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--- |
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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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- FastJobs/Visual_Emotional_Analysis |
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metrics: |
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- accuracy |
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- precision |
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- f1 |
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model-index: |
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- name: emo-vit-base-patch16-224-in21k |
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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: FastJobs/Visual_Emotional_Analysis |
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type: FastJobs/Visual_Emotional_Analysis |
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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: 0.61875 |
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- name: Precision |
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type: precision |
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value: 0.6229001976284585 |
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- name: F1 |
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type: f1 |
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value: 0.6163114517061885 |
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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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# emo-vit-base-patch16-224-in21k |
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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) |
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on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2392 |
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- Accuracy: 0.6188 |
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- Precision: 0.6229 |
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- F1: 0.6163 |
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## Training and evaluation data |
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### Data Split |
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Used a 4:1 ratio for training and development sets and a seed of 42. |
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### Pre-processing Augmentation |
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The main pre-processing phase for both training and evaluation includes: |
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- Resizing to (224, 224, 3) |
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- Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] |
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Other than the aforementioned pre-processing, the training set was augmented using: |
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- Random horizontal & vertical flip |
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- Color jitter |
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- Random resized crop |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| |
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| 2.0652 | 1.0 | 10 | 1.9712 | 0.35 | 0.3441 | 0.3294 | |
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| 1.9006 | 2.0 | 20 | 1.6055 | 0.425 | 0.3497 | 0.3578 | |
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| 1.6274 | 3.0 | 30 | 1.4991 | 0.4875 | 0.5747 | 0.4621 | |
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| 1.4742 | 4.0 | 40 | 1.4417 | 0.4313 | 0.4744 | 0.4037 | |
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| 1.3546 | 5.0 | 50 | 1.3699 | 0.4125 | 0.3896 | 0.3387 | |
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| 1.2574 | 6.0 | 60 | 1.2200 | 0.5125 | 0.5072 | 0.4783 | |
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| 1.183 | 7.0 | 70 | 1.1368 | 0.5375 | 0.5802 | 0.5341 | |
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| 1.0869 | 8.0 | 80 | 1.1332 | 0.5687 | 0.6024 | 0.5622 | |
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| 1.002 | 9.0 | 90 | 1.1178 | 0.55 | 0.5663 | 0.5423 | |
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| 0.9453 | 10.0 | 100 | 1.1601 | 0.5563 | 0.5994 | 0.5515 | |
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| 0.9495 | 11.0 | 110 | 1.1202 | 0.525 | 0.5695 | 0.5266 | |
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| 0.7805 | 12.0 | 120 | 1.1620 | 0.5375 | 0.5577 | 0.5323 | |
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| 0.7487 | 13.0 | 130 | 1.2094 | 0.5687 | 0.6218 | 0.5716 | |
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| 0.6805 | 14.0 | 140 | 1.2662 | 0.5437 | 0.5875 | 0.5345 | |
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| 0.6491 | 15.0 | 150 | 1.1673 | 0.5625 | 0.5707 | 0.5511 | |
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| 0.6168 | 16.0 | 160 | 1.2981 | 0.475 | 0.5388 | 0.4846 | |
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| 0.5512 | 17.0 | 170 | 1.2624 | 0.575 | 0.6110 | 0.5726 | |
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| 0.5532 | 18.0 | 180 | 1.2392 | 0.6188 | 0.6229 | 0.6163 | |
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| 0.4931 | 19.0 | 190 | 1.4012 | 0.5375 | 0.5542 | 0.5277 | |
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| 0.4919 | 20.0 | 200 | 1.2323 | 0.5813 | 0.5825 | 0.5758 | |
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| 0.4243 | 21.0 | 210 | 1.3046 | 0.5875 | 0.5967 | 0.5750 | |
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| 0.3971 | 22.0 | 220 | 1.3169 | 0.5687 | 0.5812 | 0.5610 | |
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| 0.3534 | 23.0 | 230 | 1.4052 | 0.5625 | 0.6240 | 0.5527 | |
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| 0.3456 | 24.0 | 240 | 1.3372 | 0.5875 | 0.5998 | 0.5838 | |
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| 0.3381 | 25.0 | 250 | 1.4000 | 0.55 | 0.5589 | 0.5468 | |
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| 0.3786 | 26.0 | 260 | 1.3531 | 0.5687 | 0.6269 | 0.5764 | |
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| 0.3614 | 27.0 | 270 | 1.3696 | 0.5687 | 0.6019 | 0.5704 | |
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| 0.312 | 28.0 | 280 | 1.3523 | 0.6125 | 0.6351 | 0.6148 | |
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| 0.2643 | 29.0 | 290 | 1.4510 | 0.5813 | 0.6286 | 0.5825 | |
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| 0.3553 | 30.0 | 300 | 1.5255 | 0.6062 | 0.6560 | 0.6113 | |
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| 0.2807 | 31.0 | 310 | 1.5901 | 0.5813 | 0.5921 | 0.5655 | |
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| 0.3252 | 32.0 | 320 | 1.5669 | 0.575 | 0.5764 | 0.5639 | |
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| 0.3796 | 33.0 | 330 | 1.6251 | 0.5375 | 0.5776 | 0.5431 | |
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| 0.2635 | 34.0 | 340 | 1.7397 | 0.4938 | 0.5513 | 0.4944 | |
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| 0.2583 | 35.0 | 350 | 1.4806 | 0.6 | 0.6566 | 0.6099 | |
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| 0.3006 | 36.0 | 360 | 1.4808 | 0.5813 | 0.6310 | 0.5863 | |
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| 0.3082 | 37.0 | 370 | 1.7077 | 0.5188 | 0.5680 | 0.5156 | |
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| 0.3346 | 38.0 | 380 | 1.6861 | 0.575 | 0.6725 | 0.5638 | |
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| 0.291 | 39.0 | 390 | 1.5484 | 0.5625 | 0.5631 | 0.5535 | |
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| 0.2313 | 40.0 | 400 | 1.4933 | 0.5563 | 0.5564 | 0.5526 | |
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| 0.2163 | 41.0 | 410 | 1.5836 | 0.5938 | 0.6046 | 0.5929 | |
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| 0.2201 | 42.0 | 420 | 1.6363 | 0.5687 | 0.5954 | 0.5672 | |
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| 0.2077 | 43.0 | 430 | 1.6746 | 0.5687 | 0.5623 | 0.5622 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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