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cephelos
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dungeon-maps-seg-v0.0.1

Image Segmentation
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segformer
vision
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  1. README.md +132 -1
  2. config.json +82 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +39 -0
  5. runs/May08_16-16-07_DESKTOP-15M447P/events.out.tfevents.1715199374.DESKTOP-15M447P.37300.3 +3 -0
  6. runs/May08_16-30-36_DESKTOP-15M447P/events.out.tfevents.1715200246.DESKTOP-15M447P.13804.0 +3 -0
  7. runs/May08_16-30-36_DESKTOP-15M447P/events.out.tfevents.1715200877.DESKTOP-15M447P.13804.1 +3 -0
  8. training_args.bin +3 -0
README.md CHANGED
@@ -1,3 +1,134 @@
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  ---
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- license: mit
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: other
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+ base_model: nvidia/mit-b0
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: dungeon-maps-seg-v0.0.1
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+ results: []
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  ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # dungeon-maps-seg-v0.0.1
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the cephelos/dungeon-maps-seg dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0361
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+ - Mean Iou: 0.9518
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+ - Mean Accuracy: 0.9783
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+ - Overall Accuracy: 0.9893
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Room: 0.9923
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+ - Accuracy Wall: 0.9490
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+ - Accuracy Outside: 0.9935
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+ - Iou Unlabeled: nan
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+ - Iou Room: 0.9857
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+ - Iou Wall: 0.8788
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+ - Iou Outside: 0.9911
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Room | Accuracy Wall | Accuracy Outside | Iou Unlabeled | Iou Room | Iou Wall | Iou Outside |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:----------------:|:-------------:|:--------:|:--------:|:-----------:|
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+ | 0.2922 | 0.7692 | 20 | 0.2745 | 0.8581 | 0.9561 | 0.9598 | nan | 0.9526 | 0.9466 | 0.9690 | nan | 0.9466 | 0.6646 | 0.9632 |
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+ | 0.2099 | 1.5385 | 40 | 0.2072 | 0.8639 | 0.9584 | 0.9625 | nan | 0.9680 | 0.9472 | 0.9599 | nan | 0.9600 | 0.6732 | 0.9584 |
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+ | 0.2009 | 2.3077 | 60 | 0.1688 | 0.8968 | 0.9623 | 0.9741 | nan | 0.9718 | 0.9316 | 0.9835 | nan | 0.9649 | 0.7477 | 0.9778 |
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+ | 0.1258 | 3.0769 | 80 | 0.1482 | 0.8991 | 0.9676 | 0.9745 | nan | 0.9773 | 0.9492 | 0.9762 | nan | 0.9708 | 0.7529 | 0.9736 |
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+ | 0.1624 | 3.8462 | 100 | 0.1333 | 0.9115 | 0.9682 | 0.9785 | nan | 0.9807 | 0.9410 | 0.9829 | nan | 0.9734 | 0.7817 | 0.9795 |
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+ | 0.1098 | 4.6154 | 120 | 0.1079 | 0.9173 | 0.9624 | 0.9805 | nan | 0.9859 | 0.9145 | 0.9868 | nan | 0.9753 | 0.7950 | 0.9817 |
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+ | 0.1629 | 5.3846 | 140 | 0.1041 | 0.9195 | 0.9711 | 0.9806 | nan | 0.9790 | 0.9462 | 0.9881 | nan | 0.9738 | 0.8013 | 0.9833 |
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+ | 0.1243 | 6.1538 | 160 | 0.0872 | 0.9243 | 0.9675 | 0.9821 | nan | 0.9852 | 0.9288 | 0.9884 | nan | 0.9766 | 0.8125 | 0.9836 |
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+ | 0.0974 | 6.9231 | 180 | 0.0996 | 0.9217 | 0.9731 | 0.9811 | nan | 0.9754 | 0.9525 | 0.9915 | nan | 0.9717 | 0.8073 | 0.9861 |
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+ | 0.0861 | 7.6923 | 200 | 0.0798 | 0.9248 | 0.9706 | 0.9821 | nan | 0.9829 | 0.9403 | 0.9886 | nan | 0.9764 | 0.8142 | 0.9836 |
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+ | 0.0928 | 8.4615 | 220 | 0.0718 | 0.9276 | 0.9740 | 0.9828 | nan | 0.9830 | 0.9507 | 0.9882 | nan | 0.9773 | 0.8209 | 0.9847 |
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+ | 0.0583 | 9.2308 | 240 | 0.0726 | 0.9240 | 0.9686 | 0.9822 | nan | 0.9870 | 0.9326 | 0.9862 | nan | 0.9789 | 0.8111 | 0.9821 |
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+ | 0.0886 | 10.0 | 260 | 0.0700 | 0.9296 | 0.9740 | 0.9835 | nan | 0.9845 | 0.9491 | 0.9885 | nan | 0.9786 | 0.8250 | 0.9852 |
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+ | 0.1133 | 10.7692 | 280 | 0.0651 | 0.9322 | 0.9633 | 0.9848 | nan | 0.9912 | 0.9064 | 0.9922 | nan | 0.9794 | 0.8301 | 0.9872 |
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+ | 0.0821 | 11.5385 | 300 | 0.0616 | 0.9302 | 0.9721 | 0.9836 | nan | 0.9833 | 0.9417 | 0.9912 | nan | 0.9779 | 0.8270 | 0.9857 |
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+ | 0.07 | 12.3077 | 320 | 0.0586 | 0.9394 | 0.9690 | 0.9864 | nan | 0.9896 | 0.9232 | 0.9942 | nan | 0.9810 | 0.8485 | 0.9887 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.2
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+ - Pytorch 2.2.0+cpu
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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+ "num_channels": 3,
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+ "transformers_version": "4.40.2"
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