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beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans

Image Segmentation
Transformers
Safetensors
beit
vision
Generated from Trainer
Inference Endpoints
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  1. README.md +22 -17
  2. model.safetensors +1 -1
README.md CHANGED
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  license: apache-2.0
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  base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
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  tags:
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- - image-segmentation
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- - vision
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  - generated_from_trainer
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  model-index:
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  - name: beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans
@@ -15,22 +13,22 @@ should probably proofread and complete it, then remove this comment. -->
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  # beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans
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- This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the jpodivin/plantorgans dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1427
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- - Mean Iou: 0.0320
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- - Mean Accuracy: 0.0400
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- - Overall Accuracy: 0.0533
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  - Accuracy Void: nan
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- - Accuracy Fruit: 0.1591
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  - Accuracy Leaf: 0.0
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  - Accuracy Flower: 0.0
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- - Accuracy Stem: 0.0007
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  - Iou Void: 0.0
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- - Iou Fruit: 0.1591
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  - Iou Leaf: 0.0
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  - Iou Flower: 0.0
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- - Iou Stem: 0.0007
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  ## Model description
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@@ -56,15 +54,22 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 100
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- - num_epochs: 3.0
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  ### Training results
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- | Training Loss | Epoch | Step | Accuracy Flower | Accuracy Fruit | Accuracy Leaf | Accuracy Stem | Accuracy Void | Iou Flower | Iou Fruit | Iou Leaf | Iou Stem | Iou Void | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-------------:|:-------------:|:-------------:|:----------:|:---------:|:--------:|:--------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:|
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- | 0.6144 | 1.0 | 1149 | 0.0 | 0.0064 | 0.0013 | 0.0027 | nan | 0.0 | 0.0064 | 0.0013 | 0.0027 | 0.0 | 1.2207 | 0.0026 | 0.0021 | 0.0032 |
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- | 0.5033 | 2.0 | 2298 | 0.0 | 0.3503 | 0.0000 | 0.0006 | nan | 0.0 | 0.3487 | 0.0000 | 0.0006 | 0.0 | 1.0653 | 0.0877 | 0.0699 | 0.1172 |
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- | 0.4595 | 3.0 | 3447 | 0.0 | 0.1591 | 0.0 | 0.0007 | nan | 0.0 | 0.1591 | 0.0 | 0.0007 | 0.0 | 1.1426 | 0.0400 | 0.0320 | 0.0533 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
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  tags:
 
 
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  - generated_from_trainer
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  model-index:
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  - name: beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans
 
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  # beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2004
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+ - Mean Iou: 0.0260
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+ - Mean Accuracy: 0.0325
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+ - Overall Accuracy: 0.0435
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  - Accuracy Void: nan
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+ - Accuracy Fruit: 0.1299
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  - Accuracy Leaf: 0.0
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  - Accuracy Flower: 0.0
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+ - Accuracy Stem: 0.0001
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  - Iou Void: 0.0
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+ - Iou Fruit: 0.1299
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  - Iou Leaf: 0.0
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  - Iou Flower: 0.0
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+ - Iou Stem: 0.0001
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 10.0
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Void | Accuracy Fruit | Accuracy Leaf | Accuracy Flower | Accuracy Stem | Iou Void | Iou Fruit | Iou Leaf | Iou Flower | Iou Stem |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------------:|:-------------:|:---------------:|:-------------:|:--------:|:---------:|:--------:|:----------:|:--------:|
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+ | 1.0657 | 1.0 | 575 | 1.1178 | 0.0158 | 0.0197 | 0.0261 | nan | 0.0776 | 0.0 | 0.0 | 0.0012 | 0.0 | 0.0776 | 0.0 | 0.0 | 0.0012 |
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+ | 0.5398 | 2.0 | 1150 | 1.0816 | 0.0361 | 0.0451 | 0.0599 | nan | 0.1783 | 0.0000 | 0.0 | 0.0020 | 0.0 | 0.1783 | 0.0000 | 0.0 | 0.0020 |
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+ | 0.4794 | 3.0 | 1725 | 1.2122 | 0.0110 | 0.0138 | 0.0179 | nan | 0.0517 | 0.0003 | 0.0 | 0.0032 | 0.0 | 0.0517 | 0.0003 | 0.0 | 0.0032 |
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+ | 0.446 | 4.0 | 2300 | 1.3138 | 0.0114 | 0.0142 | 0.0189 | nan | 0.0562 | 0.0 | 0.0 | 0.0006 | 0.0 | 0.0562 | 0.0 | 0.0 | 0.0006 |
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+ | 0.4422 | 5.0 | 2875 | 1.2360 | 0.0005 | 0.0006 | 0.0006 | nan | 0.0012 | 0.0 | 0.0 | 0.0013 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.0013 |
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+ | 0.4183 | 6.0 | 3450 | 1.3598 | 0.0177 | 0.0221 | 0.0296 | nan | 0.0885 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0885 | 0.0 | 0.0 | 0.0 |
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+ | 0.3921 | 7.0 | 4025 | 1.2523 | 0.0267 | 0.0333 | 0.0446 | nan | 0.1333 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.1333 | 0.0000 | 0.0 | 0.0 |
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+ | 0.3743 | 8.0 | 4600 | 1.3146 | 0.0401 | 0.0502 | 0.0670 | nan | 0.2002 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.2001 | 0.0 | 0.0 | 0.0005 |
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+ | 0.3695 | 9.0 | 5175 | 1.2873 | 0.0294 | 0.0368 | 0.0492 | nan | 0.1472 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.1471 | 0.0 | 0.0 | 0.0001 |
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+ | 0.3796 | 10.0 | 5750 | 1.2004 | 0.0260 | 0.0325 | 0.0435 | nan | 0.1299 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.1299 | 0.0 | 0.0 | 0.0001 |
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  ### Framework versions
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