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dumperize
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movie-picture-captioning

Image-to-Text
Transformers
PyTorch
Russian
vision-encoder-decoder
image-text-to-text
Inference Endpoints
Model card Files Community
1
movie-picture-captioning
File size: 4,501 Bytes 
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---
license: apache-2.0
language:
- ru
metrics:
- bleu
pipeline_tag: image-to-text
---
# Model Card for Model ID
This model generate a description for movie posters ... mm, in principle, for any photo.

# Model Details

## Model Description

This is an encoder decoder model based on [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder). 
[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. 

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 =).

## Model Sources

- **Repository:** [github.com/slivka83](https://github.com/slivka83/)
- **Demo [optional]:** [@MPC_project_bot](https://t.me/MPC_project_bot)

# Uses

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## Direct Use

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[More Information Needed]

## Out-of-Scope Use

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# Bias, Risks, and Limitations

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## Recommendations

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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.

# Training Details

## Training Data

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.

### Preprocessing

The model was trained on 8 16 GB V100 for 90 hours. 

# Evaluation

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## Testing Data, Factors & Metrics

### Testing Data

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### Factors

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## Results

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### Summary



# Model Examination [optional]

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# Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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# Technical Specifications [optional]

## Model Architecture and Objective

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## Compute Infrastructure

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### Hardware

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### Software

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# Citation [optional]

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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

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</details>