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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_5 |
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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_5 |
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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: 0.1911 |
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- Mean Iou: 0.4677 |
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- Mean Accuracy: 0.7472 |
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- Overall Accuracy: 0.9719 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.5020 |
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- Accuracy Undropoff: 0.9923 |
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- Iou Unlabeled: 0.0 |
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- Iou Dropoff: 0.4318 |
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- Iou Undropoff: 0.9713 |
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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: 9e-06 |
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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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| 1.0685 | 5.0 | 10 | 1.0222 | 0.2189 | 0.3725 | 0.5989 | nan | 0.1256 | 0.6194 | 0.0 | 0.0497 | 0.6070 | |
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| 0.9481 | 10.0 | 20 | 0.8419 | 0.3703 | 0.6398 | 0.8451 | nan | 0.4159 | 0.8637 | 0.0 | 0.2633 | 0.8476 | |
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| 0.8268 | 15.0 | 30 | 0.7165 | 0.3949 | 0.6938 | 0.8694 | nan | 0.5023 | 0.8853 | 0.0 | 0.3136 | 0.8711 | |
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| 0.7573 | 20.0 | 40 | 0.6206 | 0.4084 | 0.7186 | 0.8994 | nan | 0.5214 | 0.9158 | 0.0 | 0.3243 | 0.9010 | |
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| 0.636 | 25.0 | 50 | 0.5194 | 0.4239 | 0.7253 | 0.9300 | nan | 0.5020 | 0.9485 | 0.0 | 0.3401 | 0.9316 | |
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| 0.5238 | 30.0 | 60 | 0.4507 | 0.4365 | 0.7368 | 0.9461 | nan | 0.5085 | 0.9651 | 0.0 | 0.3618 | 0.9476 | |
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| 0.4296 | 35.0 | 70 | 0.4064 | 0.4410 | 0.7422 | 0.9530 | nan | 0.5123 | 0.9721 | 0.0 | 0.3683 | 0.9546 | |
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| 0.4105 | 40.0 | 80 | 0.3547 | 0.4502 | 0.7467 | 0.9619 | nan | 0.5120 | 0.9814 | 0.0 | 0.3880 | 0.9627 | |
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| 0.3436 | 45.0 | 90 | 0.3304 | 0.4571 | 0.7596 | 0.9644 | nan | 0.5361 | 0.9830 | 0.0 | 0.4066 | 0.9647 | |
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| 0.2729 | 50.0 | 100 | 0.2953 | 0.4614 | 0.7552 | 0.9680 | nan | 0.5232 | 0.9873 | 0.0 | 0.4163 | 0.9678 | |
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| 0.2546 | 55.0 | 110 | 0.2770 | 0.4629 | 0.7579 | 0.9691 | nan | 0.5276 | 0.9882 | 0.0 | 0.4201 | 0.9686 | |
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| 0.2281 | 60.0 | 120 | 0.2591 | 0.4647 | 0.7566 | 0.9702 | nan | 0.5235 | 0.9896 | 0.0 | 0.4245 | 0.9696 | |
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| 0.2041 | 65.0 | 130 | 0.2453 | 0.4657 | 0.7556 | 0.9708 | nan | 0.5209 | 0.9903 | 0.0 | 0.4269 | 0.9701 | |
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| 0.1772 | 70.0 | 140 | 0.2292 | 0.4676 | 0.7542 | 0.9717 | nan | 0.5171 | 0.9914 | 0.0 | 0.4317 | 0.9711 | |
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| 0.169 | 75.0 | 150 | 0.2161 | 0.4681 | 0.7520 | 0.9719 | nan | 0.5122 | 0.9919 | 0.0 | 0.4331 | 0.9713 | |
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| 0.1543 | 80.0 | 160 | 0.2111 | 0.4682 | 0.7530 | 0.9715 | nan | 0.5147 | 0.9913 | 0.0 | 0.4336 | 0.9709 | |
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| 0.1374 | 85.0 | 170 | 0.1973 | 0.4659 | 0.7450 | 0.9715 | nan | 0.4980 | 0.9921 | 0.0 | 0.4268 | 0.9709 | |
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| 0.1523 | 90.0 | 180 | 0.1974 | 0.4681 | 0.7501 | 0.9717 | nan | 0.5085 | 0.9918 | 0.0 | 0.4332 | 0.9711 | |
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| 0.1323 | 95.0 | 190 | 0.1928 | 0.4658 | 0.7434 | 0.9717 | nan | 0.4944 | 0.9924 | 0.0 | 0.4263 | 0.9711 | |
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| 0.1254 | 100.0 | 200 | 0.1923 | 0.4671 | 0.7467 | 0.9717 | nan | 0.5013 | 0.9921 | 0.0 | 0.4301 | 0.9711 | |
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| 0.125 | 105.0 | 210 | 0.1867 | 0.4637 | 0.7380 | 0.9717 | nan | 0.4831 | 0.9929 | 0.0 | 0.4201 | 0.9711 | |
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| 0.1239 | 110.0 | 220 | 0.1912 | 0.4694 | 0.7520 | 0.9719 | nan | 0.5121 | 0.9919 | 0.0 | 0.4369 | 0.9713 | |
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| 0.1252 | 115.0 | 230 | 0.1913 | 0.4689 | 0.7503 | 0.9720 | nan | 0.5085 | 0.9921 | 0.0 | 0.4354 | 0.9714 | |
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| 0.1357 | 120.0 | 240 | 0.1911 | 0.4677 | 0.7472 | 0.9719 | nan | 0.5020 | 0.9923 | 0.0 | 0.4318 | 0.9713 | |
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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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