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segformer-b0-scene-parse-150

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+ ---
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+ license: other
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+ base_model: nvidia/mit-b0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - scene_parse_150
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+ model-index:
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+ - name: segformer-b0-scene-parse-150
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # segformer-b0-scene-parse-150
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.6391
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+ - Mean Iou: 0.0583
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+ - Mean Accuracy: 0.1172
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+ - Overall Accuracy: 0.4492
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+ - Per Category Iou: [0.44041294308583534, nan, nan, 0.6666970930892689, nan, 0.33468836772838806, nan, nan, 0.010483214113553378, nan, 0.09776380089668009, nan, nan, nan, 0.27788378028383004, 0.0, nan, 0.0, 0.0003410360439818898, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10667274078691827, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.07423709941194892, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.02291795825583798, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.12350896966281581, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 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, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan]
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+ - Per Category Accuracy: [0.6424969817484554, nan, nan, 0.8805785816574676, nan, 0.46181318389743264, nan, nan, 0.01073188993573345, nan, 0.10568203217142234, nan, nan, nan, 0.6834847599119304, 0.0, nan, nan, 0.00041847041847041847, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.23207169236547248, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.551230945262614, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.03717359970502464, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.1452108041255328, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 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, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan]
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | 3.747 | 1.0 | 20 | 3.6391 | 0.0583 | 0.1172 | 0.4492 | [0.44041294308583534, nan, nan, 0.6666970930892689, nan, 0.33468836772838806, nan, nan, 0.010483214113553378, nan, 0.09776380089668009, nan, nan, nan, 0.27788378028383004, 0.0, nan, 0.0, 0.0003410360439818898, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.10667274078691827, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.07423709941194892, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.02291795825583798, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.12350896966281581, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 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, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan] | [0.6424969817484554, nan, nan, 0.8805785816574676, nan, 0.46181318389743264, nan, nan, 0.01073188993573345, nan, 0.10568203217142234, nan, nan, nan, 0.6834847599119304, 0.0, nan, nan, 0.00041847041847041847, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.23207169236547248, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.551230945262614, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.03717359970502464, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.1452108041255328, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 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, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan] |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.2.0
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1