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upernet-convnext-small-finetuned
jpodivin/upernet-convnext-small-finetuned
upernet-convnext-small-finetuned is a image segmentation model from jpodivin. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- mit
- Model type
- upernet
- Access
- Public
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024
- SHA
- acedcadf
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
upernet-convnext-small-finetuned
This model is a fine-tuned version of openmmlab/upernet-convnext-small on the jpodivin/plantorgans dataset. It achieves the following results on the evaluation set:
- Loss: 0.2874
- Mean Iou: 0.4231
- Mean Accuracy: 0.5343
- Overall Accuracy: 0.7437
- Accuracy Void: nan
- Accuracy Fruit: 0.8642
- Accuracy Leaf: 0.7167
- Accuracy Flower: 0.0
- Accuracy Stem: 0.5563
- Iou Void: 0.0
- Iou Fruit: 0.8605
- Iou Leaf: 0.7108
- Iou Flower: 0.0
- Iou Stem: 0.5440
- Median Iou: 0.5440
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: 0.0006
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| 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 | Median Iou |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8456 | 1.0 | 575 | 0.3074 | 0.3946 | 0.4987 | 0.7054 | nan | 0.8110 | 0.6951 | 0.0 | 0.4888 | 0.0 | 0.8088 | 0.6852 | 0.0 | 0.4791 | 0.4791 |
| 0.3006 | 2.0 | 1150 | 0.2868 | 0.3945 | 0.4965 | 0.7227 | nan | 0.8533 | 0.7186 | 0.0 | 0.4139 | 0.0 | 0.8494 | 0.7139 | 0.0 | 0.4092 | 0.4092 |
| 0.3315 | 3.0 | 1725 | 0.2874 | 0.4231 | 0.5343 | 0.7437 | nan | 0.8642 | 0.7167 | 0.0 | 0.5563 | 0.0 | 0.8605 | 0.7108 | 0.0 | 0.5440 | 0.5440 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Task
- Image Segmentation
- Library
- transformers
- Type
- upernet
- License
- mit
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024