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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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Push Keras model using huggingface_hub.

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Files changed (18) hide show
  1. .gitattributes +1 -0
  2. README.md +19 -82
  3. fingerprint.pb +3 -0
  4. history.json +50 -0
  5. keras_metadata.pb +3 -0
  6. logs/events.out.tfevents.1701792988.TheHornyPrincess.307266.0 +3 -0
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README.md CHANGED
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1
  ---
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- license: apache-2.0
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- base_model: google/vit-base-patch16-224-in21k
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- tags:
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- - generated_from_trainer
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- datasets:
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- - imagefolder
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- metrics:
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- - accuracy
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- model-index:
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- - name: attraction-classifier
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- results:
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- - task:
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- name: Image Classification
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- type: image-classification
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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: 0.7955974842767296
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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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- should probably proofread and complete it, then remove this comment. -->
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-
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- # attraction-classifier
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-
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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: 0.4691
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- - Accuracy: 0.7956
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-
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  ## Model description
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  More information needed
@@ -52,53 +19,23 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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- - seed: 69
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 64
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: cosine
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- - lr_scheduler_warmup_ratio: 0.15
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- - num_epochs: 15
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6703 | 0.34 | 15 | 0.6354 | 0.7327 |
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- | 0.5449 | 0.67 | 30 | 0.5836 | 0.7421 |
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- | 0.5407 | 1.01 | 45 | 0.5594 | 0.7421 |
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- | 0.5255 | 1.34 | 60 | 0.5294 | 0.7547 |
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- | 0.5586 | 1.68 | 75 | 0.5171 | 0.7642 |
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- | 0.5438 | 2.01 | 90 | 0.5212 | 0.7704 |
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- | 0.4807 | 2.35 | 105 | 0.5181 | 0.7390 |
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- | 0.6202 | 2.68 | 120 | 0.4972 | 0.7704 |
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- | 0.5021 | 3.02 | 135 | 0.4566 | 0.7987 |
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- | 0.4313 | 3.35 | 150 | 0.4852 | 0.7925 |
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- | 0.3532 | 3.69 | 165 | 0.4378 | 0.8113 |
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- | 0.3577 | 4.02 | 180 | 0.4515 | 0.8019 |
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- | 0.4736 | 4.36 | 195 | 0.4498 | 0.7893 |
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- | 0.3516 | 4.69 | 210 | 0.4408 | 0.8239 |
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- | 0.4437 | 5.03 | 225 | 0.4611 | 0.7799 |
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- | 0.3543 | 5.36 | 240 | 0.4294 | 0.8208 |
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- | 0.4029 | 5.7 | 255 | 0.4155 | 0.8428 |
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- | 0.3808 | 6.03 | 270 | 0.4116 | 0.8302 |
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- | 0.3211 | 6.37 | 285 | 0.4009 | 0.8302 |
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- | 0.2949 | 6.7 | 300 | 0.4321 | 0.8176 |
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- | 0.2663 | 7.04 | 315 | 0.4229 | 0.8396 |
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- | 0.3049 | 7.37 | 330 | 0.4110 | 0.8365 |
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- | 0.1303 | 7.71 | 345 | 0.4288 | 0.8333 |
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- | 0.2079 | 8.04 | 360 | 0.4218 | 0.8208 |
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- | 0.208 | 8.38 | 375 | 0.3908 | 0.8365 |
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- | 0.2067 | 8.72 | 390 | 0.5191 | 0.7862 |
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- | 0.1635 | 9.05 | 405 | 0.4691 | 0.7956 |
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-
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- ### Framework versions
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- - Transformers 4.35.2
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- - Pytorch 2.0.1+cu117
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- - Datasets 2.15.0
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- - Tokenizers 0.15.0
 
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  ---
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+ library_name: keras
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ## Model description
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  More information needed
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ | Hyperparameters | Value |
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+ | :-- | :-- |
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+ | name | Adam |
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+ | weight_decay | None |
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+ | clipnorm | None |
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+ | global_clipnorm | None |
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+ | clipvalue | None |
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+ | use_ema | False |
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+ | ema_momentum | 0.99 |
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+ | ema_overwrite_frequency | None |
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+ | jit_compile | True |
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+ | is_legacy_optimizer | False |
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+ | learning_rate | 0.0010000000474974513 |
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+ | beta_1 | 0.9 |
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+ | beta_2 | 0.999 |
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+ | epsilon | 1e-07 |
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+ | amsgrad | False |
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+ | training_precision | float32 |
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