--- 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: 2.6096 - Mean Iou: 0.0583 - Mean Accuracy: 0.1134 - Overall Accuracy: 0.4953 - Per Category Iou: [0.49571156160551655, 0.6191916715464089, 0.8968421052631579, 0.47251072666473254, 0.20811818596205436, 0.05912066570634839, 0.2660966185074256, 0.07550605119417372, 0.013751337568016756, 0.0, 0.027023646667969515, 0.0, 0.30827548248495285, 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, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0] - Per Category Accuracy: [0.8824801404194488, 0.980265985770467, 0.9541189567430025, 0.9099923542904194, 0.545766298916345, 0.05988275525158769, 0.963693587365818, 0.4566744730679157, 0.01873768788099707, nan, 0.027267492763111193, 0.0, 0.3237553608055193, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0] ## 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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | 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| 4.65 | 1.0 | 20 | 4.8981 | 0.0111 | 0.0542 | 0.1778 | [0.23781923296894605, 0.1071529072845471, 0.3545084214945742, 0.003424336241673703, 0.173500240935784, 0.011141116502031824, 0.14733832310150535, 0.014669356174075356, 0.05021999189393591, 0.0, 0.20008788227926497, 0.0, 0.0, nan, 0.0, 0.00022229279136233725, 0.0, 0.0, 0.0, 2.4877478418787472e-05, 0.0, 0.0, 0.0, 0.0041564660622528315, 0.0, 0.03963539899831648, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.000855431993156544, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025614035087719297, 0.0, 0.0, 0.0, nan, 0.0, 0.00039022867400296575, 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, 0.008476970895733259, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 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, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 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nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0] | | 4.4404 | 4.0 | 80 | 4.0409 | 0.0366 | 0.0985 | 0.3873 | [0.42483159611430193, 0.370073249046759, 0.6883879748800115, 0.12399612721971312, 0.1926325147238582, 0.08451608659288751, 0.2914228998373356, 0.07205439151758536, 0.00990202834287084, 0.0, 0.043551461687236497, 0.0, 0.007207863123407354, 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, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, 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nan, 0.0, nan, 0.0] | | 3.9626 | 5.0 | 100 | 3.7955 | 0.0423 | 0.1024 | 0.4061 | [0.44505904330907126, 0.4500813078041672, 0.750275232202943, 0.32010291930388335, 0.17683639807949833, 0.022026431718061675, 0.24496684527487528, 0.054552854616484805, 0.03414087778976189, 0.0, 0.038498337054079915, 0.0, 5.468066491688539e-05, 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, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1