--- license: other base_model: nvidia/mit-b0 tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 results: [] --- # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.7908 - Mean Iou: 0.0659 - Mean Accuracy: 0.1340 - Overall Accuracy: 0.4553 - Per Category Iou: [0.4253649476473911, 0.5034109758987441, 0.5339807986465558, 0.21852700568728256, 0.5596012629463966, 0.45339501867608994, 0.3555890315391098, 0.0, 0.0, 0.2003530180601108, 0.0, 0.0019000258028195444, 0.11208022997517314, 0.0, 0.0, 0.0, 0.0, 0.0021475368733171268, 0.0, 0.0, 0.05899851264253842, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] - Per Category Accuracy: [0.8954293945491129, 0.7012820064702295, 0.976272566062967, 0.5525036023054755, 0.9601103940740133, 0.7735951342060029, 0.5235119548076005, 0.0, 0.0, 0.21074542102186217, 0.0, 0.001972194492464269, 0.5725967957276369, 0.0, 0.0, 0.0, 0.0, 0.0534890035297312, 0.0, 0.0, 0.34643377001455605, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] ## 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 | Per Category Iou | Per Category Accuracy | 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| 4.9817 | 1.0 | 20 | 4.9399 | 0.0053 | 0.0404 | 0.1008 | [0.0004356639554686628, 0.047571727054986496, 0.3591917034783372, 0.005700749531916421, 0.016380529186023025, 0.0, 0.07715175932638153, 0.013142550395202044, 0.005405405405405406, 0.11557999720370975, 0.0, 0.02197069104301514, 0.0, 0.0, 0.0, 0.0062961735638681505, 0.0, 0.0, 0.0, 0.00010213044099924423, 0.0, 0.0, 0.0022029666617711854, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0002070087239390803, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.028720928886470716, 0.0, 0.0, nan, 0.0, 0.011201513101041888, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0] | [0.00046109993293091885, 0.050755960495092955, 0.8566469955521963, 0.005721661863592699, 0.016641188480439936, 0.0, 0.10060580720515186, 0.018532778220070215, 0.006443200122727621, 0.14339220208535433, 0.0, 0.09702709941321126, 0.0, 0.0, 0.0, 0.007457345369750421, 0.0, 0.0, 0.0, 0.00011914832775321998, 0.0, nan, 0.0028254440656256476, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.006944444444444444, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.055916159567275185, nan, nan, nan, nan, 0.6112099644128114, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] | | 4.3931 | 2.0 | 40 | 4.7458 | 0.0113 | 0.0538 | 0.1648 | [0.0049349498902609935, 0.1387791292603275, 0.3392691507580562, 0.0853866986834327, 0.21992530838583121, 5.030560655985109e-05, 0.10328288618818314, 0.016953008211105576, 0.03221757322175732, 0.23892646634808545, 0.0, 0.06674982004802382, 0.00399002493765586, 0.0, 0.0, 0.0027480720889608455, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00023090498259966023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00019452414530953655, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00031117104034851154, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 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nan, nan, nan, nan, nan, nan, nan, nan, 0.2798165137614679, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] | | 3.8851 | 3.0 | 60 | 4.4082 | 0.0266 | 0.0784 | 0.2750 | [0.07161186986549094, 0.2328940506023177, 0.3639417983698355, 0.12895892226477282, 0.47633278016906083, 0.004857550406723467, 0.21220102841507713, 0.005734282907662083, 0.19340758181000503, 0.25618352177768333, 0.0, 0.08655878363458566, 0.04185396064329238, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00016407967709119547, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0] | [0.07922535211267606, 0.5688364849487547, 0.9787144975144626, 0.31195965417867433, 0.7538909807220757, 0.004998016660055534, 0.2453132882197085, 0.006094934808994923, 0.28039426248370025, 0.4027701934381364, 0.0, 0.13362226388449272, 0.06992656875834447, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00727802037845706, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] | | 3.6581 | 4.0 | 80 | 4.1884 | 0.0342 | 0.0867 | 0.3058 | [0.12062739412640987, 0.24829013302570388, 0.39237585037166106, 0.11740129305086813, 0.39894128970163617, 0.018848776624051683, 0.1954229987467484, 0.0, 0.06581415174765558, 0.306830672831732, 0.0, 0.024498229043683588, 0.11174596198219976, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01793143021087362, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 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nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan] | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3