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--- |
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license: apache-2.0 |
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tags: |
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- image-captioning |
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languages: |
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- en |
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pipeline_tag: image-to-text |
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datasets: |
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- michelecafagna26/hl |
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language: |
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- en |
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metrics: |
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- sacrebleu |
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- rouge |
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library_name: transformers |
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--- |
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## GIT-base fine-tuned for Image Captioning on High-Level descriptions of Scenes |
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[GIT](https://arxiv.org/abs/2205.14100) base trained on the [HL dataset](https://huggingface.co/datasets/michelecafagna26/hl) for **scene generation of images** |
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## Model fine-tuning 🏋️ |
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- Trained for 10 epochs |
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- lr: 5e−5 |
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- Adam optimizer |
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- half-precision (fp16) |
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## Test set metrics 🧾 |
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| Cider | SacreBLEU | Rouge-L| |
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|--------|------------|--------| |
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| 103.00 | 24.67 | 33.90 | |
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## Model in Action 🚀 |
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```python |
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import requests |
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from PIL import Image |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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processor = AutoProcessor.from_pretrained("git-base-captioning-ft-hl-scenes") |
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model = AutoModelForCausalLM.from_pretrained("git-base-captioning-ft-hl-scenes").to("cuda") |
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img_url = 'https://datasets-server.huggingface.co/assets/michelecafagna26/hl/--/default/train/0/image/image.jpg' |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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inputs = processor(raw_image, return_tensors="pt").to("cuda") |
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pixel_values = inputs.pixel_values |
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50, |
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do_sample=True, |
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top_k=120, |
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top_p=0.9, |
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early_stopping=True, |
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num_return_sequences=1) |
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processor.batch_decode(generated_ids, skip_special_tokens=True) |
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>>> "in a beach" |
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``` |
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## BibTex and citation info |
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```BibTeX |
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@inproceedings{cafagna2023hl, |
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title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and |
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{R}ationales}, |
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author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert}, |
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booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)}, |
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address = {Prague, Czech Republic}, |
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year={2023} |
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} |
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``` |
