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---
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-mini-mask2former_augmentation_default
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune-instance-segmentation-mini-mask2former_augmentation_default
This model is a fine-tuned version of [facebook/mask2former-swin-tiny-coco-instance](https://huggingface.co/facebook/mask2former-swin-tiny-coco-instance) on the jsalavedra/strawberry_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 16.2566
- Map: 0.6229
- Map 50: 0.8172
- Map 75: 0.6824
- Map Small: 0.35
- Map Medium: 0.401
- Map Large: 0.7146
- Mar 1: 0.4279
- Mar 10: 0.7886
- Mar 100: 0.831
- Mar Small: 0.5
- Mar Medium: 0.7108
- Mar Large: 0.8802
- Map Angular leafspot: 0.54
- Mar 100 Angular leafspot: 0.8135
- Map Anthracnose fruit rot: 0.405
- Mar 100 Anthracnose fruit rot: 0.7118
- Map Blossom blight: 0.7372
- Mar 100 Blossom blight: 0.8159
- Map Gray mold: 0.5767
- Mar 100 Gray mold: 0.7648
- Map Leaf spot: 0.8783
- Mar 100 Leaf spot: 0.9416
- Map Powdery mildew fruit: 0.5019
- Mar 100 Powdery mildew fruit: 0.8833
- Map Powdery mildew leaf: 0.7209
- Mar 100 Powdery mildew leaf: 0.8863
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: constant
- num_epochs: 10.0
- 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 Angular leafspot | Mar 100 Angular leafspot | Map Anthracnose fruit rot | Mar 100 Anthracnose fruit rot | Map Blossom blight | Mar 100 Blossom blight | Map Gray mold | Mar 100 Gray mold | Map Leaf spot | Mar 100 Leaf spot | Map Powdery mildew fruit | Mar 100 Powdery mildew fruit | Map Powdery mildew leaf | Mar 100 Powdery mildew leaf |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------------------:|:------------------------:|:-------------------------:|:-----------------------------:|:------------------:|:----------------------:|:-------------:|:-----------------:|:-------------:|:-----------------:|:------------------------:|:----------------------------:|:-----------------------:|:---------------------------:|
| 48.1383 | 1.0 | 91 | 32.7441 | 0.072 | 0.0944 | 0.0789 | 0.0008 | 0.028 | 0.1218 | 0.1444 | 0.2815 | 0.3583 | 0.2 | 0.1977 | 0.4277 | 0.0097 | 0.4962 | 0.001 | 0.0529 | 0.0025 | 0.0727 | 0.0527 | 0.2222 | 0.1818 | 0.8401 | 0.0002 | 0.05 | 0.2557 | 0.7737 |
| 28.1321 | 2.0 | 182 | 26.8257 | 0.2203 | 0.2891 | 0.2395 | 0.1267 | 0.1186 | 0.2617 | 0.2987 | 0.5351 | 0.5913 | 0.4 | 0.3726 | 0.6649 | 0.0203 | 0.5981 | 0.0049 | 0.1706 | 0.1769 | 0.65 | 0.1961 | 0.6324 | 0.6355 | 0.9082 | 0.0087 | 0.3444 | 0.4998 | 0.8353 |
| 23.6192 | 3.0 | 273 | 23.0934 | 0.3141 | 0.408 | 0.3423 | 0.2133 | 0.195 | 0.3834 | 0.3621 | 0.6516 | 0.6971 | 0.4 | 0.4291 | 0.7797 | 0.0457 | 0.7288 | 0.0175 | 0.4059 | 0.4873 | 0.7205 | 0.2984 | 0.7102 | 0.7575 | 0.9249 | 0.0257 | 0.5444 | 0.5666 | 0.8451 |
| 20.6054 | 4.0 | 364 | 21.1837 | 0.398 | 0.5303 | 0.434 | 0.2115 | 0.237 | 0.4998 | 0.3852 | 0.7062 | 0.7568 | 0.5 | 0.5624 | 0.8306 | 0.264 | 0.7365 | 0.0368 | 0.5882 | 0.5386 | 0.75 | 0.434 | 0.7287 | 0.7965 | 0.9331 | 0.107 | 0.7056 | 0.6088 | 0.8557 |
| 19.4288 | 5.0 | 455 | 19.9111 | 0.4366 | 0.5924 | 0.4686 | 0.1758 | 0.256 | 0.5269 | 0.4005 | 0.7295 | 0.7887 | 0.4 | 0.5917 | 0.8558 | 0.3157 | 0.7788 | 0.0679 | 0.6471 | 0.5715 | 0.7636 | 0.4758 | 0.7389 | 0.8107 | 0.9315 | 0.1825 | 0.8 | 0.632 | 0.8608 |
| 17.8867 | 6.0 | 546 | 18.9302 | 0.5064 | 0.6834 | 0.5457 | 0.35 | 0.3232 | 0.5693 | 0.395 | 0.7462 | 0.8006 | 0.45 | 0.6427 | 0.8583 | 0.3472 | 0.7731 | 0.1802 | 0.7 | 0.6516 | 0.7909 | 0.5193 | 0.7398 | 0.8209 | 0.9362 | 0.3584 | 0.8 | 0.6671 | 0.8639 |
| 16.9985 | 7.0 | 637 | 18.2692 | 0.5458 | 0.7266 | 0.6036 | 0.2667 | 0.353 | 0.6138 | 0.4145 | 0.7701 | 0.8142 | 0.4 | 0.6488 | 0.876 | 0.4325 | 0.775 | 0.2601 | 0.7118 | 0.7072 | 0.8159 | 0.5295 | 0.7472 | 0.8566 | 0.9424 | 0.3656 | 0.8333 | 0.6692 | 0.8737 |
| 16.1493 | 8.0 | 728 | 17.3118 | 0.5707 | 0.7557 | 0.6298 | 0.4036 | 0.3684 | 0.6464 | 0.4175 | 0.7831 | 0.8275 | 0.45 | 0.6815 | 0.8822 | 0.4954 | 0.7923 | 0.3283 | 0.7588 | 0.7096 | 0.8159 | 0.5474 | 0.7528 | 0.8717 | 0.9436 | 0.3381 | 0.8444 | 0.7045 | 0.8847 |
| 15.3879 | 9.0 | 819 | 16.7216 | 0.5907 | 0.7634 | 0.6611 | 0.35 | 0.3824 | 0.6636 | 0.4145 | 0.7808 | 0.8258 | 0.45 | 0.6663 | 0.8821 | 0.5305 | 0.8019 | 0.3748 | 0.7529 | 0.7337 | 0.8136 | 0.5658 | 0.7574 | 0.8738 | 0.9377 | 0.3421 | 0.8333 | 0.7144 | 0.8835 |
| 14.4614 | 10.0 | 910 | 16.2566 | 0.6229 | 0.8172 | 0.6824 | 0.35 | 0.401 | 0.7146 | 0.4279 | 0.7886 | 0.831 | 0.5 | 0.7108 | 0.8802 | 0.54 | 0.8135 | 0.405 | 0.7118 | 0.7372 | 0.8159 | 0.5767 | 0.7648 | 0.8783 | 0.9416 | 0.5019 | 0.8833 | 0.7209 | 0.8863 |
### Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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