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segformer-b0-finetuned-pokemon

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b0-finetuned-pokemon
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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-finetuned-pokemon
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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 None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0298
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+ - Mean Iou: 0.4971
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+ - Mean Accuracy: 0.9941
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+ - Overall Accuracy: 0.9941
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+ - Per Category Iou: [0.0, 0.99410193702663]
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+ - Per Category Accuracy: [nan, 0.99410193702663]
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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: 24
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+ - eval_batch_size: 24
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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: 50
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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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+ | 0.0286 | 40.0 | 1160 | 0.0304 | 0.4965 | 0.9929 | 0.9929 | [0.0, 0.9929262067682688] | [nan, 0.9929262067682688] |
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+ | 0.0272 | 41.0 | 1189 | 0.0301 | 0.4966 | 0.9933 | 0.9933 | [0.0, 0.9932505252321254] | [nan, 0.9932505252321254] |
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+ | 0.0279 | 42.0 | 1218 | 0.0305 | 0.4973 | 0.9947 | 0.9947 | [0.0, 0.994698129326027] | [nan, 0.994698129326027] |
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+ | 0.0309 | 43.0 | 1247 | 0.0295 | 0.4966 | 0.9931 | 0.9931 | [0.0, 0.9931164081595019] | [nan, 0.9931164081595019] |
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+ | 0.028 | 44.0 | 1276 | 0.0294 | 0.4967 | 0.9934 | 0.9934 | [0.0, 0.9933975466612431] | [nan, 0.9933975466612431] |
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+ | 0.0276 | 45.0 | 1305 | 0.0298 | 0.4970 | 0.9941 | 0.9941 | [0.0, 0.994062837929389] | [nan, 0.994062837929389] |
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+ | 0.0281 | 46.0 | 1334 | 0.0300 | 0.4969 | 0.9939 | 0.9939 | [0.0, 0.9938931511561724] | [nan, 0.9938931511561724] |
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+ | 0.0273 | 47.0 | 1363 | 0.0297 | 0.4968 | 0.9936 | 0.9936 | [0.0, 0.993593207587916] | [nan, 0.993593207587916] |
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+ | 0.0291 | 48.0 | 1392 | 0.0297 | 0.4970 | 0.9941 | 0.9941 | [0.0, 0.9940591430922732] | [nan, 0.9940591430922732] |
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+ | 0.0269 | 49.0 | 1421 | 0.0294 | 0.4968 | 0.9936 | 0.9936 | [0.0, 0.9936409647363091] | [nan, 0.9936409647363091] |
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+ | 0.0279 | 50.0 | 1450 | 0.0298 | 0.4971 | 0.9941 | 0.9941 | [0.0, 0.99410193702663] | [nan, 0.99410193702663] |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.1
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+ - Pytorch 1.12.0+cu113
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+ - Datasets 2.3.2
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+ - Tokenizers 0.12.1