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image_segmentation_text

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metadata
license: other
base_model: nvidia/mit-b0
tags:
  - generated_from_trainer
model-index:
  - name: image_segmentation_text_v2
    results: []

image_segmentation_text_v2

This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2273
  • Mean Iou: 0.7888
  • Mean Accuracy: 0.8815
  • Overall Accuracy: 0.9466
  • Per Category Iou: [0.9411436688097194, 0.6365339140286779]
  • Per Category Accuracy: [0.9666298272627839, 0.7963078307432842]

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: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.6854 0.4 20 0.6497 0.5097 0.8417 0.7443 [0.7115032459977992, 0.3077993102868545] [0.7144421527142827, 0.9689304620217858]
0.5904 0.8 40 0.5143 0.5981 0.8004 0.8478 [0.8333935175312267, 0.3627987944393932] [0.862384168080342, 0.7383421777739057]
0.4687 1.2 60 0.4546 0.6050 0.8161 0.8493 [0.8342992363445556, 0.37569323794278087] [0.8595097956337959, 0.7727067629087438]
0.424 1.6 80 0.3844 0.6584 0.8272 0.8881 [0.8773645633114495, 0.43953238690068724] [0.9067963946134603, 0.7476733768251818]
0.3485 2.0 100 0.3535 0.6942 0.8808 0.8995 [0.8882922581800861, 0.5000111990627714] [0.9052579907882474, 0.8563377644392374]
0.3402 2.4 120 0.3424 0.7021 0.9165 0.8977 [0.8850157203428243, 0.5191490567411473] [0.891946363487535, 0.9410955056545037]
0.3037 2.8 140 0.3071 0.7357 0.9150 0.9173 [0.9073510172037412, 0.5639900768760655] [0.9179667207588744, 0.9119966243321158]
0.2669 3.2 160 0.2528 0.7659 0.8552 0.9410 [0.9353899879693552, 0.5964963563086936] [0.9673518346317019, 0.7429811204460667]
0.225 3.6 180 0.2523 0.7695 0.8894 0.9375 [0.9308386361574114, 0.6082593419593063] [0.9523221012048093, 0.826397566814428]
0.2703 4.0 200 0.2430 0.7812 0.8943 0.9419 [0.9355827639904635, 0.6268385945303665] [0.9564666637969618, 0.8320982324555644]
0.2607 4.4 220 0.2352 0.7875 0.8918 0.9448 [0.9389026387973957, 0.6361696692483538] [0.9610553391463873, 0.822527121722995]
0.2314 4.8 240 0.2273 0.7888 0.8815 0.9466 [0.9411436688097194, 0.6365339140286779] [0.9666298272627839, 0.7963078307432842]

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0