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vit-base-renovation

Image Classification
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  1. README.md +24 -25
  2. model.safetensors +1 -1
  3. runs/Mar20_02-17-56_afa5dd1e3938/events.out.tfevents.1710901087.afa5dd1e3938.399.0 +2 -2
  4. runs/Mar20_02-17-56_afa5dd1e3938/events.out.tfevents.1710902001.afa5dd1e3938.399.1 +3 -0
README.md CHANGED
@@ -2,7 +2,6 @@
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  datasets:
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  - renovation
@@ -15,7 +14,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: renovations
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  type: renovation
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  config: default
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  split: validation
@@ -23,7 +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: 0.6545454545454545
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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
@@ -31,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-renovation
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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 renovations dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7622
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- - Accuracy: 0.6545
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  ## Model description
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@@ -66,25 +65,25 @@ 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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- | 0.9737 | 0.2 | 25 | 1.0076 | 0.5045 |
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- | 0.862 | 0.4 | 50 | 1.0220 | 0.5045 |
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- | 0.9064 | 0.6 | 75 | 0.9076 | 0.5591 |
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- | 0.8528 | 0.81 | 100 | 0.8157 | 0.65 |
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- | 0.8848 | 1.01 | 125 | 0.8089 | 0.6273 |
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- | 0.6608 | 1.21 | 150 | 0.8615 | 0.6409 |
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- | 0.6748 | 1.41 | 175 | 0.8426 | 0.6318 |
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- | 0.6559 | 1.61 | 200 | 0.8427 | 0.6091 |
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- | 0.5654 | 1.81 | 225 | 0.8267 | 0.6682 |
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- | 0.5254 | 2.02 | 250 | 0.7622 | 0.6545 |
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- | 0.2778 | 2.22 | 275 | 0.9481 | 0.6636 |
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- | 0.309 | 2.42 | 300 | 0.8998 | 0.6409 |
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- | 0.2396 | 2.62 | 325 | 0.9171 | 0.6409 |
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- | 0.2773 | 2.82 | 350 | 1.0582 | 0.6091 |
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- | 0.2516 | 3.02 | 375 | 0.9362 | 0.6455 |
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- | 0.1578 | 3.23 | 400 | 0.9264 | 0.6773 |
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- | 0.0979 | 3.43 | 425 | 0.9470 | 0.6773 |
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- | 0.0836 | 3.63 | 450 | 0.9941 | 0.6682 |
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- | 0.126 | 3.83 | 475 | 0.9761 | 0.6864 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
 
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  - generated_from_trainer
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  datasets:
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  - renovation
 
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  name: Image Classification
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  type: image-classification
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  dataset:
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+ name: renovation
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  type: renovation
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  config: default
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  split: validation
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6712328767123288
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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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  # vit-base-renovation
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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 renovation dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1227
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+ - Accuracy: 0.6712
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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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+ | 1.3065 | 0.2 | 25 | 1.2953 | 0.4201 |
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+ | 1.1693 | 0.4 | 50 | 1.2178 | 0.4749 |
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+ | 1.1812 | 0.6 | 75 | 1.1296 | 0.4932 |
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+ | 1.0392 | 0.81 | 100 | 1.0653 | 0.5936 |
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+ | 0.9393 | 1.01 | 125 | 1.0614 | 0.5936 |
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+ | 0.7521 | 1.21 | 150 | 1.1803 | 0.5342 |
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+ | 0.6482 | 1.41 | 175 | 0.9854 | 0.6210 |
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+ | 0.6643 | 1.61 | 200 | 1.0757 | 0.5616 |
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+ | 0.7273 | 1.81 | 225 | 1.0664 | 0.5662 |
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+ | 0.6387 | 2.02 | 250 | 0.9146 | 0.6575 |
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+ | 0.3924 | 2.22 | 275 | 0.9536 | 0.6530 |
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+ | 0.3131 | 2.42 | 300 | 1.0534 | 0.6347 |
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+ | 0.299 | 2.62 | 325 | 1.0690 | 0.6256 |
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+ | 0.296 | 2.82 | 350 | 1.1816 | 0.6027 |
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+ | 0.1765 | 3.02 | 375 | 0.9577 | 0.6667 |
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+ | 0.1152 | 3.23 | 400 | 1.0853 | 0.6712 |
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+ | 0.112 | 3.43 | 425 | 1.0749 | 0.6849 |
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+ | 0.1083 | 3.63 | 450 | 1.1111 | 0.6804 |
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+ | 0.0969 | 3.83 | 475 | 1.1227 | 0.6712 |
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  ### Framework versions
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