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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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model-index: |
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- name: image_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: 0.6 |
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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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# image_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 [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: 1.1877 |
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- Accuracy: 0.6 |
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## Model description |
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Hey everyone! This is the first model I’ve deployed :D. This emotion recognition model is a fine-tuned version of google/vit-base-patch16-224-in21k, trained on the ImageFolder dataset. As a first-timer, I’m proud that this model has achieved such accuracy. I plan to further train it to improve its accuracy. Wish me luck! |
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## Intended uses & limitations |
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I strongly suggest using an input picture with a clear indication of emotion, as I’ve found that the model can sometimes misinterpret the output. Additionally, this model seems to lack confidence in identifying emotions, as evidenced by the slightly varying scores. |
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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: 4 |
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- eval_batch_size: 4 |
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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: linear |
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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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| No log | 1.0 | 160 | 1.5920 | 0.375 | |
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| No log | 2.0 | 320 | 1.4689 | 0.4313 | |
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| No log | 3.0 | 480 | 1.3699 | 0.4625 | |
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| 1.4989 | 4.0 | 640 | 1.2204 | 0.5813 | |
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| 1.4989 | 5.0 | 800 | 1.2019 | 0.5437 | |
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| 1.4989 | 6.0 | 960 | 1.2126 | 0.55 | |
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| 0.9362 | 7.0 | 1120 | 1.1846 | 0.5563 | |
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| 0.9362 | 8.0 | 1280 | 1.2819 | 0.5312 | |
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| 0.9362 | 9.0 | 1440 | 1.2583 | 0.525 | |
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| 0.5396 | 10.0 | 1600 | 1.1571 | 0.6 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
