File size: 2,477 Bytes
5047659 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- Image Regression
datasets:
- "tonyassi/clothing-sales-ds"
metrics:
- accuracy
model-index:
- name: "sales-prediction"
results: []
---
# sales-prediction
## Image Regression Model
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.
```python
from ImageRegression import predict
predict(repo_id='tonyassi/sales-prediction',image_path='image.jpg')
```
---
## Dataset
Dataset: tonyassi/clothing-sales-ds\
Value Column: 'sales'\
Train Test Split: 0.2
---
## Training
Base Model: [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)\
Epochs: 10\
Learning Rate: 0.0001
---
## Usage
### Download
```bash
git clone https://github.com/TonyAssi/ImageRegression.git
cd ImageRegression
```
### Installation
```bash
pip install -r requirements.txt
```
### Import
```python
from ImageRegression import train_model, upload_model, predict
```
### Inference (Prediction)
- **repo_id** 🤗 repo id of the model
- **image_path** path to image
```python
predict(repo_id='tonyassi/sales-prediction',
image_path='image.jpg')
```
The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster.
### Train Model
- **dataset_id** 🤗 dataset id
- **value_column_name** column name of prediction values in dataset
- **test_split** test split of the train/test split
- **output_dir** the directory where the checkpoints will be saved
- **num_train_epochs** training epochs
- **learning_rate** learning rate
```python
train_model(dataset_id='tonyassi/clothing-sales-ds',
value_column_name='sales',
test_split=0.2,
output_dir='./results',
num_train_epochs=10,
learning_rate=0.0001)
```
The trainer will save the checkpoints in the output_dir location. The model.safetensors are the trained weights you'll use for inference (predicton).
### Upload Model
This function will upload your model to the 🤗 Hub.
- **model_id** the name of the model id
- **token** go [here](https://huggingface.co/settings/tokens) to create a new 🤗 token
- **checkpoint_dir** checkpoint folder that will be uploaded
```python
upload_model(model_id='sales-prediction',
token='YOUR_HF_TOKEN',
checkpoint_dir='./results/checkpoint-940')
``` |