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nvidia-segformer-b0-finetuned-ade-512-512-finetuned-ISIC17

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metadata
license: other
tags:
  - generated_from_trainer
model-index:
  - name: nvidia-segformer-b0-finetuned-ade-512-512-finetuned-ISIC17
    results: []

nvidia-segformer-b0-finetuned-ade-512-512-finetuned-ISIC17

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1948
  • Mean Iou: 0.8064
  • Mean Accuracy: 0.8726
  • Overall Accuracy: 0.9381
  • Per Category Iou: [0.6841604127643356, 0.9285439643646547]
  • Per Category Accuracy: [0.7721651141608432, 0.9729809595315688]

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.481 0.16 10 0.4235 0.6191 0.6970 0.8761 [0.3719409076673884, 0.8662862424406493] [0.42270204900152314, 0.9713331864930521]
0.4147 0.32 20 0.3894 0.7067 0.8502 0.8853 [0.5464942438498753, 0.8668431573745645] [0.7965579529885418, 0.9038859083170013]
0.356 0.48 30 0.3148 0.7467 0.8513 0.9107 [0.5963581593534901, 0.897077797385972] [0.7603709174964982, 0.9422313184595918]
0.3039 0.63 40 0.3024 0.7620 0.8671 0.9162 [0.6211722830632663, 0.9028139512386881] [0.7918407335685692, 0.9422883932404167]
0.2545 0.79 50 0.2849 0.7766 0.8898 0.9201 [0.6468577863419183, 0.9063792530493855] [0.8432862096150755, 0.9362151542385662]
0.2635 0.95 60 0.2504 0.7828 0.8644 0.9279 [0.6487213857926865, 0.9168129696986418] [0.7671470887645524, 0.9616549114054705]
0.2175 1.11 70 0.2497 0.7849 0.8682 0.9283 [0.6526705030304356, 0.9171225024239068] [0.7762677096648272, 0.9602225755678137]
0.2025 1.27 80 0.2400 0.7840 0.8632 0.9288 [0.6501844204669202, 0.9178944798865282] [0.7627291445016801, 0.9636411137781736]
0.2035 1.43 90 0.2288 0.7931 0.8749 0.9313 [0.6657367286733036, 0.9203778068784213] [0.7885027822639286, 0.9612655167036179]
0.2488 1.59 100 0.2110 0.7978 0.8719 0.9341 [0.6717638717220313, 0.923859975121704] [0.7766611302038285, 0.9672003292652145]
0.1954 1.75 110 0.2067 0.7962 0.8597 0.9354 [0.666599427783381, 0.9258672754383861] [0.7436428904928473, 0.9757231213956472]
0.1806 1.9 120 0.2047 0.7926 0.8525 0.9349 [0.6596059897565958, 0.925563006736469] [0.726197674685608, 0.9787940661520825]
0.161 2.06 130 0.2047 0.7903 0.8505 0.9342 [0.6558737849234609, 0.9247714617107691] [0.7223974159771602, 0.9786951901233297]
0.1736 2.22 140 0.2023 0.7948 0.8588 0.9349 [0.6643652721485811, 0.9252950591002775] [0.742124317828686, 0.9754152391272543]
0.1947 2.38 150 0.2077 0.7985 0.8656 0.9355 [0.6712414223331253, 0.9257326708494226] [0.7585178608332249, 0.9726888331181641]
0.1464 2.54 160 0.1960 0.8030 0.8680 0.9373 [0.678274892507806, 0.9276935390097538] [0.7620104248788739, 0.9740685958478499]
0.1644 2.7 170 0.1964 0.8064 0.8751 0.9377 [0.6847175060674714, 0.9279857318627613] [0.7791196258677832, 0.9710404169835255]
0.1803 2.86 180 0.1948 0.8064 0.8726 0.9381 [0.6841604127643356, 0.9285439643646547] [0.7721651141608432, 0.9729809595315688]

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.0+cu116
  • Datasets 2.7.0
  • Tokenizers 0.12.1