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MananSuri27
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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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+ ---
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+ library_name: transformers
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+ license: other
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+ base_model: facebook/mask2former-swin-tiny-coco-instance
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000
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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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+ # finetune-instance-segmentation-flowchartseg-mask2former_20epochs_a6000
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+
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+ This model is a fine-tuned version of [facebook/mask2former-swin-tiny-coco-instance](https://huggingface.co/facebook/mask2former-swin-tiny-coco-instance) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 6.9523
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+ - Map: 0.7683
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+ - Map 50: 0.9698
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+ - Map 75: 0.9012
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+ - Map Small: 0.6166
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+ - Map Medium: 0.8012
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+ - Map Large: 0.9911
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+ - Mar 1: 0.0414
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+ - Mar 10: 0.3998
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+ - Mar 100: 0.8409
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+ - Mar Small: 0.6954
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+ - Mar Medium: 0.8711
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+ - Mar Large: 0.9938
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+ - Map Per Class: 0.7683
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+ - Mar 100 Per Class: 0.8409
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+ - Classes: 0
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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: 1e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 20.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-------------:|:-----------------:|:-------:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0.dev0
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.21.0