0 | 0.0 | 0.0046 | nan | 0.0669 | 0.4894 | 0.1258 | 0.0 | 0.2144 | 0.0516 | 0.1074 | 0.1515 | 0.0 | 0.0 | 0.0017 | 0.0 | 0.0243 | 0.0670 | 0.1063 |
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+ | 2.0869 | 7.5 | 300 | 2.2183 | 0.0932 | 0.1839 | 0.3354 | nan | 0.5208 | 0.0717 | 0.1836 | 0.4192 | 0.0 | 0.0 | 0.0006 | nan | 0.0768 | 0.3608 | 0.2060 | 0.0 | 0.4077 | 0.0617 | 0.1436 | 0.2158 | 0.0 | 0.0 | 0.0003 | 0.0 | 0.0358 | 0.0787 | 0.1746 |
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+ | 2.0559 | 10.0 | 400 | 2.0298 | 0.1110 | 0.2055 | 0.3886 | nan | 0.6144 | 0.1027 | 0.1815 | 0.4598 | 0.0 | 0.0 | 0.0005 | nan | 0.0909 | 0.3011 | 0.3041 | 0.0 | 0.4559 | 0.0880 | 0.1400 | 0.2409 | 0.0 | 0.0 | 0.0003 | 0.0 | 0.0534 | 0.1001 | 0.2538 |
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+ | 1.9554 | 12.5 | 500 | 1.8871 | 0.1189 | 0.2111 | 0.4100 | nan | 0.6561 | 0.1004 | 0.1647 | 0.4900 | 0.0 | 0.0 | 0.0009 | nan | 0.0763 | 0.2611 | 0.3619 | 0.0 | 0.4739 | 0.0896 | 0.1263 | 0.2616 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0531 | 0.1207 | 0.3015 |
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+ | 2.0181 | 15.0 | 600 | 1.7720 | 0.1247 | 0.2139 | 0.4199 | nan | 0.6735 | 0.1008 | 0.1723 | 0.4898 | 0.0 | 0.0 | 0.0 | nan | 0.0706 | 0.2349 | 0.3972 | 0.0 | 0.4860 | 0.0912 | 0.1293 | 0.2720 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0532 | 0.1386 | 0.3256 |
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+ | 1.6723 | 17.5 | 700 | 1.7386 | 0.1258 | 0.2129 | 0.4251 | nan | 0.6860 | 0.1011 | 0.1724 | 0.5062 | 0.0 | 0.0 | 0.0 | nan | 0.0615 | 0.2167 | 0.3848 | 0.0 | 0.4927 | 0.0917 | 0.1304 | 0.2814 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0488 | 0.1426 | 0.3221 |
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+ | 1.5613 | 20.0 | 800 | 1.7751 | 0.1269 | 0.2151 | 0.4322 | nan | 0.7050 | 0.1020 | 0.1730 | 0.5066 | 0.0 | 0.0 | 0.0 | nan | 0.0570 | 0.2288 | 0.3788 | 0.0 | 0.4990 | 0.0927 | 0.1308 | 0.2841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0465 | 0.1502 | 0.3199 |
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+ | 1.5653 | 22.5 | 900 | 1.7222 | 0.1272 | 0.2142 | 0.4277 | nan | 0.6924 | 0.1003 | 0.1794 | 0.5018 | 0.0 | 0.0 | 0.0 | nan | 0.0568 | 0.2295 | 0.3814 | 0.0 | 0.4969 | 0.0914 | 0.1341 | 0.2837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0466 | 0.1523 | 0.3209 |
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+ | 1.5196 | 25.0 | 1000 | 1.7138 | 0.1266 | 0.2136 | 0.4273 | nan | 0.6939 | 0.0982 | 0.1706 | 0.5041 | 0.0 | 0.0 | 0.0 | nan | 0.0557 | 0.2283 | 0.3849 | 0.0 | 0.4965 | 0.0899 | 0.1288 | 0.2845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0462 | 0.1513 | 0.3225 |
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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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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.2"
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+ }
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