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finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000

Image Segmentation
Transformers
Safetensors
mask2former
instance-segmentation
vision
Generated from Trainer
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finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000 / README.md
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metadata
library_name: transformers
license: other
base_model: facebook/mask2former-swin-tiny-coco-instance
tags:
  - image-segmentation
  - instance-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000
    results: []

finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000

This model is a fine-tuned version of facebook/mask2former-swin-tiny-coco-instance on the MananSuri27/flowchartseg dataset. It achieves the following results on the evaluation set:

  • Loss: 6.9523
  • Map: 0.7683
  • Map 50: 0.9698
  • Map 75: 0.9012
  • Map Small: 0.6166
  • Map Medium: 0.8012
  • Map Large: 0.9911
  • Mar 1: 0.0414
  • Mar 10: 0.3998
  • Mar 100: 0.8409
  • Mar Small: 0.6954
  • Mar Medium: 0.8711
  • Mar Large: 0.9938
  • Map Per Class: 0.7683
  • Mar 100 Per Class: 0.8409
  • Classes: 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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Map Map 50 Map 75 Map Small Map Medium Map Large Mar 1 Mar 10 Mar 100 Mar Small Mar Medium Mar Large Map Per Class Mar 100 Per Class Classes
21.8993 1.0 75 12.4440 0.6744 0.9348 0.7871 0.4962 0.7115 0.9595 0.0394 0.3779 0.7627 0.5896 0.7977 0.9741 0.6744 0.7627 0
10.836 2.0 150 10.1046 0.7114 0.9553 0.8305 0.5421 0.7495 0.9806 0.0399 0.3853 0.7924 0.6239 0.8267 0.9864 0.7114 0.7924 0
9.422 3.0 225 9.3849 0.7277 0.9599 0.8501 0.5591 0.7674 0.9829 0.0398 0.3882 0.8057 0.6454 0.8385 0.9852 0.7277 0.8057 0
8.7626 4.0 300 8.7372 0.7337 0.9632 0.8564 0.5685 0.7713 0.9825 0.0401 0.3902 0.8122 0.6565 0.8441 0.9889 0.7337 0.8122 0
8.2403 5.0 375 8.3938 0.7416 0.9673 0.8706 0.5659 0.7812 0.991 0.0403 0.3914 0.8177 0.657 0.8508 0.9926 0.7416 0.8177 0
7.8836 6.0 450 7.9279 0.7504 0.9692 0.8794 0.5853 0.7878 0.9901 0.0412 0.3964 0.824 0.6685 0.8561 0.9914 0.7504 0.824 0
7.6029 7.0 525 7.8302 0.7562 0.9707 0.8933 0.5965 0.7925 0.9909 0.0415 0.3969 0.8294 0.68 0.8603 0.9926 0.7562 0.8294 0
7.3823 8.0 600 7.5476 0.756 0.9689 0.8892 0.5973 0.792 0.9908 0.0412 0.3982 0.8315 0.6809 0.8626 0.9926 0.756 0.8315 0
7.2456 9.0 675 7.4617 0.7611 0.969 0.8979 0.6055 0.795 0.9902 0.0411 0.3995 0.8338 0.6865 0.8642 0.9926 0.7611 0.8338 0
7.068 10.0 750 7.3238 0.7627 0.9688 0.8988 0.6028 0.7964 0.9901 0.0413 0.3995 0.8347 0.685 0.8656 0.9938 0.7627 0.8347 0
7.0105 11.0 825 7.2475 0.7655 0.9703 0.8992 0.6099 0.7982 0.991 0.0409 0.4007 0.8372 0.6893 0.8679 0.9938 0.7655 0.8372 0
6.9651 12.0 900 7.2345 0.7679 0.9709 0.9045 0.6147 0.8008 0.9899 0.0412 0.3999 0.8393 0.6944 0.8692 0.9938 0.7679 0.8393 0
6.8849 13.0 975 7.0420 0.764 0.9698 0.8996 0.6091 0.7977 0.9899 0.0405 0.3996 0.8377 0.692 0.8677 0.9938 0.764 0.8377 0
6.8559 14.0 1050 7.0714 0.7643 0.9693 0.8986 0.6084 0.7991 0.9899 0.0413 0.3994 0.8387 0.6913 0.8693 0.9938 0.7643 0.8387 0
6.7735 15.0 1125 6.9974 0.7671 0.9702 0.9007 0.6119 0.8014 0.9908 0.0413 0.3998 0.8395 0.6902 0.8706 0.9938 0.7671 0.8395 0
6.7596 16.0 1200 6.9287 0.7675 0.9702 0.9041 0.614 0.8007 0.991 0.0406 0.399 0.8393 0.6931 0.8696 0.9938 0.7675 0.8393 0
6.7191 17.0 1275 7.0029 0.7678 0.9701 0.9039 0.61 0.8016 0.991 0.0407 0.3991 0.8407 0.6943 0.8711 0.9938 0.7678 0.8407 0
6.6811 18.0 1350 6.9408 0.7678 0.97 0.901 0.6121 0.8014 0.991 0.041 0.4008 0.84 0.6928 0.8705 0.9938 0.7678 0.84 0
6.6732 19.0 1425 6.9605 0.7679 0.9696 0.9012 0.6142 0.801 0.9911 0.0413 0.4005 0.8405 0.6935 0.871 0.9938 0.7679 0.8405 0
6.7621 19.7407 1480 6.9523 0.7683 0.9698 0.9012 0.6166 0.8012 0.9911 0.0414 0.3998 0.8409 0.6954 0.8711 0.9938 0.7683 0.8409 0

Framework versions

  • Transformers 4.49.0.dev0
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.21.0