--- license: other tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-lane-10k-steps results: [] --- # segformer-finetuned-lane-10k-steps This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset. It achieves the following results on the evaluation set: - Loss: 0.0365 - Mean Iou: 0.4899 - Mean Accuracy: 0.7371 - Overall Accuracy: 0.7371 - Accuracy Background: nan - Accuracy Left: 0.7394 - Accuracy Right: 0.7348 - Iou Background: 0.0 - Iou Left: 0.7371 - Iou Right: 0.7325 ## 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: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:| | 0.0792 | 1.0 | 308 | 0.0714 | 0.0148 | 0.0229 | 0.0225 | nan | 0.0373 | 0.0085 | 0.0 | 0.0362 | 0.0083 | | 0.0437 | 2.0 | 616 | 0.0502 | 0.1687 | 0.2775 | 0.2784 | nan | 0.2492 | 0.3058 | 0.0 | 0.2343 | 0.2718 | | 0.0326 | 3.0 | 924 | 0.0445 | 0.2614 | 0.4441 | 0.4479 | nan | 0.3134 | 0.5748 | 0.0 | 0.3100 | 0.4742 | | 0.0224 | 4.0 | 1232 | 0.0370 | 0.4048 | 0.6098 | 0.6100 | nan | 0.6043 | 0.6153 | 0.0 | 0.6031 | 0.6113 | | 0.0184 | 5.0 | 1540 | 0.0346 | 0.3820 | 0.5858 | 0.5870 | nan | 0.5421 | 0.6295 | 0.0 | 0.5400 | 0.6060 | | 0.0159 | 6.0 | 1848 | 0.0319 | 0.4367 | 0.6567 | 0.6573 | nan | 0.6343 | 0.6791 | 0.0 | 0.6341 | 0.6760 | | 0.0139 | 7.0 | 2156 | 0.0317 | 0.4555 | 0.6855 | 0.6860 | nan | 0.6691 | 0.7019 | 0.0 | 0.6680 | 0.6986 | | 0.0129 | 8.0 | 2464 | 0.0321 | 0.4348 | 0.6533 | 0.6535 | nan | 0.6479 | 0.6588 | 0.0 | 0.6474 | 0.6571 | | 0.0122 | 9.0 | 2772 | 0.0275 | 0.4541 | 0.6827 | 0.6830 | nan | 0.6710 | 0.6943 | 0.0 | 0.6697 | 0.6927 | | 0.0111 | 10.0 | 3080 | 0.0305 | 0.4609 | 0.6928 | 0.6927 | nan | 0.6969 | 0.6887 | 0.0 | 0.6963 | 0.6865 | | 0.011 | 11.0 | 3388 | 0.0286 | 0.4646 | 0.6988 | 0.6991 | nan | 0.6890 | 0.7087 | 0.0 | 0.6883 | 0.7055 | | 0.0103 | 12.0 | 3696 | 0.0298 | 0.4693 | 0.7058 | 0.7062 | nan | 0.6939 | 0.7177 | 0.0 | 0.6932 | 0.7148 | | 0.0097 | 13.0 | 4004 | 0.0293 | 0.4717 | 0.7090 | 0.7087 | nan | 0.7184 | 0.6996 | 0.0 | 0.7176 | 0.6975 | | 0.0093 | 14.0 | 4312 | 0.0330 | 0.4537 | 0.6835 | 0.6836 | nan | 0.6775 | 0.6894 | 0.0 | 0.6768 | 0.6843 | | 0.009 | 15.0 | 4620 | 0.0331 | 0.4804 | 0.7226 | 0.7226 | nan | 0.7194 | 0.7257 | 0.0 | 0.7178 | 0.7234 | | 0.0088 | 16.0 | 4928 | 0.0315 | 0.4890 | 0.7355 | 0.7357 | nan | 0.7275 | 0.7435 | 0.0 | 0.7259 | 0.7411 | | 0.0086 | 17.0 | 5236 | 0.0338 | 0.4813 | 0.7234 | 0.7234 | nan | 0.7224 | 0.7243 | 0.0 | 0.7216 | 0.7223 | | 0.0085 | 18.0 | 5544 | 0.0348 | 0.4743 | 0.7129 | 0.7126 | nan | 0.7225 | 0.7033 | 0.0 | 0.7217 | 0.7012 | | 0.0083 | 19.0 | 5852 | 0.0357 | 0.4812 | 0.7245 | 0.7244 | nan | 0.7281 | 0.7210 | 0.0 | 0.7254 | 0.7183 | | 0.0081 | 20.0 | 6160 | 0.0334 | 0.4829 | 0.7271 | 0.7269 | nan | 0.7337 | 0.7205 | 0.0 | 0.7305 | 0.7182 | | 0.0079 | 21.0 | 6468 | 0.0359 | 0.4773 | 0.7177 | 0.7177 | nan | 0.7184 | 0.7170 | 0.0 | 0.7174 | 0.7146 | | 0.0077 | 22.0 | 6776 | 0.0351 | 0.4874 | 0.7332 | 0.7329 | nan | 0.7440 | 0.7223 | 0.0 | 0.7432 | 0.7190 | | 0.0075 | 23.0 | 7084 | 0.0344 | 0.4855 | 0.7296 | 0.7292 | nan | 0.7437 | 0.7156 | 0.0 | 0.7425 | 0.7141 | | 0.0077 | 24.0 | 7392 | 0.0362 | 0.4799 | 0.7216 | 0.7216 | nan | 0.7236 | 0.7196 | 0.0 | 0.7223 | 0.7174 | | 0.0071 | 25.0 | 7700 | 0.0391 | 0.4775 | 0.7179 | 0.7180 | nan | 0.7173 | 0.7186 | 0.0 | 0.7161 | 0.7163 | | 0.0077 | 26.0 | 8008 | 0.0339 | 0.4895 | 0.7367 | 0.7366 | nan | 0.7405 | 0.7329 | 0.0 | 0.7388 | 0.7297 | | 0.0069 | 27.0 | 8316 | 0.0344 | 0.4858 | 0.7305 | 0.7305 | nan | 0.7291 | 0.7318 | 0.0 | 0.7278 | 0.7297 | | 0.0069 | 28.0 | 8624 | 0.0361 | 0.4844 | 0.7283 | 0.7282 | nan | 0.7324 | 0.7243 | 0.0 | 0.7309 | 0.7221 | | 0.007 | 29.0 | 8932 | 0.0371 | 0.4837 | 0.7273 | 0.7270 | nan | 0.7360 | 0.7186 | 0.0 | 0.7345 | 0.7166 | | 0.007 | 30.0 | 9240 | 0.0366 | 0.4854 | 0.7305 | 0.7303 | nan | 0.7379 | 0.7231 | 0.0 | 0.7353 | 0.7208 | | 0.0067 | 31.0 | 9548 | 0.0367 | 0.4866 | 0.7322 | 0.7321 | nan | 0.7357 | 0.7286 | 0.0 | 0.7335 | 0.7263 | | 0.0068 | 32.0 | 9856 | 0.0364 | 0.4883 | 0.7348 | 0.7347 | nan | 0.7377 | 0.7318 | 0.0 | 0.7355 | 0.7295 | | 0.0067 | 32.47 | 10000 | 0.0365 | 0.4899 | 0.7371 | 0.7371 | nan | 0.7394 | 0.7348 | 0.0 | 0.7371 | 0.7325 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3