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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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tags:
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- Image Regression
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datasets:
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- "BrownEnergy/secchi_depth"
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metrics:
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- accuracy
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model-index:
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- name: "sd_depth_regression"
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results: []
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---
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# sd_depth_regression
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## Image Regression Model
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This model was trained with [Image Regression Model Trainer](https://github.com/TonyAssi/ImageRegression/tree/main). It takes an image as input and outputs a float value.
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```python
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from ImageRegression import predict
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predict(repo_id='BrownEnergy/sd_depth_regression',image_path='image.jpg')
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```
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---
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## Dataset
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Dataset: BrownEnergy/secchi_depth\
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Value Column: 'sd_depth'\
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Train Test Split: 0.2
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---
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## Training
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Base Model: [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)\
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Epochs: 10\
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Learning Rate: 0.0001
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---
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## Usage
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### Download
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```bash
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git clone https://github.com/TonyAssi/ImageRegression.git
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cd ImageRegression
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```
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### Installation
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```bash
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pip install -r requirements.txt
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```
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### Import
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```python
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from ImageRegression import train_model, upload_model, predict
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```
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### Inference (Prediction)
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- **repo_id** 🤗 repo id of the model
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- **image_path** path to image
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```python
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predict(repo_id='BrownEnergy/sd_depth_regression',
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image_path='image.jpg')
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```
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The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster.
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### Train Model
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- **dataset_id** 🤗 dataset id
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- **value_column_name** column name of prediction values in dataset
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- **test_split** test split of the train/test split
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- **output_dir** the directory where the checkpoints will be saved
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- **num_train_epochs** training epochs
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- **learning_rate** learning rate
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```python
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train_model(dataset_id='BrownEnergy/secchi_depth',
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value_column_name='sd_depth',
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test_split=0.2,
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output_dir='./results',
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num_train_epochs=10,
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learning_rate=0.0001)
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```
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The trainer will save the checkpoints in the output_dir location. The model.safetensors are the trained weights you'll use for inference (predicton).
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### Upload Model
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This function will upload your model to the 🤗 Hub.
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- **model_id** the name of the model id
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- **token** go [here](https://huggingface.co/settings/tokens) to create a new 🤗 token
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- **checkpoint_dir** checkpoint folder that will be uploaded
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```python
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upload_model(model_id='sd_depth_regression',
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token='YOUR_HF_TOKEN',
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checkpoint_dir='./results/checkpoint-940')
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``` |
