Model save

README.md

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+---
+license: mit
+base_model: openmmlab/upernet-convnext-small
+tags:
+- generated_from_trainer
+model-index:
+- name: upernet-convnext-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-convnext-small-finetuned
+
+This model is a fine-tuned version of [openmmlab/upernet-convnext-small](https://huggingface.co/openmmlab/upernet-convnext-small) on an unknown 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