--- 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: 0.0387 - Mean Iou: 0.4153 - Mean Accuracy: 0.8306 - Overall Accuracy: 0.8306 - Accuracy Unlabeled: nan - Accuracy Tool: nan - Accuracy Wear: 0.8306 - Iou Unlabeled: 0.0 - Iou Tool: nan - Iou Wear: 0.8306 ## 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 | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:| | 0.8874 | 1.18 | 20 | 0.8947 | 0.4560 | 0.9119 | 0.9119 | nan | nan | 0.9119 | 0.0 | nan | 0.9119 | | 0.5792 | 2.35 | 40 | 0.5701 | 0.2551 | 0.7653 | 0.7653 | nan | nan | 0.7653 | 0.0 | 0.0 | 0.7653 | | 0.5031 | 3.53 | 60 | 0.4364 | 0.4652 | 0.9305 | 0.9305 | nan | nan | 0.9305 | 0.0 | nan | 0.9305 | | 0.4025 | 4.71 | 80 | 0.4218 | 0.4529 | 0.9058 | 0.9058 | nan | nan | 0.9058 | 0.0 | nan | 0.9058 | | 0.3212 | 5.88 | 100 | 0.3115 | 0.4447 | 0.8894 | 0.8894 | nan | nan | 0.8894 | 0.0 | nan | 0.8894 | | 0.2797 | 7.06 | 120 | 0.2646 | 0.3291 | 0.6582 | 0.6582 | nan | nan | 0.6582 | 0.0 | nan | 0.6582 | | 0.2143 | 8.24 | 140 | 0.2223 | 0.4177 | 0.8354 | 0.8354 | nan | nan | 0.8354 | 0.0 | nan | 0.8354 | | 0.1951 | 9.41 | 160 | 0.1815 | 0.4313 | 0.8625 | 0.8625 | nan | nan | 0.8625 | 0.0 | nan | 0.8625 | | 0.1475 | 10.59 | 180 | 0.1571 | 0.4014 | 0.8029 | 0.8029 | nan | nan | 0.8029 | 0.0 | nan | 0.8029 | | 0.1523 | 11.76 | 200 | 0.1386 | 0.4242 | 0.8485 | 0.8485 | nan | nan | 0.8485 | 0.0 | nan | 0.8485 | | 0.1324 | 12.94 | 220 | 0.1127 | 0.4429 | 0.8858 | 0.8858 | nan | nan | 0.8858 | 0.0 | nan | 0.8858 | | 0.0977 | 14.12 | 240 | 0.1064 | 0.4458 | 0.8916 | 0.8916 | nan | nan | 0.8916 | 0.0 | nan | 0.8916 | | 0.0858 | 15.29 | 260 | 0.0915 | 0.4561 | 0.9122 | 0.9122 | nan | nan | 0.9122 | 0.0 | nan | 0.9122 | | 0.0782 | 16.47 | 280 | 0.0934 | 0.4611 | 0.9223 | 0.9223 | nan | nan | 0.9223 | 0.0 | nan | 0.9223 | | 0.0763 | 17.65 | 300 | 0.0757 | 0.4542 | 0.9084 | 0.9084 | nan | nan | 0.9084 | 0.0 | nan | 0.9084 | | 0.0665 | 18.82 | 320 | 0.0718 | 0.4259 | 0.8518 | 0.8518 | nan | nan | 0.8518 | 0.0 | nan | 0.8518 | | 0.0658 | 20.0 | 340 | 0.0636 | 0.3842 | 0.7685 | 0.7685 | nan | nan | 0.7685 | 0.0 | nan | 0.7685 | | 0.0672 | 21.18 | 360 | 0.0590 | 0.4212 | 0.8425 | 0.8425 | nan | nan | 0.8425 | 0.0 | nan | 0.8425 | | 0.05 | 22.35 | 380 | 0.0586 | 0.4502 | 0.9005 | 0.9005 | nan | nan | 0.9005 | 0.0 | nan | 0.9005 | | 0.0525 | 23.53 | 400 | 0.0546 | 0.3913 | 0.7827 | 0.7827 | nan | nan | 0.7827 | 0.0 | nan | 0.7827 | | 0.0451 | 24.71 | 420 | 0.0528 | 0.4383 | 0.8767 | 0.8767 | nan | nan | 0.8767 | 0.0 | nan | 0.8767 | | 0.0407 | 25.88 | 440 | 0.0494 | 0.4337 | 0.8675 | 0.8675 | nan | nan | 0.8675 | 0.0 | nan | 0.8675 | | 0.0462 | 27.06 | 460 | 0.0510 | 0.3397 | 0.6795 | 0.6795 | nan | nan | 0.6795 | 0.0 | nan | 0.6795 | | 0.0376 | 28.24 | 480 | 0.0451 | 0.4271 | 0.8541 | 0.8541 | nan | nan | 0.8541 | 0.0 | nan | 0.8541 | | 0.0349 | 29.41 | 500 | 0.0456 | 0.4173 | 0.8346 | 0.8346 | nan | nan | 0.8346 | 0.0 | nan | 0.8346 | | 0.0406 | 30.59 | 520 | 0.0449 | 0.3863 | 0.7726 | 0.7726 | nan | nan | 0.7726 | 0.0 | nan | 0.7726 | | 0.0333 | 31.76 | 540 | 0.0438 | 0.4361 | 0.8721 | 0.8721 | nan | nan | 0.8721 | 0.0 | nan | 0.8721 | | 0.0331 | 32.94 | 560 | 0.0480 | 0.3417 | 0.6834 | 0.6834 | nan | nan | 0.6834 | 0.0 | nan | 0.6834 | | 0.0756 | 34.12 | 580 | 0.0420 | 0.4362 | 0.8723 | 0.8723 | nan | nan | 0.8723 | 0.0 | nan | 0.8723 | | 0.0295 | 35.29 | 600 | 0.0437 | 0.3674 | 0.7349 | 0.7349 | nan | nan | 0.7349 | 0.0 | nan | 0.7349 | | 0.0325 | 36.47 | 620 | 0.0409 | 0.4087 | 0.8174 | 0.8174 | nan | nan | 0.8174 | 0.0 | nan | 0.8174 | | 0.0299 | 37.65 | 640 | 0.0405 | 0.4150 | 0.8299 | 0.8299 | nan | nan | 0.8299 | 0.0 | nan | 0.8299 | | 0.0384 | 38.82 | 660 | 0.0416 | 0.3690 | 0.7380 | 0.7380 | nan | nan | 0.7380 | 0.0 | nan | 0.7380 | | 0.0269 | 40.0 | 680 | 0.0393 | 0.4356 | 0.8713 | 0.8713 | nan | nan | 0.8713 | 0.0 | nan | 0.8713 | | 0.025 | 41.18 | 700 | 0.0389 | 0.3976 | 0.7952 | 0.7952 | nan | nan | 0.7952 | 0.0 | nan | 0.7952 | | 0.0256 | 42.35 | 720 | 0.0392 | 0.3729 | 0.7459 | 0.7459 | nan | nan | 0.7459 | 0.0 | nan | 0.7459 | | 0.0303 | 43.53 | 740 | 0.0400 | 0.3869 | 0.7738 | 0.7738 | nan | nan | 0.7738 | 0.0 | nan | 0.7738 | | 0.0244 | 44.71 | 760 | 0.0389 | 0.4022 | 0.8044 | 0.8044 | nan | nan | 0.8044 | 0.0 | nan | 0.8044 | | 0.03 | 45.88 | 780 | 0.0387 | 0.4003 | 0.8006 | 0.8006 | nan | nan | 0.8006 | 0.0 | nan | 0.8006 | | 0.0238 | 47.06 | 800 | 0.0384 | 0.4073 | 0.8147 | 0.8147 | nan | nan | 0.8147 | 0.0 | nan | 0.8147 | | 0.0278 | 48.24 | 820 | 0.0394 | 0.4151 | 0.8302 | 0.8302 | nan | nan | 0.8302 | 0.0 | nan | 0.8302 | | 0.0281 | 49.41 | 840 | 0.0387 | 0.4153 | 0.8306 | 0.8306 | nan | nan | 0.8306 | 0.0 | nan | 0.8306 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.13.3