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PushkarA07
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segformer-b0-finetuned-oldapp-dec-4

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
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  2. config.json +78 -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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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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- [More Information Needed]
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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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-
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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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-
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- ## Technical Specifications [optional]
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-
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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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- [More Information Needed]
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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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- ## Glossary [optional]
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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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- [More Information Needed]
 
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  ---
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  library_name: transformers
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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-oldapp-dec-4
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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-oldapp-dec-4
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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 PushkarA07/oldapptiles5 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0403
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+ - Mean Iou: 0.4995
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+ - Mean Accuracy: 0.5
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+ - Overall Accuracy: 0.9990
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+ - Accuracy Abnormality: 0.0
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+ - Iou Abnormality: 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: 2
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+ - eval_batch_size: 2
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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: 50
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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 Abnormality | Iou Abnormality |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:|
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+ | 0.2898 | 0.7143 | 10 | 0.2757 | 0.4992 | 0.4997 | 0.9984 | 0.0 | 0.0 |
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+ | 0.2605 | 1.4286 | 20 | 0.2029 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.2573 | 2.1429 | 30 | 0.1829 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.4388 | 2.8571 | 40 | 0.1301 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1893 | 3.5714 | 50 | 0.1529 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.171 | 4.2857 | 60 | 0.1322 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1502 | 5.0 | 70 | 0.1239 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1267 | 5.7143 | 80 | 0.1285 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1421 | 6.4286 | 90 | 0.0891 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1271 | 7.1429 | 100 | 0.0684 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1206 | 7.8571 | 110 | 0.0718 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0802 | 8.5714 | 120 | 0.0802 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0765 | 9.2857 | 130 | 0.0591 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.1025 | 10.0 | 140 | 0.0541 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0711 | 10.7143 | 150 | 0.0572 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0621 | 11.4286 | 160 | 0.0488 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0611 | 12.1429 | 170 | 0.0446 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0665 | 12.8571 | 180 | 0.0383 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0625 | 13.5714 | 190 | 0.0492 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0473 | 14.2857 | 200 | 0.0454 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0379 | 15.0 | 210 | 0.0384 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0597 | 15.7143 | 220 | 0.0423 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0436 | 16.4286 | 230 | 0.0474 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0591 | 17.1429 | 240 | 0.0424 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0337 | 17.8571 | 250 | 0.0359 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0455 | 18.5714 | 260 | 0.0394 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0322 | 19.2857 | 270 | 0.0321 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0339 | 20.0 | 280 | 0.0440 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0336 | 20.7143 | 290 | 0.0459 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0357 | 21.4286 | 300 | 0.0324 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0286 | 22.1429 | 310 | 0.0264 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0301 | 22.8571 | 320 | 0.0166 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0252 | 23.5714 | 330 | 0.0229 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0296 | 24.2857 | 340 | 0.0298 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.033 | 25.0 | 350 | 0.0389 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0279 | 25.7143 | 360 | 0.0346 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0266 | 26.4286 | 370 | 0.0425 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0278 | 27.1429 | 380 | 0.0395 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0458 | 27.8571 | 390 | 0.0228 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0289 | 28.5714 | 400 | 0.0183 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0261 | 29.2857 | 410 | 0.0300 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.031 | 30.0 | 420 | 0.0295 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0342 | 30.7143 | 430 | 0.0457 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0271 | 31.4286 | 440 | 0.0349 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0198 | 32.1429 | 450 | 0.0407 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0231 | 32.8571 | 460 | 0.0443 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0392 | 33.5714 | 470 | 0.0398 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0166 | 34.2857 | 480 | 0.0206 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0215 | 35.0 | 490 | 0.0302 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0983 | 35.7143 | 500 | 0.0117 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0162 | 36.4286 | 510 | 0.0237 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0211 | 37.1429 | 520 | 0.0395 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0228 | 37.8571 | 530 | 0.0526 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0198 | 38.5714 | 540 | 0.0480 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0192 | 39.2857 | 550 | 0.0479 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0213 | 40.0 | 560 | 0.0390 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0185 | 40.7143 | 570 | 0.0456 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0179 | 41.4286 | 580 | 0.0337 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0307 | 42.1429 | 590 | 0.0460 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0199 | 42.8571 | 600 | 0.0443 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0121 | 43.5714 | 610 | 0.0313 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0184 | 44.2857 | 620 | 0.0435 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0243 | 45.0 | 630 | 0.0440 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.02 | 45.7143 | 640 | 0.0457 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0144 | 46.4286 | 650 | 0.0440 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.013 | 47.1429 | 660 | 0.0295 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0156 | 47.8571 | 670 | 0.0429 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0201 | 48.5714 | 680 | 0.0453 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.018 | 49.2857 | 690 | 0.0384 | 0.4995 | 0.5 | 0.9990 | 0.0 | 0.0 |
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+ | 0.0118 | 50.0 | 700 | 0.0403 | 0.4995 | 0.5 | 0.9990 | 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 4.46.2
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "nvidia/mit-b0",
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+ "architectures": [
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+ "SegformerForSemanticSegmentation"
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+ ],
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+ 1,
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+ 4,
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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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+ 32,
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+ "id2label": {
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+ "0": "normal",
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+ "initializer_range": 0.02,
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+ "layer_norm_eps": 1e-06,
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+ "mlp_ratios": [
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+ "model_type": "segformer",
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+ 8
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+ "num_channels": 3,
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+ "num_encoder_blocks": 4,
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+ "patch_sizes": [
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+ "reshape_last_stage": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.46.2"
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+ }
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