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--- |
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license: mit |
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base_model: microsoft/git-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: git-base-on-diffuision-dataset2 |
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results: [] |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: image-to-text |
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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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# git-base-on-diffuision-dataset2 |
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This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on hieudinhpro/diffuision-dataset2 dataset. |
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## Model description |
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GIT (short for GenerativeImage2Text) model, base-sized version. \ |
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It was introduced in the paper GIT: A Generative Image-to-text Transformer for Vision and Language \ |
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Model train for task : Sketch Scene image to text |
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## How to use mdoel |
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``` |
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# Load model directly |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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processor = AutoProcessor.from_pretrained("microsoft/git-base") |
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model = AutoModelForCausalLM.from_pretrained("hieudinhpro/git-base-on-diffuision-dataset2") |
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``` |
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``` |
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# load image |
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from PIL import Image |
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image = Image.open('/content/image_3.jpg') |
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``` |
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``` |
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# pre image |
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inputs = processor(images=image, return_tensors="pt") |
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pixel_values = inputs.pixel_values |
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# predict |
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
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# decode to text |
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generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(generated_caption) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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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: 1 |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
