update model card README.md

README.md

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+---
+license: other
+base_model: nvidia/mit-b5
+tags:
+- generated_from_trainer
+model-index:
+- name: segcrack9k_conglomerate_segformer_aug
+  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. -->
+
+# segcrack9k_conglomerate_segformer_aug
+
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0362
+- Mean Iou: 0.3412
+- Mean Accuracy: 0.6823
+- Overall Accuracy: 0.6823
+- Accuracy Background: nan
+- Accuracy Crack: 0.6823
+- Iou Background: 0.0
+- Iou Crack: 0.6823
+
+## 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: 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: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
+| 0.0323        | 0.14  | 1000 | 0.0445          | 0.3573   | 0.7146        | 0.7146           | nan                 | 0.7146         | 0.0            | 0.7146    |
+| 0.0222        | 0.27  | 2000 | 0.0394          | 0.3591   | 0.7181        | 0.7181           | nan                 | 0.7181         | 0.0            | 0.7181    |
+| 0.0335        | 0.41  | 3000 | 0.0404          | 0.2907   | 0.5813        | 0.5813           | nan                 | 0.5813         | 0.0            | 0.5813    |
+| 0.013         | 0.54  | 4000 | 0.0384          | 0.3244   | 0.6489        | 0.6489           | nan                 | 0.6489         | 0.0            | 0.6489    |
+| 0.0159        | 0.68  | 5000 | 0.0382          | 0.3088   | 0.6176        | 0.6176           | nan                 | 0.6176         | 0.0            | 0.6176    |
+| 0.0608        | 0.81  | 6000 | 0.0366          | 0.3251   | 0.6502        | 0.6502           | nan                 | 0.6502         | 0.0            | 0.6502    |
+| 0.1738        | 0.95  | 7000 | 0.0362          | 0.3412   | 0.6823        | 0.6823           | nan                 | 0.6823         | 0.0            | 0.6823    |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.3
+- Tokenizers 0.13.3