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weed_resnet_balanced

Image Classification
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PyTorch
resnet
Generated from Trainer
Eval Results
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8678571428571429
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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
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4372
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- - Accuracy: 0.8679
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  ## Model description
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@@ -66,16 +66,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.4335 | 1.0 | 69 | 2.4087 | 0.2375 |
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- | 2.3043 | 2.0 | 138 | 2.2215 | 0.3339 |
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- | 1.8342 | 3.0 | 207 | 1.6984 | 0.5786 |
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- | 1.4059 | 4.0 | 276 | 1.1954 | 0.6804 |
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- | 1.0081 | 5.0 | 345 | 0.8756 | 0.7482 |
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- | 0.8916 | 6.0 | 414 | 0.6818 | 0.8232 |
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- | 0.7313 | 7.0 | 483 | 0.5369 | 0.8482 |
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- | 0.6677 | 8.0 | 552 | 0.5223 | 0.8554 |
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- | 0.6206 | 9.0 | 621 | 0.4609 | 0.8732 |
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- | 0.6543 | 10.0 | 690 | 0.4372 | 0.8679 |
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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: 0.7766666666666666
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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-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2397
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+ - Accuracy: 0.7767
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.4803 | 0.99 | 37 | 2.4724 | 0.1133 |
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+ | 2.4464 | 1.99 | 74 | 2.4305 | 0.2967 |
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+ | 2.3843 | 2.99 | 111 | 2.3658 | 0.4233 |
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+ | 2.3018 | 3.99 | 148 | 2.2287 | 0.5067 |
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+ | 2.1075 | 4.99 | 185 | 2.0144 | 0.5967 |
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+ | 1.8743 | 5.99 | 222 | 1.7228 | 0.65 |
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+ | 1.7114 | 6.99 | 259 | 1.5487 | 0.6833 |
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+ | 1.5345 | 7.99 | 296 | 1.3920 | 0.7267 |
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+ | 1.4471 | 8.99 | 333 | 1.2914 | 0.7333 |
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+ | 1.3994 | 9.99 | 370 | 1.2397 | 0.7767 |
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  ### Framework versions