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vit-base-patch16-224-ve-U13b-80RX1

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  1. README.md +45 -45
  2. model.safetensors +1 -1
  3. runs/Jun23_16-08-59_DESKTOP-SKBE9FB/events.out.tfevents.1719180541.DESKTOP-SKBE9FB.16180.0 +2 -2
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: 0.8043478260869565
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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 [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: 25847261397527471458109882368.0000
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- - Accuracy: 0.8043
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  ## Model description
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@@ -65,48 +65,48 @@ The following hyperparameters were used during training:
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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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- | 62421131753362939742087282688.0000 | 0.99 | 51 | 25847261397527471458109882368.0000 | 0.3696 |
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- | 68366009359745128248184406016.0000 | 2.0 | 103 | 25847261397527471458109882368.0000 | 0.5435 |
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- | 56476265480660303086264254464.0000 | 2.99 | 154 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 59448700505958211923205947392.0000 | 4.0 | 206 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 57962481104362666995704922112.0000 | 4.99 | 257 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 56476269258553484104324612096.0000 | 6.0 | 309 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 60934916129660575832646615040.0000 | 6.99 | 360 | 25847261397527471458109882368.0000 | 0.8043 |
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- | 69852217427661121325411336192.0000 | 8.0 | 412 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 59448704283851401737359327232.0000 | 8.99 | 463 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 81741965084639127505391845376.0000 | 10.0 | 515 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 57962484882255848013765279744.0000 | 10.99 | 566 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 57962473548576287367398162432.0000 | 12.0 | 618 | 25847261397527471458109882368.0000 | 0.8043 |
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- | 57962484882255848013765279744.0000 | 12.99 | 669 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 60934916129660575832646615040.0000 | 14.0 | 721 | 25847261397527471458109882368.0000 | 0.8043 |
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- | 57962477326469477181551542272.0000 | 14.99 | 772 | 25847261397527471458109882368.0000 | 0.7826 |
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- | 50531391652171295598227488768.0000 | 16.0 | 824 | 25847261397527471458109882368.0000 | 0.7826 |
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- | 57962481104362666995704922112.0000 | 16.99 | 875 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 47558960404766567779346153472.0000 | 18.0 | 927 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 68366001803958757415970668544.0000 | 18.99 | 978 | 25847261397527471458109882368.0000 | 0.7826 |
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- | 60439517218248428756995670016.0000 | 20.0 | 1030 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 75797095034043309831508459520.0000 | 20.99 | 1081 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 59448700505958211923205947392.0000 | 22.0 | 1133 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 63907354932851674483741687808.0000 | 22.99 | 1184 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 78769533837234408482603532288.0000 | 24.0 | 1236 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 66879786180256393506530000896.0000 | 24.99 | 1287 | 25847261397527471458109882368.0000 | 0.8043 |
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- | 56476269258553484104324612096.0000 | 26.0 | 1339 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 66879786180256393506530000896.0000 | 26.99 | 1390 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 60934919907553756850706972672.0000 | 28.0 | 1442 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 54990046079064749362670206976.0000 | 28.99 | 1493 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 69852232539233862989838811136.0000 | 30.0 | 1545 | 25847261397527471458109882368.0000 | 0.7826 |
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- | 71338440607149856067065741312.0000 | 30.99 | 1596 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 66879793736042764338743738368.0000 | 32.0 | 1648 | 25847261397527471458109882368.0000 | 0.7609 |
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- | 44586525379468658942404460544.0000 | 32.99 | 1699 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 59448700505958211923205947392.0000 | 34.0 | 1751 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 63907347377065294855434928128.0000 | 34.99 | 1802 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 75797095034043309831508459520.0000 | 36.0 | 1854 | 25847261397527471458109882368.0000 | 0.7391 |
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- | 62421135531256120760147640320.0000 | 36.99 | 1905 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 53503830455362394249322561536.0000 | 38.0 | 1957 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 56476265480660303086264254464.0000 | 38.99 | 2008 | 25847261397527471458109882368.0000 | 0.7174 |
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- | 53503826677469204435169181696.0000 | 39.61 | 2040 | 25847261397527471458109882368.0000 | 0.7174 |
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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: 0.7608695652173914
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  ---
27
 
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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: 1.2530
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+ - Accuracy: 0.7609
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  ## Model description
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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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+ | 1.3157 | 0.99 | 51 | 1.2968 | 0.3478 |
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+ | 1.0334 | 2.0 | 103 | 1.0060 | 0.5217 |
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+ | 0.691 | 2.99 | 154 | 0.7506 | 0.7609 |
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+ | 0.5005 | 4.0 | 206 | 0.6433 | 0.7826 |
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+ | 0.3478 | 4.99 | 257 | 0.5674 | 0.7609 |
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+ | 0.3339 | 6.0 | 309 | 0.6623 | 0.7609 |
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+ | 0.2533 | 6.99 | 360 | 0.6905 | 0.7391 |
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+ | 0.138 | 8.0 | 412 | 0.7251 | 0.7826 |
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+ | 0.1289 | 8.99 | 463 | 0.7467 | 0.7391 |
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+ | 0.152 | 10.0 | 515 | 0.9011 | 0.7174 |
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+ | 0.2609 | 10.99 | 566 | 1.0150 | 0.7174 |
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+ | 0.2202 | 12.0 | 618 | 0.9713 | 0.7826 |
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+ | 0.1083 | 12.99 | 669 | 1.1106 | 0.6739 |
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+ | 0.07 | 14.0 | 721 | 1.1211 | 0.7174 |
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+ | 0.0791 | 14.99 | 772 | 1.1830 | 0.7609 |
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+ | 0.0427 | 16.0 | 824 | 0.7770 | 0.8478 |
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+ | 0.1219 | 16.99 | 875 | 1.0962 | 0.7391 |
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+ | 0.0739 | 18.0 | 927 | 0.9447 | 0.7609 |
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+ | 0.1989 | 18.99 | 978 | 1.1543 | 0.7391 |
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+ | 0.1097 | 20.0 | 1030 | 1.1795 | 0.7609 |
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+ | 0.1204 | 20.99 | 1081 | 1.2679 | 0.6739 |
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+ | 0.0514 | 22.0 | 1133 | 1.0646 | 0.7174 |
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+ | 0.0612 | 22.99 | 1184 | 1.1413 | 0.6957 |
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+ | 0.0207 | 24.0 | 1236 | 0.8928 | 0.7826 |
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+ | 0.1063 | 24.99 | 1287 | 1.1186 | 0.7609 |
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+ | 0.1076 | 26.0 | 1339 | 1.1741 | 0.7609 |
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+ | 0.0714 | 26.99 | 1390 | 1.0977 | 0.8043 |
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+ | 0.062 | 28.0 | 1442 | 1.3965 | 0.7174 |
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+ | 0.0617 | 28.99 | 1493 | 1.1849 | 0.7609 |
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+ | 0.0536 | 30.0 | 1545 | 1.0865 | 0.7826 |
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+ | 0.0707 | 30.99 | 1596 | 1.2081 | 0.7609 |
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+ | 0.0967 | 32.0 | 1648 | 1.3300 | 0.7391 |
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+ | 0.0564 | 32.99 | 1699 | 1.2240 | 0.7826 |
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+ | 0.0435 | 34.0 | 1751 | 1.2391 | 0.7609 |
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+ | 0.043 | 34.99 | 1802 | 1.1813 | 0.7609 |
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+ | 0.0218 | 36.0 | 1854 | 1.2496 | 0.7826 |
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+ | 0.0043 | 36.99 | 1905 | 1.2797 | 0.7174 |
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+ | 0.0051 | 38.0 | 1957 | 1.2493 | 0.7391 |
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+ | 0.0123 | 38.99 | 2008 | 1.2538 | 0.7391 |
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+ | 0.0546 | 39.61 | 2040 | 1.2530 | 0.7609 |
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  ### Framework versions
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