Instructions to use wjworld/open_clip_quilt1m_ft_cy_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use wjworld/open_clip_quilt1m_ft_cy_1 with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:wjworld/open_clip_quilt1m_ft_cy_1') tokenizer = open_clip.get_tokenizer('hf-hub:wjworld/open_clip_quilt1m_ft_cy_1') - Notebooks
- Google Colab
- Kaggle
Model card for open_clip_quilt1m_ft_cy_1
This model is finetuned based on the Quilt-1M VIT-B-32 model using Chaoyang Dataset.
The training csv file is : /dataset/chaoyang/chaoyang_train_multi_annos.csv
For this model, I insert the multi-labels into the prompts. Since the "normal" and "serrated" are the adj, so I add the nouns for better expression.
The Paired Text used for training as listed below:
normal: "normal histology" serrated: "serrated polyps" adenocarcinomas: "adenocarcinomas" adenomas: "adenomas"
So for different combinations, replacing the labels with corresponding words.
"normal histology, normal histology, normal histology"
"serrated polyps, serrated polyps, serrated polyps"
"adenocarcinomas, adenocarcinomas, adenocarcinomas"
"adenomas, adenomas, adenomas"
"adenomas, normal histology, adenomas"
The model is finetuned with Chaoyang Dataset with 64 epochs, but I choose the 32th checkpoint as the final model according to the plot of loss. I.e., the loss began kept stable.
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