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vit-emotion-classifier

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +54 -14
  2. pytorch_model.bin +1 -1
  3. training_args.bin +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: 0.45
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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: 1.5873
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- - Accuracy: 0.45
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  ## Model description
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@@ -61,22 +61,62 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 10
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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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- | 2.0836 | 1.0 | 10 | 2.0665 | 0.1875 |
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- | 2.0109 | 2.0 | 20 | 1.9931 | 0.2375 |
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- | 1.9063 | 3.0 | 30 | 1.8809 | 0.3875 |
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- | 1.7788 | 4.0 | 40 | 1.7753 | 0.3937 |
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- | 1.6657 | 5.0 | 50 | 1.6817 | 0.4313 |
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- | 1.586 | 6.0 | 60 | 1.6085 | 0.5125 |
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- | 1.5155 | 7.0 | 70 | 1.5815 | 0.5188 |
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- | 1.4671 | 8.0 | 80 | 1.5461 | 0.4813 |
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- | 1.44 | 9.0 | 90 | 1.5231 | 0.4688 |
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- | 1.4175 | 10.0 | 100 | 1.5112 | 0.5062 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.55
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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: 1.3090
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+ - Accuracy: 0.55
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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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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+ | 1.4729 | 1.0 | 10 | 1.5748 | 0.4875 |
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+ | 1.4484 | 2.0 | 20 | 1.5526 | 0.4875 |
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+ | 1.4053 | 3.0 | 30 | 1.5228 | 0.4562 |
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+ | 1.3492 | 4.0 | 40 | 1.4721 | 0.5 |
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+ | 1.2664 | 5.0 | 50 | 1.4448 | 0.5125 |
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+ | 1.2005 | 6.0 | 60 | 1.3783 | 0.5062 |
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+ | 1.1231 | 7.0 | 70 | 1.3427 | 0.5375 |
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+ | 1.0472 | 8.0 | 80 | 1.2859 | 0.5625 |
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+ | 0.9852 | 9.0 | 90 | 1.2732 | 0.5813 |
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+ | 0.8974 | 10.0 | 100 | 1.2220 | 0.575 |
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+ | 0.8314 | 11.0 | 110 | 1.2782 | 0.5312 |
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+ | 0.7964 | 12.0 | 120 | 1.2889 | 0.5437 |
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+ | 0.6993 | 13.0 | 130 | 1.2989 | 0.5188 |
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+ | 0.6915 | 14.0 | 140 | 1.3053 | 0.5375 |
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+ | 0.608 | 15.0 | 150 | 1.2563 | 0.5875 |
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+ | 0.5416 | 16.0 | 160 | 1.2473 | 0.5563 |
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+ | 0.5202 | 17.0 | 170 | 1.2753 | 0.5625 |
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+ | 0.5047 | 18.0 | 180 | 1.2791 | 0.5563 |
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+ | 0.4779 | 19.0 | 190 | 1.3142 | 0.5437 |
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+ | 0.4569 | 20.0 | 200 | 1.2743 | 0.5813 |
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+ | 0.4313 | 21.0 | 210 | 1.2727 | 0.5312 |
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+ | 0.4536 | 22.0 | 220 | 1.2514 | 0.5938 |
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+ | 0.4166 | 23.0 | 230 | 1.3260 | 0.5312 |
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+ | 0.3673 | 24.0 | 240 | 1.2950 | 0.55 |
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+ | 0.3544 | 25.0 | 250 | 1.2268 | 0.5875 |
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+ | 0.3568 | 26.0 | 260 | 1.3874 | 0.4875 |
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+ | 0.3509 | 27.0 | 270 | 1.3735 | 0.525 |
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+ | 0.3711 | 28.0 | 280 | 1.2886 | 0.5375 |
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+ | 0.3555 | 29.0 | 290 | 1.3152 | 0.5375 |
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+ | 0.3068 | 30.0 | 300 | 1.3927 | 0.5375 |
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+ | 0.3007 | 31.0 | 310 | 1.4131 | 0.5188 |
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+ | 0.3062 | 32.0 | 320 | 1.3256 | 0.575 |
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+ | 0.3114 | 33.0 | 330 | 1.3714 | 0.5 |
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+ | 0.279 | 34.0 | 340 | 1.4198 | 0.5188 |
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+ | 0.2888 | 35.0 | 350 | 1.5321 | 0.475 |
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+ | 0.2647 | 36.0 | 360 | 1.4342 | 0.5062 |
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+ | 0.2574 | 37.0 | 370 | 1.4149 | 0.5563 |
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+ | 0.2539 | 38.0 | 380 | 1.4286 | 0.5125 |
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+ | 0.2566 | 39.0 | 390 | 1.4805 | 0.5125 |
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+ | 0.2298 | 40.0 | 400 | 1.3820 | 0.4875 |
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+ | 0.2236 | 41.0 | 410 | 1.3683 | 0.5437 |
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+ | 0.2201 | 42.0 | 420 | 1.3332 | 0.5687 |
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+ | 0.2696 | 43.0 | 430 | 1.4725 | 0.5188 |
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+ | 0.2319 | 44.0 | 440 | 1.3926 | 0.5375 |
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+ | 0.2269 | 45.0 | 450 | 1.3477 | 0.5563 |
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+ | 0.2201 | 46.0 | 460 | 1.4054 | 0.5563 |
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+ | 0.2114 | 47.0 | 470 | 1.3308 | 0.55 |
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+ | 0.2319 | 48.0 | 480 | 1.3353 | 0.5625 |
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+ | 0.2177 | 49.0 | 490 | 1.3019 | 0.5437 |
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+ | 0.2042 | 50.0 | 500 | 1.3089 | 0.5875 |
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  ### Framework versions
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