update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-segments-sidewalk-4
+  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-4
+
+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: 1.6258
+- Mean Iou: 0.1481
+- Mean Accuracy: 0.1991
+- Overall Accuracy: 0.7316
+- Per Category Iou: [nan, 0.4971884694242825, 0.7844619900838784, 0.0, 0.10165655377640956, 0.007428563507709108, nan, 4.566798099115959e-06, 0.0, 0.0, 0.5570746278221521, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.534278997386317, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7557693923373933, 0.5270379031768208, 0.8254522211471568, 0.0, 0.0, 0.0, 0.0]
+- Per Category Accuracy: [nan, 0.8698779680369205, 0.9122325676343133, 0.0, 0.10179229832932858, 0.007508413919135004, nan, 4.566798099115959e-06, 0.0, 0.0, 0.8968168359562617, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8492049383357001, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9388033874781816, 0.6627890453030717, 0.9334458854084583, 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                               | Per Category Accuracy                                                                                                                                                                                                                                                                                                                       |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 1.7912        | 1.0   | 25   | 1.6392          | 0.1412   | 0.1911        | 0.7210           | [nan, 0.48942576059104514, 0.7754689525048201, 0.0, 0.031932013148008094, 0.004348266117522573, nan, 1.5527099355168697e-05, 0.0, 0.0, 0.5356571432088642, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5243044552616699, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7355207837531991, 0.4479559177066271, 0.8315839315332364, 0.0, 0.0, 0.0, 0.0] | [nan, 0.8476069713517648, 0.9129050708992534, 0.0, 0.03194435645315849, 0.004370669306327572, nan, 1.552711353699426e-05, 0.0, 0.0, 0.897824434787493, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8555478632753987, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9510113270409175, 0.5116786406550935, 0.9122706949370997, 0.0, 0.0, 0.0, 0.0]  |
+| 1.7531        | 2.0   | 50   | 1.6258          | 0.1481   | 0.1991        | 0.7316           | [nan, 0.4971884694242825, 0.7844619900838784, 0.0, 0.10165655377640956, 0.007428563507709108, nan, 4.566798099115959e-06, 0.0, 0.0, 0.5570746278221521, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.534278997386317, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7557693923373933, 0.5270379031768208, 0.8254522211471568, 0.0, 0.0, 0.0, 0.0]     | [nan, 0.8698779680369205, 0.9122325676343133, 0.0, 0.10179229832932858, 0.007508413919135004, nan, 4.566798099115959e-06, 0.0, 0.0, 0.8968168359562617, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8492049383357001, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9388033874781816, 0.6627890453030717, 0.9334458854084583, 0.0, 0.0, 0.0, 0.0] |
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0
+- Datasets 2.2.2
+- Tokenizers 0.12.1