whyoke/segmentation_model_50ep_2

README.md

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+---
+library_name: transformers
+license: other
+base_model: nvidia/mit-b0
+tags:
+- generated_from_trainer
+model-index:
+- name: segmentation_model_50ep_2
+  results: []
+---
+
+<!-- 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. -->
+
+# segmentation_model_50ep_2
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0151
+- Mean Iou: 0.4992
+- Mean Accuracy: 0.5002
+- Overall Accuracy: 0.9980
+- Per Category Iou: [0.9979567074182948, 0.0004395926441497546]
+- Per Category Accuracy: [0.9999017103951866, 0.00046175157765122367]
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                             | Per Category Accuracy                        |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:|
+| 0.0176        | 12.1951 | 1000 | 0.0153          | 0.4991   | 0.5001        | 0.9978           | [0.9978437819175541, 0.00041657987919183504] | [0.9997885648043022, 0.00046175157765122367] |
+| 0.0173        | 24.3902 | 2000 | 0.0153          | 0.4991   | 0.5001        | 0.9978           | [0.9978095148690534, 0.0004100657472081357]  | [0.999754230969827, 0.00046175157765122367]  |
+| 0.0144        | 36.5854 | 3000 | 0.0146          | 0.4991   | 0.5001        | 0.9980           | [0.9979986133831826, 0.00026932399676811203] | [0.9999440574585123, 0.00027705094659073417] |
+| 0.0208        | 48.7805 | 4000 | 0.0151          | 0.4992   | 0.5002        | 0.9980           | [0.9979567074182948, 0.0004395926441497546]  | [0.9999017103951866, 0.00046175157765122367] |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.2.0
+- Datasets 2.4.0
+- Tokenizers 0.20.3