End of training

README.md

@@ -1,201 +1,80 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b1
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b1-finetuned-sudoku
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# segformer-b1-finetuned-sudoku
 
+This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the mrkprc1/SudokuBoundaries2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7703
+- Mean Iou: 0.0967
+- Mean Accuracy: 0.1934
+- Overall Accuracy: 0.1934
+- Accuracy Unlabelled: nan
+- Accuracy Sudoku-boundary: 0.1934
+- Iou Unlabelled: 0.0
+- Iou Sudoku-boundary: 0.1934
 
+## Model description
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Training and evaluation data
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+More information needed
 
-### Model Sources [optional]
+## Training procedure
 
-<!-- Provide the basic links for the model. -->
+### Training hyperparameters
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 50
 
-## Uses
+### Training results
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
+| 0.6531        | 3.33  | 20   | 0.7016          | 0.1433   | 0.2867        | 0.2867           | nan                 | 0.2867                   | 0.0            | 0.2867              |
+| 0.7654        | 6.67  | 40   | 0.7142          | 0.3064   | 0.6129        | 0.6129           | nan                 | 0.6129                   | 0.0            | 0.6129              |
+| 0.4761        | 10.0  | 60   | 1.0391          | 0.0002   | 0.0005        | 0.0005           | nan                 | 0.0005                   | 0.0            | 0.0005              |
+| 0.7746        | 13.33 | 80   | 1.7648          | 0.0      | 0.0           | 0.0              | nan                 | 0.0                      | 0.0            | 0.0                 |
+| 0.5488        | 16.67 | 100  | 1.2288          | 0.0      | 0.0           | 0.0              | nan                 | 0.0                      | 0.0            | 0.0                 |
+| 0.6242        | 20.0  | 120  | 1.5012          | 0.0      | 0.0           | 0.0              | nan                 | 0.0                      | 0.0            | 0.0                 |
+| 0.5423        | 23.33 | 140  | 0.9650          | 0.0029   | 0.0059        | 0.0059           | nan                 | 0.0059                   | 0.0            | 0.0059              |
+| 0.521         | 26.67 | 160  | 0.8594          | 0.0197   | 0.0393        | 0.0393           | nan                 | 0.0393                   | 0.0            | 0.0393              |
+| 0.5655        | 30.0  | 180  | 0.7950          | 0.0527   | 0.1055        | 0.1055           | nan                 | 0.1055                   | 0.0            | 0.1055              |
+| 0.4229        | 33.33 | 200  | 0.7910          | 0.0982   | 0.1964        | 0.1964           | nan                 | 0.1964                   | 0.0            | 0.1964              |
+| 0.288         | 36.67 | 220  | 0.7591          | 0.1358   | 0.2715        | 0.2715           | nan                 | 0.2715                   | 0.0            | 0.2715              |
+| 0.2002        | 40.0  | 240  | 0.7395          | 0.2414   | 0.4828        | 0.4828           | nan                 | 0.4828                   | 0.0            | 0.4828              |
+| 0.6014        | 43.33 | 260  | 0.7405          | 0.2644   | 0.5289        | 0.5289           | nan                 | 0.5289                   | 0.0            | 0.5289              |
+| 0.4336        | 46.67 | 280  | 0.7423          | 0.1751   | 0.3502        | 0.3502           | nan                 | 0.3502                   | 0.0            | 0.3502              |
+| 0.565         | 50.0  | 300  | 0.7703          | 0.0967   | 0.1934        | 0.1934           | nan                 | 0.1934                   | 0.0            | 0.1934              |
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
 
+### Framework versions
 
+- Transformers 4.37.1
+- Pytorch 2.1.2
+- Datasets 2.16.1
+- Tokenizers 0.15.1

config.json

@@ -0,0 +1,78 @@
+{
+  "_name_or_path": "nvidia/mit-b1",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "unlabelled",
+    "1": "sudoku-boundary"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "sudoku-boundary": 1,
+    "unlabelled": 0
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.1"
+}

model.safetensors

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+size 54737376

training_args.bin

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