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resnet-18

Image Classification
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Safetensors
resnet
Generated from Trainer
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  1. README.md +14 -15
  2. model.safetensors +1 -1
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: 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
@@ -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 [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8444
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- - Accuracy: 1.0
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  ## Model description
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@@ -65,18 +65,17 @@ 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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- | No log | 0.9091 | 5 | 1.0318 | 0.4156 |
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- | 1.0893 | 2.0 | 11 | 0.9520 | 0.6364 |
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- | 1.0893 | 2.9091 | 16 | 0.9017 | 0.8442 |
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- | 0.9912 | 4.0 | 22 | 0.8444 | 1.0 |
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- | 0.9912 | 4.9091 | 27 | 0.8027 | 1.0 |
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- | 0.9248 | 6.0 | 33 | 0.7631 | 1.0 |
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- | 0.9248 | 6.9091 | 38 | 0.7369 | 1.0 |
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- | 0.8716 | 8.0 | 44 | 0.7156 | 1.0 |
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- | 0.8716 | 8.9091 | 49 | 0.7137 | 1.0 |
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- | 0.8517 | 9.0909 | 50 | 0.7117 | 1.0 |
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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: 0.3645833333333333
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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 [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 11764246045329271284874346496.0000
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+ - Accuracy: 0.3646
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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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+ | No log | 0.8889 | 6 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12280654765223701014034513920.0000 | 1.9259 | 13 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12468689009365309292638371840.0000 | 2.9630 | 20 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12468689009365309292638371840.0000 | 4.0 | 27 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12353039198673126935870046208.0000 | 4.8889 | 33 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12598814762274077621016854528.0000 | 5.9259 | 40 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12598814762274077621016854528.0000 | 6.9630 | 47 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12352969307649181344730185728.0000 | 8.0 | 54 | 11764246045329271284874346496.0000 | 0.3646 |
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+ | 12396413190344989372642754560.0000 | 8.8889 | 60 | 11764246045329271284874346496.0000 | 0.3646 |
 
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  ### Framework versions
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