nielsr
/

sidewalk-semantic-demo

+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+model-index:
+- name: sidewalk-semantic-demo
+  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. -->
+# sidewalk-semantic-demo
+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: 1.7591
+- Mean Iou: 0.1135
+- Mean Accuracy: 0.1608
+- Overall Accuracy: 0.6553
+- Per Category Iou: [nan, 0.38512238586129177, 0.723869670479682, 3.007496184239216e-05, 0.04329871029371091, 0.0006725029325634934, nan, 0.0, 0.0, 0.0, 0.5420712902837528, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939727049879936, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5630706428968278, 0.2911849732223226, 0.5899473333836793, 0.0, 0.0, 1.723395088323998e-05, 0.0]
+- Per Category Accuracy: [nan, 0.6995968221991989, 0.8870903675336742, 3.007496184239216e-05, 0.043772127605383085, 0.0006731284624713075, nan, 0.0, 0.0, 0.0, 0.8074880705716012, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8257698903048035, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9746918606102934, 0.3057553223999185, 0.6001142624744604, 0.0, 0.0, 1.7275073149137866e-05, 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: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                  | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                             |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 2.3589        | 1.0   | 53   | 1.9020          | 0.1014   | 0.1491        | 0.6442           | [0.0, 0.3612513514640175, 0.6751826209974531, 0.0, 0.030376890155720412, 0.0008039971158010613, nan, 2.235273737210043e-05, 0.0, 0.0, 0.5369771616036864, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4924640887729494, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5705205266526164, 0.07944837262494953, 0.5986634961452602, 0.0, 0.0, 0.00011218284533795612, 0.0] | [nan, 0.523053840654786, 0.9469253318772407, 0.0, 0.030589314463641413, 0.0008054985216698098, nan, 2.2371239534454507e-05, 0.0, 0.0, 0.8528562962514211, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7547252442297603, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9698553453075568, 0.08054302832748386, 0.6107703679316233, 0.0, 0.0, 0.00011444735961303836, 0.0] |
+| 2.1214        | 2.0   | 106  | 1.7800          | 0.1158   | 0.1627        | 0.6622           | [nan, 0.3912271306195065, 0.7114203717790301, 0.0001503748092119608, 0.04491329385698775, 0.0008871978593462472, nan, 1.3975654410017748e-06, 0.0, 0.0, 0.5167420849064452, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.49676247687874375, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5965069148571663, 0.3115535309159788, 0.636016670211685, 0.0, 0.0, 0.0, 0.0]   | [nan, 0.6306423988442347, 0.9198450793635351, 0.0001503748092119608, 0.045391490029595895, 0.0008886008009872551, nan, 1.3982024709034067e-06, 0.0, 0.0, 0.8587918189550764, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8103648148965297, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9600035488335386, 0.3307256120335472, 0.6505175702762634, 0.0, 0.0, 0.0, 0.0]  |
+| 1.9022        | 3.0   | 159  | 1.7591          | 0.1135   | 0.1608        | 0.6553           | [nan, 0.38512238586129177, 0.723869670479682, 3.007496184239216e-05, 0.04329871029371091, 0.0006725029325634934, nan, 0.0, 0.0, 0.0, 0.5420712902837528, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939727049879936, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5630706428968278, 0.2911849732223226, 0.5899473333836793, 0.0, 0.0, 1.723395088323998e-05, 0.0]    | [nan, 0.6995968221991989, 0.8870903675336742, 3.007496184239216e-05, 0.043772127605383085, 0.0006731284624713075, nan, 0.0, 0.0, 0.0, 0.8074880705716012, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8257698903048035, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9746918606102934, 0.3057553223999185, 0.6001142624744604, 0.0, 0.0, 1.7275073149137866e-05, 0.0]  |
+### Framework versions
+- Transformers 4.17.0
+- Pytorch 1.10.0+cu111
+- Datasets 2.0.0
+- Tokenizers 0.11.6