README.md

---
tags:
- computer_vision
- vision_models_playground
- custom-implementation
---
# **Vision Models Playground**
This is a trained model from the Vision Models Playground repository.
Link to the repository: https://github.com/Akrielz/vision_models_playground

## **Model**
This model is a custom implementation of **ResNetYoloV1** from the ```vision_models_playground.models.segmentation.yolo_v1``` module.
Please look in the config file for more information about the model architecture.

## **Usage**
To load the torch model, you can use the following code snippet:

```python
import torch
from vision_models_playground.utility.hub import load_vmp_model_from_hub


model = load_vmp_model_from_hub("Akriel/ResNetYoloV1")

x = torch.randn(...)
y = model(x)  # y will be of type torch.Tensor
```

To load the pipeline that includes the model, you can use the following code snippet:

```python
from vision_models_playground.utility.hub import load_vmp_pipeline_from_hub

pipeline = load_vmp_pipeline_from_hub("Akriel/ResNetYoloV1")

x = raw_data  # raw_data will be of type pipeline.input_type
y = pipeline(x)  # y will be of type pipeline.output_type
```

## **Metrics**

The model was evaluated on the following dataset: **YoloPascalVocDataset** from ```vision_models_playground.datasets.yolo_pascal_voc_dataset```  

These are the results of the evaluation:  
- MulticlassAccuracy: 0.7241  
- MulticlassAveragePrecision: 0.7643  
- MulticlassAUROC: 0.9684  
- Dice: 0.7241  
- MulticlassF1Score: 0.7241  
- LossTracker: 4.1958  


## **Additional Information**
The train and evaluation runs are also saved using tensorboard. You can use the following command to visualize the runs:

```bash
tensorboard --logdir ./model
```

```bash
tensorboard --logdir ./eval
```