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rshrott
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ryan_model314

Image Classification
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vit
Generated from Trainer
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  1. README.md +29 -17
  2. model.safetensors +1 -1
  3. runs/Mar26_20-28-31_ryanserver/events.out.tfevents.1711499316.ryanserver.7179.0 +2 -2
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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  model-index:
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  - name: ryan_model314
@@ -14,11 +13,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # ryan_model314
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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 beans dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2404
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- - Na Accuracy: 0.96
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- - Ordinal Accuracy: 0.5921
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  ## Model description
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@@ -50,18 +49,31 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Na Accuracy | Ordinal Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|
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- | 0.3682 | 0.32 | 100 | 0.3209 | 0.942 | 0.5268 |
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- | 0.2877 | 0.64 | 200 | 0.2783 | 0.944 | 0.5921 |
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- | 0.3079 | 0.96 | 300 | 0.2544 | 0.958 | 0.5338 |
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- | 0.1901 | 1.28 | 400 | 0.2632 | 0.948 | 0.6061 |
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- | 0.277 | 1.6 | 500 | 0.2404 | 0.96 | 0.5921 |
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- | 0.2081 | 1.92 | 600 | 0.2585 | 0.95 | 0.6270 |
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- | 0.0809 | 2.24 | 700 | 0.2631 | 0.954 | 0.6737 |
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- | 0.1 | 2.56 | 800 | 0.2693 | 0.958 | 0.6364 |
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- | 0.0924 | 2.88 | 900 | 0.2566 | 0.962 | 0.6503 |
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- | 0.0304 | 3.19 | 1000 | 0.2782 | 0.954 | 0.6527 |
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- | 0.0256 | 3.51 | 1100 | 0.2662 | 0.96 | 0.6643 |
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- | 0.0424 | 3.83 | 1200 | 0.2670 | 0.962 | 0.6760 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  model-index:
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  - name: ryan_model314
 
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  # ryan_model314
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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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3635
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+ - Na Accuracy: 0.939
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+ - Ordinal Accuracy: 0.6609
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Na Accuracy | Ordinal Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|
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+ | 0.3042 | 0.16 | 100 | 0.3673 | 0.928 | 0.4671 |
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+ | 0.2904 | 0.32 | 200 | 0.2977 | 0.933 | 0.5790 |
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+ | 0.2648 | 0.48 | 300 | 0.2831 | 0.944 | 0.5940 |
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+ | 0.3036 | 0.64 | 400 | 0.2776 | 0.949 | 0.5871 |
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+ | 0.2656 | 0.8 | 500 | 0.2846 | 0.931 | 0.6101 |
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+ | 0.2954 | 0.96 | 600 | 0.2532 | 0.947 | 0.5952 |
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+ | 0.1991 | 1.12 | 700 | 0.2603 | 0.942 | 0.6078 |
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+ | 0.1678 | 1.28 | 800 | 0.2905 | 0.942 | 0.6332 |
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+ | 0.2514 | 1.44 | 900 | 0.2566 | 0.94 | 0.6090 |
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+ | 0.2328 | 1.6 | 1000 | 0.2884 | 0.94 | 0.5617 |
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+ | 0.1826 | 1.76 | 1100 | 0.2870 | 0.943 | 0.6044 |
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+ | 0.2013 | 1.92 | 1200 | 0.2937 | 0.941 | 0.5905 |
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+ | 0.0663 | 2.08 | 1300 | 0.2954 | 0.938 | 0.6251 |
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+ | 0.1503 | 2.24 | 1400 | 0.3188 | 0.937 | 0.5986 |
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+ | 0.0611 | 2.4 | 1500 | 0.3393 | 0.945 | 0.5998 |
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+ | 0.0743 | 2.56 | 1600 | 0.3182 | 0.942 | 0.6482 |
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+ | 0.0908 | 2.72 | 1700 | 0.3332 | 0.942 | 0.6482 |
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+ | 0.1108 | 2.88 | 1800 | 0.3256 | 0.943 | 0.6459 |
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+ | 0.0786 | 3.04 | 1900 | 0.3222 | 0.944 | 0.6540 |
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+ | 0.043 | 3.2 | 2000 | 0.3501 | 0.941 | 0.6482 |
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+ | 0.0472 | 3.36 | 2100 | 0.3455 | 0.943 | 0.6609 |
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+ | 0.032 | 3.52 | 2200 | 0.3562 | 0.94 | 0.6517 |
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+ | 0.0434 | 3.68 | 2300 | 0.3499 | 0.94 | 0.6597 |
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+ | 0.0341 | 3.84 | 2400 | 0.3611 | 0.94 | 0.6482 |
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+ | 0.0305 | 4.0 | 2500 | 0.3635 | 0.939 | 0.6609 |
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  ### Framework versions
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