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convnext-tiny-224-finetuned-eurosat-albumentations

Image Classification
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  1. README.md +16 -7
  2. model.safetensors +1 -1
  3. runs/Apr06_13-45-13_615cc53a6797/events.out.tfevents.1712411576.615cc53a6797.172.9 +3 -0
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.49640287769784175
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0272
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- - Accuracy: 0.4964
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  ## Model description
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@@ -61,15 +61,24 @@ 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_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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- | 1.094 | 0.96 | 19 | 1.0497 | 0.4317 |
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- | 1.0281 | 1.97 | 39 | 1.0299 | 0.4676 |
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- | 1.0053 | 2.89 | 57 | 1.0272 | 0.4964 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6366906474820144
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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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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8091
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+ - Accuracy: 0.6367
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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_ratio: 0.1
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+ - num_epochs: 12
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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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+ | 1.1311 | 0.96 | 19 | 1.0751 | 0.3813 |
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+ | 1.0477 | 1.97 | 39 | 1.0354 | 0.5036 |
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+ | 0.9932 | 2.99 | 59 | 1.0054 | 0.5144 |
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+ | 0.9445 | 4.0 | 79 | 0.9702 | 0.5432 |
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+ | 0.8911 | 4.96 | 98 | 0.9461 | 0.5647 |
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+ | 0.8339 | 5.97 | 118 | 0.9079 | 0.5827 |
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+ | 0.7923 | 6.99 | 138 | 0.8767 | 0.5899 |
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+ | 0.751 | 8.0 | 158 | 0.8521 | 0.6187 |
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+ | 0.7222 | 8.96 | 177 | 0.8315 | 0.6223 |
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+ | 0.688 | 9.97 | 197 | 0.8183 | 0.6259 |
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+ | 0.6734 | 10.99 | 217 | 0.8091 | 0.6367 |
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+ | 0.6734 | 11.54 | 228 | 0.8090 | 0.6331 |
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  ### Framework versions
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