--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-test results: [] --- # segformer-b0-finetuned-segments-sidewalk-test This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.1893 - Mean Iou: 0.4552 - Mean Accuracy: 0.9104 - Overall Accuracy: 0.9104 - Accuracy Other: nan - Accuracy Flat-sidewalk: 0.9104 - Iou Other: 0.0 - Iou Flat-sidewalk: 0.9104 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Other | Accuracy Flat-sidewalk | Iou Other | Iou Flat-sidewalk | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:----------------------:|:---------:|:-----------------:| | 0.4805 | 0.05 | 20 | 0.5080 | 0.4534 | 0.9069 | 0.9069 | nan | 0.9069 | 0.0 | 0.9069 | | 0.2146 | 0.1 | 40 | 0.3937 | 0.4660 | 0.9319 | 0.9319 | nan | 0.9319 | 0.0 | 0.9319 | | 0.215 | 0.15 | 60 | 0.3593 | 0.4476 | 0.8952 | 0.8952 | nan | 0.8952 | 0.0 | 0.8952 | | 0.151 | 0.2 | 80 | 0.2834 | 0.4423 | 0.8845 | 0.8845 | nan | 0.8845 | 0.0 | 0.8845 | | 0.174 | 0.25 | 100 | 0.3268 | 0.4612 | 0.9225 | 0.9225 | nan | 0.9225 | 0.0 | 0.9225 | | 0.1597 | 0.3 | 120 | 0.2900 | 0.4229 | 0.8457 | 0.8457 | nan | 0.8457 | 0.0 | 0.8457 | | 0.2165 | 0.35 | 140 | 0.2723 | 0.4411 | 0.8822 | 0.8822 | nan | 0.8822 | 0.0 | 0.8822 | | 0.2 | 0.4 | 160 | 0.2598 | 0.4167 | 0.8334 | 0.8334 | nan | 0.8334 | 0.0 | 0.8334 | | 0.577 | 0.45 | 180 | 0.3185 | 0.4708 | 0.9416 | 0.9416 | nan | 0.9416 | 0.0 | 0.9416 | | 0.2466 | 0.5 | 200 | 0.2305 | 0.4295 | 0.8589 | 0.8589 | nan | 0.8589 | 0.0 | 0.8589 | | 0.1742 | 0.55 | 220 | 0.2439 | 0.4544 | 0.9089 | 0.9089 | nan | 0.9089 | 0.0 | 0.9089 | | 0.1764 | 0.6 | 240 | 0.2318 | 0.4359 | 0.8719 | 0.8719 | nan | 0.8719 | 0.0 | 0.8719 | | 0.1432 | 0.65 | 260 | 0.2253 | 0.4318 | 0.8636 | 0.8636 | nan | 0.8636 | 0.0 | 0.8636 | | 0.1472 | 0.7 | 280 | 0.2193 | 0.4353 | 0.8707 | 0.8707 | nan | 0.8707 | 0.0 | 0.8707 | | 0.4737 | 0.75 | 300 | 0.2347 | 0.4407 | 0.8813 | 0.8813 | nan | 0.8813 | 0.0 | 0.8813 | | 0.1567 | 0.8 | 320 | 0.2212 | 0.4248 | 0.8496 | 0.8496 | nan | 0.8496 | 0.0 | 0.8496 | | 0.0832 | 0.85 | 340 | 0.2170 | 0.4426 | 0.8852 | 0.8852 | nan | 0.8852 | 0.0 | 0.8852 | | 0.1718 | 0.9 | 360 | 0.2079 | 0.4390 | 0.8780 | 0.8780 | nan | 0.8780 | 0.0 | 0.8780 | | 0.3256 | 0.95 | 380 | 0.2127 | 0.4576 | 0.9151 | 0.9151 | nan | 0.9151 | 0.0 | 0.9151 | | 0.089 | 1.0 | 400 | 0.2249 | 0.4603 | 0.9207 | 0.9207 | nan | 0.9207 | 0.0 | 0.9207 | | 0.103 | 1.05 | 420 | 0.2051 | 0.4360 | 0.8720 | 0.8720 | nan | 0.8720 | 0.0 | 0.8720 | | 0.3474 | 1.1 | 440 | 0.2216 | 0.4333 | 0.8666 | 0.8666 | nan | 0.8666 | 0.0 | 0.8666 | | 0.0851 | 1.15 | 460 | 0.2306 | 0.4681 | 0.9361 | 0.9361 | nan | 0.9361 | 0.0 | 0.9361 | | 0.1989 | 1.2 | 480 | 0.2029 | 0.4516 | 0.9032 | 0.9032 | nan | 0.9032 | 0.0 | 0.9032 | | 0.2072 | 1.25 | 500 | 0.2076 | 0.4666 | 0.9331 | 0.9331 | nan | 0.9331 | 0.0 | 0.9331 | | 0.2898 | 1.3 | 520 | 0.2164 | 0.4645 | 0.9291 | 0.9291 | nan | 0.9291 | 0.0 | 0.9291 | | 0.1578 | 1.35 | 540 | 0.2057 | 0.4457 | 0.8914 | 0.8914 | nan | 0.8914 | 0.0 | 0.8914 | | 0.2697 | 1.4 | 560 | 0.1973 | 0.4646 | 0.9292 | 0.9292 | nan | 0.9292 | 0.0 | 0.9292 | | 0.1269 | 1.45 | 580 | 0.1830 | 0.4467 | 0.8934 | 0.8934 | nan | 0.8934 | 0.0 | 0.8934 | | 0.0908 | 1.5 | 600 | 0.1866 | 0.4471 | 0.8941 | 0.8941 | nan | 0.8941 | 0.0 | 0.8941 | | 0.0614 | 1.55 | 620 | 0.1983 | 0.4632 | 0.9264 | 0.9264 | nan | 0.9264 | 0.0 | 0.9264 | | 0.1043 | 1.6 | 640 | 0.1941 | 0.4598 | 0.9196 | 0.9196 | nan | 0.9196 | 0.0 | 0.9196 | | 0.0532 | 1.65 | 660 | 0.1920 | 0.4553 | 0.9106 | 0.9106 | nan | 0.9106 | 0.0 | 0.9106 | | 0.5912 | 1.7 | 680 | 0.1880 | 0.4530 | 0.9059 | 0.9059 | nan | 0.9059 | 0.0 | 0.9059 | | 0.0604 | 1.75 | 700 | 0.1964 | 0.4611 | 0.9221 | 0.9221 | nan | 0.9221 | 0.0 | 0.9221 | | 0.0899 | 1.8 | 720 | 0.1975 | 0.4623 | 0.9245 | 0.9245 | nan | 0.9245 | 0.0 | 0.9245 | | 0.1153 | 1.85 | 740 | 0.1866 | 0.4580 | 0.9160 | 0.9160 | nan | 0.9160 | 0.0 | 0.9160 | | 0.1038 | 1.9 | 760 | 0.1998 | 0.4652 | 0.9304 | 0.9304 | nan | 0.9304 | 0.0 | 0.9304 | | 0.1448 | 1.95 | 780 | 0.1977 | 0.4624 | 0.9248 | 0.9248 | nan | 0.9248 | 0.0 | 0.9248 | | 0.1298 | 2.0 | 800 | 0.1893 | 0.4552 | 0.9104 | 0.9104 | nan | 0.9104 | 0.0 | 0.9104 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1