Model save

README.md

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+---
+license: mit
+base_model: openmmlab/upernet-swin-small
+tags:
+- generated_from_trainer
+model-index:
+- name: upernet-swin-small-finetuned
+  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. -->
+
+# upernet-swin-small-finetuned
+
+This model is a fine-tuned version of [openmmlab/upernet-swin-small](https://huggingface.co/openmmlab/upernet-swin-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2914
+- Mean Iou: 0.4182
+- Mean Accuracy: 0.5282
+- Overall Accuracy: 0.7341
+- Accuracy Void: nan
+- Accuracy Fruit: 0.8590
+- Accuracy Leaf: 0.7032
+- Accuracy Flower: 0.0
+- Accuracy Stem: 0.5505
+- Iou Void: 0.0
+- Iou Fruit: 0.8554
+- Iou Leaf: 0.6976
+- Iou Flower: 0.0
+- Iou Stem: 0.5381
+- Median Iou: 0.5381
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------------:|:-------------:|:---------------:|:-------------:|:--------:|:---------:|:--------:|:----------:|:--------:|:----------:|
+| 0.8566        | 1.0   | 575  | 0.3365          | 0.3723   | 0.4705        | 0.6560           | nan           | 0.8000         | 0.6122        | 0.0             | 0.4699        | 0.0      | 0.7976    | 0.6041   | 0.0        | 0.4598   | 0.4598     |
+| 0.3338        | 2.0   | 1150 | 0.3030          | 0.3922   | 0.4937        | 0.7155           | nan           | 0.8558         | 0.7024        | 0.0             | 0.4166        | 0.0      | 0.8517    | 0.6972   | 0.0        | 0.4119   | 0.4119     |
+| 0.3477        | 3.0   | 1725 | 0.2914          | 0.4182   | 0.5282        | 0.7341           | nan           | 0.8590         | 0.7032        | 0.0             | 0.5505        | 0.0      | 0.8554    | 0.6976   | 0.0        | 0.5381   | 0.5381     |
+
+
+### Framework versions
+
+- Transformers 4.38.0.dev0
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0