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--- |
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license: other |
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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: dropoff-utcustom-train-SF-RGB-b5_7 |
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results: [] |
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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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# dropoff-utcustom-train-SF-RGB-b5_7 |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1841 |
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- Mean Iou: 0.7025 |
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- Mean Accuracy: 0.7532 |
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- Overall Accuracy: 0.9721 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.5145 |
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- Accuracy Undropoff: 0.9919 |
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- Iou Unlabeled: nan |
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- Iou Dropoff: 0.4336 |
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- Iou Undropoff: 0.9715 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 120 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:| |
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| 0.8255 | 5.0 | 10 | 0.7949 | 0.4128 | 0.7856 | 0.9393 | nan | 0.6179 | 0.9533 | 0.0 | 0.3007 | 0.9377 | |
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| 0.4434 | 10.0 | 20 | 0.4247 | 0.4471 | 0.7066 | 0.9705 | nan | 0.4187 | 0.9944 | 0.0 | 0.3714 | 0.9700 | |
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| 0.2107 | 15.0 | 30 | 0.2726 | 0.6711 | 0.7003 | 0.9715 | nan | 0.4046 | 0.9961 | nan | 0.3713 | 0.9710 | |
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| 0.1678 | 20.0 | 40 | 0.2388 | 0.6801 | 0.7343 | 0.9691 | nan | 0.4782 | 0.9904 | nan | 0.3917 | 0.9685 | |
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| 0.0972 | 25.0 | 50 | 0.1849 | 0.6764 | 0.7096 | 0.9715 | nan | 0.4241 | 0.9952 | nan | 0.3818 | 0.9709 | |
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| 0.0604 | 30.0 | 60 | 0.2019 | 0.4644 | 0.7568 | 0.9704 | nan | 0.5239 | 0.9897 | 0.0 | 0.4236 | 0.9697 | |
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| 0.0497 | 35.0 | 70 | 0.1793 | 0.6838 | 0.7345 | 0.9700 | nan | 0.4775 | 0.9914 | nan | 0.3983 | 0.9694 | |
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| 0.0492 | 40.0 | 80 | 0.2000 | 0.4639 | 0.7567 | 0.9702 | nan | 0.5239 | 0.9896 | 0.0 | 0.4223 | 0.9695 | |
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| 0.0409 | 45.0 | 90 | 0.1893 | 0.7030 | 0.7778 | 0.9696 | nan | 0.5687 | 0.9869 | nan | 0.4372 | 0.9688 | |
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| 0.0328 | 50.0 | 100 | 0.1842 | 0.7040 | 0.7715 | 0.9704 | nan | 0.5545 | 0.9885 | nan | 0.4382 | 0.9697 | |
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| 0.0332 | 55.0 | 110 | 0.1781 | 0.7015 | 0.7563 | 0.9715 | nan | 0.5216 | 0.9910 | nan | 0.4322 | 0.9709 | |
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| 0.0314 | 60.0 | 120 | 0.1732 | 0.6890 | 0.7305 | 0.9717 | nan | 0.4675 | 0.9935 | nan | 0.4068 | 0.9711 | |
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| 0.0318 | 65.0 | 130 | 0.1786 | 0.6971 | 0.7477 | 0.9715 | nan | 0.5037 | 0.9918 | nan | 0.4233 | 0.9709 | |
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| 0.0291 | 70.0 | 140 | 0.1814 | 0.7119 | 0.7687 | 0.9725 | nan | 0.5466 | 0.9909 | nan | 0.4521 | 0.9718 | |
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| 0.0273 | 75.0 | 150 | 0.1755 | 0.7101 | 0.7677 | 0.9722 | nan | 0.5446 | 0.9907 | nan | 0.4487 | 0.9715 | |
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| 0.0274 | 80.0 | 160 | 0.1786 | 0.7006 | 0.7494 | 0.9720 | nan | 0.5066 | 0.9922 | nan | 0.4297 | 0.9714 | |
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| 0.0248 | 85.0 | 170 | 0.1741 | 0.7029 | 0.7526 | 0.9722 | nan | 0.5131 | 0.9921 | nan | 0.4341 | 0.9716 | |
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| 0.0248 | 90.0 | 180 | 0.1832 | 0.7050 | 0.7595 | 0.9719 | nan | 0.5278 | 0.9912 | nan | 0.4387 | 0.9713 | |
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| 0.0242 | 95.0 | 190 | 0.1808 | 0.7028 | 0.7539 | 0.9720 | nan | 0.5160 | 0.9918 | nan | 0.4341 | 0.9714 | |
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| 0.024 | 100.0 | 200 | 0.1796 | 0.7022 | 0.7501 | 0.9723 | nan | 0.5077 | 0.9925 | nan | 0.4327 | 0.9717 | |
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| 0.0231 | 105.0 | 210 | 0.1835 | 0.7137 | 0.7731 | 0.9724 | nan | 0.5557 | 0.9905 | nan | 0.4556 | 0.9717 | |
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| 0.0238 | 110.0 | 220 | 0.1823 | 0.7046 | 0.7565 | 0.9721 | nan | 0.5214 | 0.9917 | nan | 0.4376 | 0.9715 | |
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| 0.0228 | 115.0 | 230 | 0.1833 | 0.7009 | 0.7504 | 0.9720 | nan | 0.5088 | 0.9921 | nan | 0.4305 | 0.9714 | |
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| 0.0255 | 120.0 | 240 | 0.1841 | 0.7025 | 0.7532 | 0.9721 | nan | 0.5145 | 0.9919 | nan | 0.4336 | 0.9715 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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