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---
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
``` |