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--- |
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license: apache-2.0 |
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language: |
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- ru |
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metrics: |
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- bleu |
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pipeline_tag: image-to-text |
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widget: |
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- src: https://huggingface.co/dumperize/movie-picture-captioning/resolve/main/vertical_15x.jpeg |
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example_title: Custom Image Sample 1 |
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--- |
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# Model Card for Model ID |
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This model generate a description for movie posters ... mm, in principle, for any photo. |
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# Model Details |
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## Model Description |
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This is an encoder decoder model based on [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder). |
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[Google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) was used as encoder, [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) as decoder. |
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We refined the model on the dataset with descriptions and movie posters by russian app Kinoposk. Now the model generates descriptions on the jargon of blockbusters =). |
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## Model Sources |
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- **Repository:** [github.com/slivka83](https://github.com/slivka83/) |
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- **Demo [optional]:** [@MPC_project_bot](https://t.me/MPC_project_bot) |
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# How to use |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("dumperize/movie-picture-captioning") |
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feature_extractor = ViTFeatureExtractor.from_pretrained("dumperize/movie-picture-captioning") |
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model = VisionEncoderDecoderModel.from_pretrained("dumperize/movie-picture-captioning") |
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max_length = 128 |
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num_beams = 4 |
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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image_path = 'path/to/image.jpg'; |
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image = Image.open(image_path) |
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image = image.resize([224,224]) |
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if image.mode != "RGB": |
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image = image.convert(mode="RGB") |
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pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values |
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pixel_values = pixel_values.to(device) |
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output_ids = model.generate(pixel_values, **gen_kwargs) |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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print([pred.strip() for pred in preds]) |
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``` |
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# Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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## Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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We compiled a dataset from the open source of all Russian-language films for October 2022 - [kinopoisk](https://www.kinopoisk.ru/). Films with very short or very long descriptions were not included in the dataset, films with blank or very small images were excluded too. |
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### Preprocessing |
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The model was trained on 8 16 GB V100 for 90 hours. |
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# Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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[More Information Needed] |
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### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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## Results |
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[More Information Needed] |
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### Summary |
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# Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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# Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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[More Information Needed] |
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## Compute Infrastructure |
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[More Information Needed] |
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### Hardware |
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[More Information Needed] |
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### Software |
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[More Information Needed] |
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# Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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# Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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# More Information [optional] |
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[More Information Needed] |
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# Model Card Authors [optional] |
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[More Information Needed] |
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# Model Card Contact |
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[More Information Needed] |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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[More Information Needed] |
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</details> |
