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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_backboneFrozen
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_backboneFrozen
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: 19.5597
- Map: 0.4279
- Map 50: 0.592
- Map 75: 0.476
- Map Small: 0.3002
- Map Medium: 0.2838
- Map Large: 0.4955
- Mar 1: 0.3752
- Mar 10: 0.7188
- Mar 100: 0.7682
- Mar Small: 0.45
- Mar Medium: 0.5977
- Mar Large: 0.8285
- Map Angular leafspot: 0.162
- Mar 100 Angular leafspot: 0.7154
- Map Anthracnose fruit rot: 0.1562
- Mar 100 Anthracnose fruit rot: 0.6118
- Map Blossom blight: 0.6367
- Mar 100 Blossom blight: 0.7455
- Map Gray mold: 0.4846
- Mar 100 Gray mold: 0.7259
- Map Leaf spot: 0.7665
- Mar 100 Leaf spot: 0.9253
- Map Powdery mildew fruit: 0.1744
- Mar 100 Powdery mildew fruit: 0.8056
- Map Powdery mildew leaf: 0.6152
- Mar 100 Powdery mildew leaf: 0.8478
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------------------:|:------------------------:|:-------------------------:|:-----------------------------:|:------------------:|:----------------------:|:-------------:|:-----------------:|:-------------:|:-----------------:|:------------------------:|:----------------------------:|:-----------------------:|:---------------------------:|
| 53.1072 | 1.0 | 91 | 35.8147 | 0.0329 | 0.0466 | 0.0372 | 0.0 | 0.0194 | 0.052 | 0.0866 | 0.2227 | 0.2877 | 0.0 | 0.1691 | 0.3315 | 0.0011 | 0.0577 | 0.0005 | 0.0353 | 0.0005 | 0.0455 | 0.1032 | 0.3204 | 0.0595 | 0.7537 | 0.0003 | 0.0611 | 0.0653 | 0.74 |
| 31.9378 | 2.0 | 182 | 31.1508 | 0.146 | 0.1929 | 0.1592 | 0.0002 | 0.0603 | 0.1944 | 0.1728 | 0.3353 | 0.3875 | 0.05 | 0.2558 | 0.456 | 0.0361 | 0.1942 | 0.0012 | 0.0353 | 0.0142 | 0.1909 | 0.2208 | 0.5352 | 0.4042 | 0.8455 | 0.0017 | 0.1222 | 0.3436 | 0.7894 |
| 28.4456 | 3.0 | 273 | 27.9586 | 0.2437 | 0.3525 | 0.2638 | 0.0043 | 0.1252 | 0.2879 | 0.2934 | 0.5682 | 0.6154 | 0.15 | 0.4384 | 0.695 | 0.0633 | 0.55 | 0.0105 | 0.1824 | 0.297 | 0.5682 | 0.3008 | 0.6528 | 0.5844 | 0.8805 | 0.0137 | 0.6778 | 0.4367 | 0.7961 |
| 25.1946 | 4.0 | 364 | 25.4498 | 0.2996 | 0.4292 | 0.3245 | 0.0868 | 0.1635 | 0.3718 | 0.315 | 0.6344 | 0.6812 | 0.45 | 0.5052 | 0.7494 | 0.0876 | 0.5827 | 0.0309 | 0.4 | 0.4503 | 0.6591 | 0.3582 | 0.6889 | 0.6635 | 0.8946 | 0.0214 | 0.7333 | 0.4852 | 0.8098 |
| 23.2545 | 5.0 | 455 | 23.8964 | 0.3353 | 0.4803 | 0.3614 | 0.1095 | 0.19 | 0.4087 | 0.3296 | 0.672 | 0.7218 | 0.4 | 0.5347 | 0.7931 | 0.1005 | 0.6288 | 0.0936 | 0.5235 | 0.5137 | 0.6909 | 0.4064 | 0.6972 | 0.675 | 0.9019 | 0.038 | 0.7833 | 0.5196 | 0.8267 |
| 21.9945 | 6.0 | 546 | 22.5007 | 0.366 | 0.5185 | 0.3862 | 0.1667 | 0.2226 | 0.4397 | 0.3526 | 0.6857 | 0.729 | 0.4 | 0.5301 | 0.8134 | 0.1146 | 0.6788 | 0.1253 | 0.5294 | 0.578 | 0.7114 | 0.4334 | 0.7 | 0.701 | 0.9082 | 0.0576 | 0.75 | 0.5525 | 0.8255 |
| 20.5361 | 7.0 | 637 | 21.4663 | 0.383 | 0.5312 | 0.4124 | 0.3 | 0.2354 | 0.4501 | 0.3424 | 0.6987 | 0.7456 | 0.4 | 0.5526 | 0.8183 | 0.1369 | 0.7 | 0.1323 | 0.5941 | 0.5833 | 0.7227 | 0.449 | 0.7111 | 0.7275 | 0.9097 | 0.0821 | 0.75 | 0.5701 | 0.8318 |
| 19.5394 | 8.0 | 728 | 20.5817 | 0.4007 | 0.5596 | 0.4369 | 0.3 | 0.2559 | 0.4733 | 0.3649 | 0.7059 | 0.7526 | 0.4 | 0.5733 | 0.8147 | 0.1224 | 0.6923 | 0.161 | 0.6059 | 0.5924 | 0.7205 | 0.4677 | 0.7204 | 0.7382 | 0.9179 | 0.1318 | 0.7722 | 0.5918 | 0.8388 |
| 18.9893 | 9.0 | 819 | 20.0124 | 0.4102 | 0.5697 | 0.4529 | 0.3 | 0.266 | 0.478 | 0.3705 | 0.7166 | 0.7613 | 0.4 | 0.5824 | 0.8326 | 0.1426 | 0.7 | 0.1322 | 0.6412 | 0.6233 | 0.7409 | 0.4696 | 0.7194 | 0.7467 | 0.9195 | 0.1517 | 0.7667 | 0.6049 | 0.8416 |
| 18.3583 | 10.0 | 910 | 19.5597 | 0.4279 | 0.592 | 0.476 | 0.3002 | 0.2838 | 0.4955 | 0.3752 | 0.7188 | 0.7682 | 0.45 | 0.5977 | 0.8285 | 0.162 | 0.7154 | 0.1562 | 0.6118 | 0.6367 | 0.7455 | 0.4846 | 0.7259 | 0.7665 | 0.9253 | 0.1744 | 0.8056 | 0.6152 | 0.8478 |
### Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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