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embunna
/
resnet-18

Image Classification
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Safetensors
resnet
Generated from Trainer
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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. runs/Jun06_03-54-41_b446df81307f/events.out.tfevents.1717646089.b446df81307f.157.2 +2 -2
README.md CHANGED
@@ -32,7 +32,7 @@ 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: nan
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  - Accuracy: 0.3958
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  ## Model description
@@ -65,17 +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 | 0.8889 | 6 | nan | 0.3958 |
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- | 0.0 | 1.9259 | 13 | nan | 0.3958 |
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- | 0.0 | 2.9630 | 20 | nan | 0.3958 |
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- | 0.0 | 4.0 | 27 | nan | 0.3958 |
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- | 0.0 | 4.8889 | 33 | nan | 0.3958 |
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- | 0.0 | 5.9259 | 40 | nan | 0.3958 |
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- | 0.0 | 6.9630 | 47 | nan | 0.3958 |
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- | 0.0 | 8.0 | 54 | nan | 0.3958 |
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- | 0.0 | 8.8889 | 60 | nan | 0.3958 |
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  ### Framework versions
 
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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: 2853997620560357538714861174784.0000
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  - Accuracy: 0.3958
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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 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3195940475249625978836740997120.0000 | 1.9259 | 13 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3162732975480959463298416246784.0000 | 2.9630 | 20 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3162732975480959463298416246784.0000 | 4.0 | 27 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3162728139777681004781717422080.0000 | 4.8889 | 33 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3203328220933290977723370438656.0000 | 5.9259 | 40 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3203328220933290977723370438656.0000 | 6.9630 | 47 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3203332331281077554872573755392.0000 | 8.0 | 54 | 2853997620560357538714861174784.0000 | 0.3958 |
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+ | 3162713390882682044075758059520.0000 | 8.8889 | 60 | 2853997620560357538714861174784.0000 | 0.3958 |
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  ### Framework versions
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