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---
license: apache-2.0
language:
- ru
metrics:
- bleu
pipeline_tag: image-to-text
widget:
- src: https://huggingface.co/dumperize/movie-picture-captioning/resolve/main/vertical_15x.jpeg
example_title: Custom Image Sample 1
---
# 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)
# How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("dumperize/movie-picture-captioning")
feature_extractor = ViTFeatureExtractor.from_pretrained("dumperize/movie-picture-captioning")
model = VisionEncoderDecoderModel.from_pretrained("dumperize/movie-picture-captioning")
max_length = 128
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
image_path = 'path/to/image.jpg';
image = Image.open(image_path)
image = image.resize([224,224])
if image.mode != "RGB":
image = image.convert(mode="RGB")
pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print([pred.strip() for pred in preds])
```
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
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[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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## Results
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### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
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### Hardware
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### Software
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# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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# Glossary [optional]
<!-- 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 [optional]
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# Model Card Authors [optional]
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# Model Card Contact
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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>
[More Information Needed]
</details> |