End of training

README.md

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+---
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: exemple2-finetuned-segments
+  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. -->
+
+# exemple2-finetuned-segments
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
+
+## 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 | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                              | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 5    | 3.5279          | 0.0125   | 0.0416        | 0.1024           | [0.003071310796148687, 0.1616174857459709, 0.0, 0.0, 0.0022739959938265523, 0.0, 0.0005909869482034998, 0.0017997990408432924, 0.0, 0.05474664289453949, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.09700429854115147, 0.0, 0.04184948021660935, 0.0006682389937106918, 0.0, 0.0, 0.00415553197202368, 0.004015113955619657, 0.0, 0.0, 6.202127329674078e-05, 0.05336540352809607, 0.0, 0.0, 0.0, 0.00025955435304111186, 0.0, 0.0] | [0.0032144853023359787, 0.18635872416872554, nan, 0.0, 0.0038356619632496504, nan, 0.0010970621048716994, 0.008956864181701621, nan, 0.06383171787997526, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.11043176613738219, nan, 0.051045006388159, 0.02862641327880683, nan, nan, 0.34138655462184875, 0.049846125151543415, 0.0, nan, 6.220517339692085e-05, 0.10777804260728432, 0.0, 0.0, 0.0, 0.0002737135463322385, 0.0, nan] |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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