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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: SkLIP-masked |
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results: [] |
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license: mit |
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datasets: |
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- joshuachou/SkinCAP |
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language: |
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- en |
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base_model: |
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- allenai/scibert_scivocab_uncased |
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- openai/clip-vit-base-patch32 |
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pipeline_tag: feature-extraction |
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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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# SkLIP |
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SkLIP (Skin CLIP) is a hybrid CLIP model finetuned on the [SkinCAP](https://hf.rst.im/datasets/joshuachou/SkinCAP), a multi-modal dermatology dataset annotated |
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with rich medical captions. It is built witha SciBERT text encoder and the pre-trained CLIP-32 vision encoder. |
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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: 1e-05 |
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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 |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 4.2018 | 1.0 | 57 | 4.1344 | |
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| 4.1697 | 2.0 | 114 | 4.1298 | |
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| 4.1668 | 3.0 | 171 | 4.1276 | |
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| 4.164 | 4.0 | 228 | 4.1263 | |
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| 4.158 | 5.0 | 285 | 4.1253 | |
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| 4.1583 | 6.0 | 342 | 4.1246 | |
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| 4.1569 | 7.0 | 399 | 4.1243 | |
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| 4.1575 | 8.0 | 456 | 4.1241 | |
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| 4.1564 | 9.0 | 513 | 4.1240 | |
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| 4.1604 | 10.0 | 570 | 4.1240 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
