End of training

README.md

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+---
+license: other
+base_model: nvidia/mit-b3
+tags:
+- generated_from_trainer
+model-index:
+- name: segformer_cracks_v2
+  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. -->
+
+# segformer_cracks_v2
+
+This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.0537
+- eval_mean_iou: 0.7604
+- eval_mean_accuracy: 0.8281
+- eval_overall_accuracy: 0.9788
+- eval_per_category_iou: [0.9782807410260149, 0.5424475354128461]
+- eval_per_category_accuracy: [0.9911386477455301, 0.6649739140722758]
+- eval_runtime: 1877.8784
+- eval_samples_per_second: 1.459
+- eval_steps_per_second: 0.365
+- epoch: 1.0
+- step: 1370
+
+## 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: 8
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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