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--- |
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base_model: openai/clip-vit-large-patch14 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: clip-vit-l-14-pmc-finetuned |
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results: [] |
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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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# clip-vit-l-14-pmc-finetuned |
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This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on an **pmc_oa** (https://huggingface.co/datasets/axiong/pmc_oa) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0125 |
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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: 8 |
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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: 10.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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### finetune this model use the script from *run_clip.py* (https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text) |
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```shell |
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python -W ignore run_clip.py --model_name_or_path openai/clip-vit-large-patch14 \ |
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--output_dir ./clip-vit-l-14-pmc-finetuned \ |
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--train_file data/pmc_roco_train.csv \ |
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--validation_file data/pmc_roco_valid.csv \ |
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--image_column image --caption_column caption \ |
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--max_seq_length 77 \ |
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--do_train --do_eval \ |
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--per_device_train_batch_size 16 --per_device_eval_batch_size 8 \ |
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--remove_unused_columns=False \ |
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--learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \ |
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--overwrite_output_dir \ |
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--num_train_epochs 10 \ |
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--logging_dir ./pmc_vit_logs \ |
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--save_total_limit 2 \ |
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--report_to tensorboard |
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``` |
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### usage |
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```python |
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from PIL import Image |
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import requests |
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from transformers import CLIPProcessor, CLIPModel |
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model = CLIPModel.from_pretrained("ryanyip7777/pmc_vit-l-14_hf") |
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processor = CLIPProcessor.from_pretrained("ryanyip7777/pmc_vit-l-14_hf") |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) |
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outputs = model(**inputs) |
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score |
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities |
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``` |
