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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +19 -19
  2. model.safetensors +1 -1
README.md CHANGED
@@ -16,13 +16,13 @@ model-index:
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  dataset:
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  name: imagefolder
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  type: imagefolder
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- config: smtn_girls_likeOrNot
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  split: train
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- args: smtn_girls_likeOrNot
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8286558345642541
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3887
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- - Accuracy: 0.8287
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  ## Model description
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@@ -67,21 +67,21 @@ 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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- | 0.5824 | 0.99 | 42 | 0.5195 | 0.7829 |
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- | 0.4574 | 2.0 | 85 | 0.4473 | 0.8154 |
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- | 0.4165 | 2.99 | 127 | 0.3977 | 0.8316 |
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- | 0.346 | 4.0 | 170 | 0.3881 | 0.8390 |
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- | 0.3025 | 4.99 | 212 | 0.3950 | 0.8213 |
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- | 0.3085 | 6.0 | 255 | 0.3965 | 0.8139 |
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- | 0.2646 | 6.99 | 297 | 0.3895 | 0.8552 |
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- | 0.3022 | 8.0 | 340 | 0.3828 | 0.8390 |
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- | 0.2384 | 8.99 | 382 | 0.3878 | 0.8375 |
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- | 0.2162 | 9.88 | 420 | 0.3887 | 0.8287 |
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  ### Framework versions
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- - Transformers 4.31.0
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- - Pytorch 2.0.1+cu117
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- - Datasets 2.12.0
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- - Tokenizers 0.13.3
 
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  dataset:
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  name: imagefolder
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  type: imagefolder
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+ config: default
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  split: train
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+ args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8389955686853766
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3983
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+ - Accuracy: 0.8390
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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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+ | 0.5745 | 0.99 | 42 | 0.5208 | 0.7829 |
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+ | 0.4617 | 2.0 | 85 | 0.4346 | 0.8065 |
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+ | 0.4245 | 2.99 | 127 | 0.4151 | 0.8346 |
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+ | 0.3512 | 4.0 | 170 | 0.3854 | 0.8508 |
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+ | 0.3146 | 4.99 | 212 | 0.4062 | 0.8360 |
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+ | 0.3235 | 6.0 | 255 | 0.3864 | 0.8390 |
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+ | 0.2699 | 6.99 | 297 | 0.4094 | 0.8508 |
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+ | 0.3049 | 8.0 | 340 | 0.3735 | 0.8567 |
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+ | 0.2459 | 8.99 | 382 | 0.4037 | 0.8360 |
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+ | 0.2277 | 9.88 | 420 | 0.3983 | 0.8390 |
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  ### Framework versions
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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