update model card README.md

README.md

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+---
+license: other
+tags:
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-segments-sidewalk-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. -->
+
+# segformer-b0-finetuned-segments-sidewalk-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: 1.6110
+- Mean Iou: 0.1367
+- Mean Accuracy: 0.1875
+- Overall Accuracy: 0.6881
+- Per Category Iou: [nan, 0.4358992825292338, 0.7376212533796545, 0.0, 0.02621246785982382, 0.00043211553703970075, nan, 8.015736841193088e-05, 0.0, 0.0, 0.5772424406028427, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.5033973978720222, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6694084376012885, 0.36677387384992766, 0.7828643395512115, 0.0, 0.0, 0.0, 0.0]
+- Per Category Accuracy: [nan, 0.7389109053459296, 0.9324628469379894, 0.0, 0.02623033308234139, 0.0004332220803205606, nan, 8.024933685852042e-05, 0.0, 0.0, 0.8439578372191763, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8847599631606713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9401313327814339, 0.38739120652321696, 0.8720917833707338, 0.0, 0.0, 0.0, 0.0]
+
+## 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: 4
+- eval_batch_size: 4
+- 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 | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                        | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                               |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 1.9298        | 0.5   | 100  | 1.7681          | 0.1199   | 0.1738        | 0.6593           | [nan, 0.40090810552182926, 0.7239864361905679, 0.0, 0.0017647225539722043, 0.00039874720019120934, nan, 0.0001017888855199896, 0.0, 0.0, 0.5266858358368541, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.4982673421879428, 0.0, 0.00013373579730320418, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6162371375150854, 0.06163531325154459, 0.7657580090286384, 0.0, 0.0, 0.00018678310951226077, 0.0] | [nan, 0.7294785690321806, 0.9040101249330418, 0.0, 0.001765437619648865, 0.00039998860566583264, nan, 0.0001019383468202827, 0.0, 0.0, 0.8602156135201492, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8580331196294607, 0.0, 0.00013373747781887638, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9448016748237693, 0.06210387463397946, 0.852450875265527, 0.0, 0.0, 0.0001871709770030242, 0.0] |
+| 2.0284        | 1.0   | 200  | 1.6110          | 0.1367   | 0.1875        | 0.6881           | [nan, 0.4358992825292338, 0.7376212533796545, 0.0, 0.02621246785982382, 0.00043211553703970075, nan, 8.015736841193088e-05, 0.0, 0.0, 0.5772424406028427, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.5033973978720222, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6694084376012885, 0.36677387384992766, 0.7828643395512115, 0.0, 0.0, 0.0, 0.0]                                          | [nan, 0.7389109053459296, 0.9324628469379894, 0.0, 0.02623033308234139, 0.0004332220803205606, nan, 8.024933685852042e-05, 0.0, 0.0, 0.8439578372191763, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8847599631606713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9401313327814339, 0.38739120652321696, 0.8720917833707338, 0.0, 0.0, 0.0, 0.0]                                       |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1
+- Datasets 2.4.0
+- Tokenizers 0.12.1