Model save
Browse files- README.md +80 -0
- model.safetensors +1 -1
README.md
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---
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license: apache-2.0
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base_model: facebook/convnextv2-base-22k-224
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: convnextv2-base-22k-224-finetuned-eurosat
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8418079096045198
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# convnextv2-base-22k-224-finetuned-eurosat
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This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4207
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- Accuracy: 0.8418
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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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_ratio: 0.1
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.5909 | 1.0 | 198 | 0.5287 | 0.7966 |
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| 0.2224 | 2.0 | 396 | 0.4225 | 0.8418 |
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| 0.1975 | 3.0 | 594 | 0.4207 | 0.8418 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.2.0+cu118
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- Datasets 2.17.0
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- Tokenizers 0.15.2
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model.safetensors
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
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size 350829548
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version https://git-lfs.github.com/spec/v1
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size 350829548
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