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HorcruxNo13
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vit-base-patch16-224

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +23 -23
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7066666666666667
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  - name: Precision
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  type: precision
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- value: 0.5034113712374582
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  - name: Recall
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  type: recall
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- value: 0.7066666666666667
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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
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5891
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- - Accuracy: 0.7067
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- - Precision: 0.5034
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- - Recall: 0.7067
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- - F1 Score: 0.5880
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  ## Model description
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@@ -78,21 +78,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.5970 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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- | No log | 2.0 | 8 | 0.5990 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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- | No log | 3.0 | 12 | 0.5648 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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- | 0.6217 | 4.0 | 16 | 0.6035 | 0.7042 | 0.6625 | 0.7042 | 0.6709 |
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- | 0.6217 | 5.0 | 20 | 0.5560 | 0.7333 | 0.8050 | 0.7333 | 0.6286 |
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- | 0.6217 | 6.0 | 24 | 0.5656 | 0.7167 | 0.6184 | 0.7167 | 0.6194 |
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- | 0.6217 | 7.0 | 28 | 0.5552 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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- | 0.5729 | 8.0 | 32 | 0.5532 | 0.7292 | 0.7126 | 0.7292 | 0.6263 |
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- | 0.5729 | 9.0 | 36 | 0.5634 | 0.7292 | 0.6863 | 0.7292 | 0.6453 |
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- | 0.5729 | 10.0 | 40 | 0.5589 | 0.7333 | 0.7009 | 0.7333 | 0.6536 |
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- | 0.5729 | 11.0 | 44 | 0.5676 | 0.7292 | 0.6848 | 0.7292 | 0.6612 |
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- | 0.5599 | 12.0 | 48 | 0.5655 | 0.7333 | 0.6952 | 0.7333 | 0.6688 |
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- | 0.5599 | 13.0 | 52 | 0.5692 | 0.7333 | 0.6954 | 0.7333 | 0.6816 |
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- | 0.5599 | 14.0 | 56 | 0.5746 | 0.725 | 0.6864 | 0.725 | 0.6863 |
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- | 0.5382 | 15.0 | 60 | 0.5752 | 0.7208 | 0.6832 | 0.7208 | 0.6864 |
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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: 0.9033333333333333
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  - name: Precision
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  type: precision
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+ value: 0.892075919335706
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  - name: Recall
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  type: recall
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+ value: 0.9033333333333333
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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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2446
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+ - Accuracy: 0.9033
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+ - Precision: 0.8921
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+ - Recall: 0.9033
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+ - F1 Score: 0.8889
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.5037 | 0.8667 | 0.8150 | 0.8667 | 0.8224 |
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+ | No log | 2.0 | 8 | 0.3500 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | No log | 3.0 | 12 | 0.3154 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.5284 | 4.0 | 16 | 0.2974 | 0.8833 | 0.8659 | 0.8833 | 0.8497 |
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+ | 0.5284 | 5.0 | 20 | 0.2954 | 0.8875 | 0.8731 | 0.8875 | 0.8768 |
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+ | 0.5284 | 6.0 | 24 | 0.2721 | 0.8958 | 0.8871 | 0.8958 | 0.8716 |
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+ | 0.5284 | 7.0 | 28 | 0.2679 | 0.8875 | 0.8817 | 0.8875 | 0.8527 |
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+ | 0.3362 | 8.0 | 32 | 0.2634 | 0.8875 | 0.8817 | 0.8875 | 0.8527 |
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+ | 0.3362 | 9.0 | 36 | 0.2507 | 0.9042 | 0.8953 | 0.9042 | 0.8879 |
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+ | 0.3362 | 10.0 | 40 | 0.2439 | 0.9083 | 0.9006 | 0.9083 | 0.8941 |
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+ | 0.3362 | 11.0 | 44 | 0.2589 | 0.8917 | 0.8861 | 0.8917 | 0.8884 |
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+ | 0.3017 | 12.0 | 48 | 0.2428 | 0.9083 | 0.9005 | 0.9083 | 0.9024 |
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+ | 0.3017 | 13.0 | 52 | 0.2543 | 0.9 | 0.8949 | 0.9 | 0.8970 |
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+ | 0.3017 | 14.0 | 56 | 0.2651 | 0.8958 | 0.8944 | 0.8958 | 0.8951 |
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+ | 0.278 | 15.0 | 60 | 0.2637 | 0.8958 | 0.8944 | 0.8958 | 0.8951 |
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  ### Framework versions
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