End of training
Browse files- README.md +93 -0
- model.safetensors +1 -1
- runs/Oct21_12-57-35_a0c73928d8ac/events.out.tfevents.1729516487.a0c73928d8ac.204.1 +3 -0
- runs/Oct21_13-23-16_a0c73928d8ac/events.out.tfevents.1729517001.a0c73928d8ac.6606.0 +3 -0
- runs/Oct21_13-23-16_a0c73928d8ac/events.out.tfevents.1729517739.a0c73928d8ac.6606.1 +3 -0
- training_args.bin +1 -1
README.md
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---
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library_name: transformers
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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: emotion_classification
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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: 0.525
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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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# emotion_classification
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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: 1.4174
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- Accuracy: 0.525
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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: 42
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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: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 15
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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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| 2.0777 | 1.0 | 10 | 2.0583 | 0.1812 |
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| 2.0139 | 2.0 | 20 | 1.9850 | 0.2687 |
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| 1.8654 | 3.0 | 30 | 1.8583 | 0.3063 |
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| 1.7044 | 4.0 | 40 | 1.7314 | 0.3937 |
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| 1.5957 | 5.0 | 50 | 1.6253 | 0.4125 |
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| 1.5016 | 6.0 | 60 | 1.5818 | 0.3812 |
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| 1.4279 | 7.0 | 70 | 1.5329 | 0.45 |
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| 1.347 | 8.0 | 80 | 1.5491 | 0.425 |
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| 1.3019 | 9.0 | 90 | 1.4662 | 0.5125 |
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| 1.236 | 10.0 | 100 | 1.4375 | 0.5 |
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| 1.1922 | 11.0 | 110 | 1.4149 | 0.5062 |
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| 1.1551 | 12.0 | 120 | 1.4065 | 0.5125 |
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| 1.1501 | 13.0 | 130 | 1.3861 | 0.5125 |
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| 1.1258 | 14.0 | 140 | 1.3940 | 0.5312 |
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| 1.1036 | 15.0 | 150 | 1.4022 | 0.5125 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.19.1
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