--- license: other tags: - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-toolwear results: [] --- # segformer-b0-finetuned-segments-toolwear This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0480 - Mean Iou: 0.4892 - Mean Accuracy: 0.9785 - Overall Accuracy: 0.9785 - Accuracy Unlabeled: nan - Accuracy Tool: 0.9785 - Iou Unlabeled: 0.0 - Iou Tool: 0.9785 ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Iou Unlabeled | Iou Tool | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| | 0.257 | 1.82 | 20 | 0.4274 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.14 | 3.64 | 40 | 0.1857 | 0.4878 | 0.9756 | 0.9756 | nan | 0.9756 | 0.0 | 0.9756 | | 0.0945 | 5.45 | 60 | 0.1383 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 | | 0.0766 | 7.27 | 80 | 0.1041 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0752 | 9.09 | 100 | 0.1055 | 0.4836 | 0.9672 | 0.9672 | nan | 0.9672 | 0.0 | 0.9672 | | 0.0433 | 10.91 | 120 | 0.1180 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 | | 0.0358 | 12.73 | 140 | 0.0857 | 0.4831 | 0.9662 | 0.9662 | nan | 0.9662 | 0.0 | 0.9662 | | 0.0357 | 14.55 | 160 | 0.0765 | 0.4865 | 0.9730 | 0.9730 | nan | 0.9730 | 0.0 | 0.9730 | | 0.0401 | 16.36 | 180 | 0.0898 | 0.4793 | 0.9587 | 0.9587 | nan | 0.9587 | 0.0 | 0.9587 | | 0.042 | 18.18 | 200 | 0.0755 | 0.4828 | 0.9655 | 0.9655 | nan | 0.9655 | 0.0 | 0.9655 | | 0.0366 | 20.0 | 220 | 0.0744 | 0.4818 | 0.9635 | 0.9635 | nan | 0.9635 | 0.0 | 0.9635 | | 0.0213 | 21.82 | 240 | 0.0708 | 0.4828 | 0.9656 | 0.9656 | nan | 0.9656 | 0.0 | 0.9656 | | 0.0284 | 23.64 | 260 | 0.0684 | 0.4851 | 0.9701 | 0.9701 | nan | 0.9701 | 0.0 | 0.9701 | | 0.0237 | 25.45 | 280 | 0.0625 | 0.4879 | 0.9757 | 0.9757 | nan | 0.9757 | 0.0 | 0.9757 | | 0.0189 | 27.27 | 300 | 0.0603 | 0.4858 | 0.9716 | 0.9716 | nan | 0.9716 | 0.0 | 0.9716 | | 0.026 | 29.09 | 320 | 0.0632 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 | | 0.0231 | 30.91 | 340 | 0.0662 | 0.4840 | 0.9680 | 0.9680 | nan | 0.9680 | 0.0 | 0.9680 | | 0.0218 | 32.73 | 360 | 0.0563 | 0.4855 | 0.9710 | 0.9710 | nan | 0.9710 | 0.0 | 0.9710 | | 0.0253 | 34.55 | 380 | 0.0627 | 0.4848 | 0.9697 | 0.9697 | nan | 0.9697 | 0.0 | 0.9697 | | 0.0142 | 36.36 | 400 | 0.0621 | 0.4844 | 0.9689 | 0.9689 | nan | 0.9689 | 0.0 | 0.9689 | | 0.0214 | 38.18 | 420 | 0.0668 | 0.4820 | 0.9639 | 0.9639 | nan | 0.9639 | 0.0 | 0.9639 | | 0.0166 | 40.0 | 440 | 0.0555 | 0.4858 | 0.9716 | 0.9716 | nan | 0.9716 | 0.0 | 0.9716 | | 0.0185 | 41.82 | 460 | 0.0545 | 0.4859 | 0.9718 | 0.9718 | nan | 0.9718 | 0.0 | 0.9718 | | 0.0218 | 43.64 | 480 | 0.0500 | 0.4876 | 0.9753 | 0.9753 | nan | 0.9753 | 0.0 | 0.9753 | | 0.0184 | 45.45 | 500 | 0.0481 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.018 | 47.27 | 520 | 0.0487 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0254 | 49.09 | 540 | 0.0480 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3