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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_4 |
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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_4 |
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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: 0.2242 |
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- Mean Iou: 0.4568 |
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- Mean Accuracy: 0.7402 |
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- Overall Accuracy: 0.9696 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.4899 |
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- Accuracy Undropoff: 0.9904 |
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- Iou Unlabeled: 0.0 |
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- Iou Dropoff: 0.4016 |
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- Iou Undropoff: 0.9690 |
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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: 7e-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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| 0.9465 | 5.0 | 10 | 0.9974 | 0.2695 | 0.5001 | 0.6771 | nan | 0.3071 | 0.6931 | 0.0 | 0.1261 | 0.6824 | |
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| 0.8558 | 10.0 | 20 | 0.8237 | 0.3822 | 0.7119 | 0.8664 | nan | 0.5434 | 0.8804 | 0.0 | 0.2787 | 0.8678 | |
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| 0.7585 | 15.0 | 30 | 0.6801 | 0.4232 | 0.7487 | 0.9194 | nan | 0.5625 | 0.9349 | 0.0 | 0.3494 | 0.9202 | |
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| 0.715 | 20.0 | 40 | 0.6076 | 0.4298 | 0.7663 | 0.9232 | nan | 0.5952 | 0.9375 | 0.0 | 0.3661 | 0.9233 | |
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| 0.6145 | 25.0 | 50 | 0.5298 | 0.4398 | 0.7760 | 0.9380 | nan | 0.5994 | 0.9527 | 0.0 | 0.3819 | 0.9375 | |
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| 0.5355 | 30.0 | 60 | 0.4821 | 0.4426 | 0.7749 | 0.9428 | nan | 0.5918 | 0.9581 | 0.0 | 0.3857 | 0.9422 | |
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| 0.4619 | 35.0 | 70 | 0.4266 | 0.4493 | 0.7716 | 0.9524 | nan | 0.5743 | 0.9688 | 0.0 | 0.3962 | 0.9517 | |
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| 0.4367 | 40.0 | 80 | 0.3941 | 0.4519 | 0.7738 | 0.9568 | nan | 0.5742 | 0.9734 | 0.0 | 0.3997 | 0.9559 | |
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| 0.3839 | 45.0 | 90 | 0.3801 | 0.4528 | 0.7796 | 0.9577 | nan | 0.5853 | 0.9738 | 0.0 | 0.4017 | 0.9567 | |
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| 0.3164 | 50.0 | 100 | 0.3549 | 0.4543 | 0.7785 | 0.9608 | nan | 0.5797 | 0.9773 | 0.0 | 0.4030 | 0.9599 | |
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| 0.3018 | 55.0 | 110 | 0.3327 | 0.4573 | 0.7731 | 0.9639 | nan | 0.5650 | 0.9812 | 0.0 | 0.4087 | 0.9631 | |
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| 0.2646 | 60.0 | 120 | 0.3127 | 0.4590 | 0.7703 | 0.9658 | nan | 0.5571 | 0.9835 | 0.0 | 0.4121 | 0.9650 | |
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| 0.2378 | 65.0 | 130 | 0.2958 | 0.4628 | 0.7728 | 0.9673 | nan | 0.5607 | 0.9850 | 0.0 | 0.4217 | 0.9666 | |
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| 0.2076 | 70.0 | 140 | 0.2778 | 0.4675 | 0.7729 | 0.9693 | nan | 0.5586 | 0.9871 | 0.0 | 0.4340 | 0.9686 | |
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| 0.1951 | 75.0 | 150 | 0.2648 | 0.4666 | 0.7719 | 0.9692 | nan | 0.5567 | 0.9871 | 0.0 | 0.4314 | 0.9685 | |
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| 0.1734 | 80.0 | 160 | 0.2522 | 0.4673 | 0.7643 | 0.9703 | nan | 0.5397 | 0.9890 | 0.0 | 0.4322 | 0.9696 | |
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| 0.1569 | 85.0 | 170 | 0.2436 | 0.4660 | 0.7603 | 0.9703 | nan | 0.5312 | 0.9894 | 0.0 | 0.4282 | 0.9697 | |
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| 0.1691 | 90.0 | 180 | 0.2411 | 0.4647 | 0.7624 | 0.9697 | nan | 0.5363 | 0.9885 | 0.0 | 0.4250 | 0.9690 | |
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| 0.1498 | 95.0 | 190 | 0.2335 | 0.4623 | 0.7537 | 0.9699 | nan | 0.5179 | 0.9895 | 0.0 | 0.4176 | 0.9692 | |
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| 0.1478 | 100.0 | 200 | 0.2281 | 0.4585 | 0.7420 | 0.9700 | nan | 0.4934 | 0.9906 | 0.0 | 0.4062 | 0.9693 | |
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| 0.1407 | 105.0 | 210 | 0.2278 | 0.4615 | 0.7501 | 0.9701 | nan | 0.5102 | 0.9900 | 0.0 | 0.4151 | 0.9694 | |
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| 0.1397 | 110.0 | 220 | 0.2305 | 0.4610 | 0.7512 | 0.9698 | nan | 0.5129 | 0.9896 | 0.0 | 0.4140 | 0.9691 | |
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| 0.1317 | 115.0 | 230 | 0.2265 | 0.4576 | 0.7430 | 0.9695 | nan | 0.4959 | 0.9901 | 0.0 | 0.4038 | 0.9689 | |
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| 0.1548 | 120.0 | 240 | 0.2242 | 0.4568 | 0.7402 | 0.9696 | nan | 0.4899 | 0.9904 | 0.0 | 0.4016 | 0.9690 | |
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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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