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attraction-classifier

Image Classification
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vit
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  1. README.md +23 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -8,7 +8,7 @@ datasets:
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  metrics:
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  - accuracy
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  model-index:
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- - name: emikes-classifier
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  results:
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  - task:
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  name: Image Classification
@@ -22,18 +22,18 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 1.0
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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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- # emikes-classifier
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0253
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- - Accuracy: 1.0
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  ## Model description
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@@ -65,14 +65,24 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.3954 | 1.25 | 10 | 0.3092 | 0.8571 |
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- | 0.1249 | 2.5 | 20 | 0.1407 | 1.0 |
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- | 0.046 | 3.75 | 30 | 0.0666 | 1.0 |
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- | 0.034 | 5.0 | 40 | 0.1060 | 0.9286 |
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- | 0.0255 | 6.25 | 50 | 0.0295 | 1.0 |
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- | 0.0198 | 7.5 | 60 | 0.0274 | 1.0 |
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- | 0.0209 | 8.75 | 70 | 0.1060 | 0.9286 |
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- | 0.02 | 10.0 | 80 | 0.0253 | 1.0 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - accuracy
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  model-index:
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+ - name: attraction-classifier
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  results:
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  - task:
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  name: Image Classification
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8021680216802168
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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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+ # attraction-classifier
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5632
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+ - Accuracy: 0.8022
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6613 | 0.48 | 100 | 0.6067 | 0.6477 |
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+ | 0.6115 | 0.97 | 200 | 0.5579 | 0.6992 |
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+ | 0.5542 | 1.45 | 300 | 0.5501 | 0.7182 |
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+ | 0.4758 | 1.93 | 400 | 0.5108 | 0.7534 |
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+ | 0.5219 | 2.42 | 500 | 0.5122 | 0.7561 |
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+ | 0.4631 | 2.9 | 600 | 0.4842 | 0.7832 |
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+ | 0.3866 | 3.38 | 700 | 0.5298 | 0.7480 |
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+ | 0.3704 | 3.86 | 800 | 0.4963 | 0.7453 |
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+ | 0.4222 | 4.35 | 900 | 0.4832 | 0.7561 |
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+ | 0.3162 | 4.83 | 1000 | 0.4807 | 0.7778 |
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+ | 0.2686 | 5.31 | 1100 | 0.4949 | 0.7859 |
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+ | 0.304 | 5.8 | 1200 | 0.4719 | 0.7751 |
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+ | 0.2246 | 6.28 | 1300 | 0.5014 | 0.8157 |
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+ | 0.2503 | 6.76 | 1400 | 0.5077 | 0.8103 |
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+ | 0.169 | 7.25 | 1500 | 0.4630 | 0.8238 |
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+ | 0.2248 | 7.73 | 1600 | 0.5329 | 0.7832 |
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+ | 0.164 | 8.21 | 1700 | 0.5608 | 0.7859 |
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+ | 0.208 | 8.7 | 1800 | 0.5632 | 0.8022 |
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  ### Framework versions
model.safetensors CHANGED
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