PushkarA07
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README.md
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library_name: transformers
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---
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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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### Direct Use
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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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<!-- 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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## 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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## Training Details
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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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[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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- **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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#### 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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[More Information Needed]
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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-breastcancer-oct-1
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results: []
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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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# segformer-b0-finetuned-breastcancer-oct-1
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the as-cle-bert/breastcancer-semantic-segmentation dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8847
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- Mean Iou: 0.3706
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- Mean Accuracy: 0.6794
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- Overall Accuracy: 0.9001
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- Accuracy Background: nan
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- Accuracy Benign Breast Cancer: 0.4671
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- Accuracy Malignant Breast Cancer: 0.6340
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- Accuracy Ignore: 0.9373
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- Iou Background: 0.0
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- Iou Benign Breast Cancer: 0.2793
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- Iou Malignant Breast Cancer: 0.2886
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- Iou Ignore: 0.9144
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 6e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Benign Breast Cancer | Accuracy Malignant Breast Cancer | Accuracy Ignore | Iou Background | Iou Benign Breast Cancer | Iou Malignant Breast Cancer | Iou Ignore |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-----------------------------:|:--------------------------------:|:---------------:|:--------------:|:------------------------:|:---------------------------:|:----------:|
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| 1.1874 | 0.625 | 10 | 1.3226 | 0.2354 | 0.6369 | 0.6665 | nan | 0.5183 | 0.7181 | 0.6743 | 0.0 | 0.1107 | 0.1633 | 0.6677 |
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| 1.1383 | 1.25 | 20 | 1.1954 | 0.3233 | 0.6755 | 0.8512 | nan | 0.4058 | 0.7368 | 0.8840 | 0.0 | 0.2166 | 0.2110 | 0.8656 |
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| 1.0144 | 1.875 | 30 | 1.1280 | 0.3353 | 0.7478 | 0.8223 | nan | 0.7394 | 0.6711 | 0.8329 | 0.0 | 0.2411 | 0.2773 | 0.8228 |
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| 0.9034 | 2.5 | 40 | 0.9430 | 0.3031 | 0.6187 | 0.8756 | nan | 0.1216 | 0.8078 | 0.9267 | 0.0 | 0.0987 | 0.2063 | 0.9073 |
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| 1.0802 | 3.125 | 50 | 0.8730 | 0.3152 | 0.6227 | 0.8809 | nan | 0.1788 | 0.7588 | 0.9306 | 0.0 | 0.1297 | 0.2209 | 0.9101 |
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| 0.8259 | 3.75 | 60 | 0.8087 | 0.3601 | 0.6257 | 0.9087 | nan | 0.3522 | 0.5685 | 0.9564 | 0.0 | 0.2487 | 0.2669 | 0.9247 |
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| 0.8869 | 4.375 | 70 | 0.8426 | 0.3574 | 0.6592 | 0.8962 | nan | 0.4059 | 0.6350 | 0.9369 | 0.0 | 0.2526 | 0.2641 | 0.9129 |
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| 0.8538 | 5.0 | 80 | 0.8847 | 0.3706 | 0.6794 | 0.9001 | nan | 0.4671 | 0.6340 | 0.9373 | 0.0 | 0.2793 | 0.2886 | 0.9144 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.19.1
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config.json
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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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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout_prob": 0.1,
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"decoder_hidden_size": 256,
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"depths": [
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],
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"downsampling_rates": [
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],
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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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160,
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],
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"id2label": {
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"0": "background",
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"1": "benign_breast_cancer",
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"2": "malignant_breast_cancer",
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"3": "ignore"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"background": 0,
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"benign_breast_cancer": 1,
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"ignore": 3,
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"malignant_breast_cancer": 2
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},
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"layer_norm_eps": 1e-06,
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"mlp_ratios": [
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],
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"model_type": "segformer",
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"num_attention_heads": [
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],
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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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"semantic_loss_ignore_index": 255,
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"sr_ratios": [
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],
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"strides": [
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"torch_dtype": "float32",
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"transformers_version": "4.44.2"
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}
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runs/Oct01_08-20-51_55df3eb193c7/events.out.tfevents.1727770854.55df3eb193c7.213.1
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training_args.bin
ADDED
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