--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: windowz_ln_segment_051525 results: [] --- # windowz_ln_segment_051525 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9734 - F1: 0.9770 - Iou: 0.9606 - Per Class Metrics: {0: {'f1': 0.99208, 'iou': 0.98429, 'accuracy': 0.98821}, 1: {'f1': 0.95742, 'iou': 0.91831, 'accuracy': 0.97945}, 2: {'f1': 0.27975, 'iou': 0.16263, 'accuracy': 0.97907}} - Loss: 0.5105 ## 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: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | | Class Metrics | Validation Loss | |:-------------:|:------:|:----:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:| | No log | 0.0503 | 86 | 0.1155 | {0: {'f1': 1e-05, 'iou': 0.0, 'accuracy': 0.25189}, 1: {'f1': 0.64518, 'iou': 0.47621, 'accuracy': 0.86451}, 2: {'f1': 0.00676, 'iou': 0.00339, 'accuracy': 0.13587}} | 1.0017 | | 1.2378 | 0.1006 | 172 | 0.0635 | {0: {'f1': 2e-05, 'iou': 1e-05, 'accuracy': 0.25181}, 1: {'f1': 0.41464, 'iou': 0.26154, 'accuracy': 0.81337}, 2: {'f1': 0.01135, 'iou': 0.00571, 'accuracy': 0.07762}} | 0.9440 | | 1.2207 | 0.1510 | 258 | 0.0554 | {0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.25189}, 1: {'f1': 0.37135, 'iou': 0.22801, 'accuracy': 0.80693}, 2: {'f1': 0.01383, 'iou': 0.00696, 'accuracy': 0.0683}} | 0.9237 | | 1.1925 | 0.2013 | 344 | 0.0499 | {0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.2519}, 1: {'f1': 0.34062, 'iou': 0.20527, 'accuracy': 0.80395}, 2: {'f1': 0.0169, 'iou': 0.00852, 'accuracy': 0.06157}} | 0.9326 | | 1.1606 | 0.2516 | 430 | 0.0647 | {0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.2519}, 1: {'f1': 0.42092, 'iou': 0.26656, 'accuracy': 0.81803}, 2: {'f1': 0.01824, 'iou': 0.0092, 'accuracy': 0.07944}} | 0.9245 | | 1.1299 | 0.3019 | 516 | 0.1179 | {0: {'f1': 0.00055, 'iou': 0.00027, 'accuracy': 0.2521}, 1: {'f1': 0.65335, 'iou': 0.48517, 'accuracy': 0.87296}, 2: {'f1': 0.01684, 'iou': 0.00849, 'accuracy': 0.12971}} | 0.8751 | | 1.0998 | 0.3523 | 602 | 0.1795 | {0: {'f1': 0.09312, 'iou': 0.04883, 'accuracy': 0.28836}, 1: {'f1': 0.74168, 'iou': 0.58942, 'accuracy': 0.89807}, 2: {'f1': 0.01625, 'iou': 0.00819, 'accuracy': 0.19265}} | 0.8243 | | 1.0998 | 0.4026 | 688 | 0.8495 | {0: {'f1': 0.98464, 'iou': 0.96975, 'accuracy': 0.97734}, 1: {'f1': 0.67506, 'iou': 0.5095, 'accuracy': 0.879}, 2: {'f1': 0.10072, 'iou': 0.05303, 'accuracy': 0.86349}} | 0.8043 | | 1.0696 | 0.4529 | 774 | 0.874 | {0: {'f1': 0.98422, 'iou': 0.96893, 'accuracy': 0.97671}, 1: {'f1': 0.75979, 'iou': 0.61262, 'accuracy': 0.90403}, 2: {'f1': 0.11786, 'iou': 0.06262, 'accuracy': 0.88981}} | 0.7506 | | 1.0419 | 0.5032 | 860 | 0.9391 | {0: {'f1': 0.98895, 'iou': 0.97813, 'accuracy': 0.98362}, 1: {'f1': 0.9206, 'iou': 0.85288, 'accuracy': 0.96291}, 2: {'f1': 0.10984, 'iou': 0.05811, 'accuracy': 0.9543}} | 0.6618 | | 1.0149 | 0.5535 | 946 | 0.7046 | {0: {'f1': 0.91333, 'iou': 0.84049, 'accuracy': 0.85914}, 1: {'f1': 0.47479, 'iou': 0.3113, 'accuracy': 0.83125}, 2: {'f1': 0.06969, 'iou': 0.0361, 'accuracy': 0.95034}} | 0.7884 | | 1.0024 | 0.6039 | 1032 | 0.8763 | {0: {'f1': 0.97412, 'iou': 0.94954, 'accuracy': 0.96061}, 1: {'f1': 0.81027, 'iou': 0.68105, 'accuracy': 0.92142}, 2: {'f1': 0.15071, 'iou': 0.0815, 'accuracy': 0.94565}} | 0.5940 | | 0.9739 | 0.6542 | 1118 | 0.2909 | {0: {'f1': 0.24044, 'iou': 0.13665, 'accuracy': 0.34611}, 1: {'f1': 0.87511, 'iou': 0.77796, 'accuracy': 0.94508}, 2: {'f1': 0.01577, 'iou': 0.00795, 'accuracy': 0.31169}} | 0.9626 | | 0.9626 | 0.7045 | 1204 | 0.9520 | {0: {'f1': 0.99067, 'iou': 0.98152, 'accuracy': 0.98615}, 1: {'f1': 0.94406, 'iou': 0.89405, 'accuracy': 0.97333}, 2: {'f1': 0.17721, 'iou': 0.09722, 'accuracy': 0.96864}} | 0.5658 | | 0.9626 | 0.7548 | 1290 | 0.9183 | {0: {'f1': 0.98633, 'iou': 0.97303, 'accuracy': 0.97941}, 1: {'f1': 0.87697, 'iou': 0.78089, 'accuracy': 0.94587}, 2: {'f1': 0.18891, 'iou': 0.1043, 'accuracy': 0.95606}} | 0.6192 | | 0.9515 | 0.8051 | 1376 | 0.3322 | {0: {'f1': 0.25117, 'iou': 0.14362, 'accuracy': 0.35705}, 1: {'f1': 0.96192, 'iou': 0.92663, 'accuracy': 0.98155}, 2: {'f1': 0.00532, 'iou': 0.00267, 'accuracy': 0.34648}} | 0.9133 | | 0.9395 | 0.8555 | 1462 | 0.9488 | {0: {'f1': 0.99219, 'iou': 0.98451, 'accuracy': 0.98835}, 1: {'f1': 0.93039, 'iou': 0.86984, 'accuracy': 0.96766}, 2: {'f1': 0.25682, 'iou': 0.14733, 'accuracy': 0.96767}} | 0.5446 | | 0.9288 | 0.9058 | 1548 | 0.9606 | {0: {'f1': 0.99208, 'iou': 0.98429, 'accuracy': 0.98821}, 1: {'f1': 0.95742, 'iou': 0.91831, 'accuracy': 0.97945}, 2: {'f1': 0.27975, 'iou': 0.16263, 'accuracy': 0.97907}} | 0.5105 | | 0.9341 | 0.9561 | 1634 | 0.9536 | {0: {'f1': 0.98841, 'iou': 0.97708, 'accuracy': 0.98281}, 1: {'f1': 0.95511, 'iou': 0.91408, 'accuracy': 0.97815}, 2: {'f1': 0.18082, 'iou': 0.0994, 'accuracy': 0.97498}} | 0.5982 | ### Framework versions - Transformers 4.45.0 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.20.3