Upload folder using huggingface_hub

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- Image Regression
+datasets:
+- "tonyassi/tony__assi-ig-ds200"
+metrics:
+- accuracy
+model-index:
+- name: "tony__assi-ig-prediction200"
+  results: []
+---
+
+# tony__assi-ig-prediction200
+## IG Prediction
+
+This model was trained with [IGPrediction](https://github.com/TonyAssi/IGPrediction). It predicts how many likes an image will get.
+
+```python
+from IGPredict import predict_ig
+predict_ig(repo_id='tonyassi/tony__assi-ig-prediction200',image_path='image.jpg')
+```
+
+---
+
+## Dataset
+Dataset: tonyassi/tony__assi-ig-ds200\
+Value Column:\
+Train Test Split: 0.2
+
+---
+
+## Training
+Base Model: [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)\
+Epochs: 20\
+Learning Rate: 0.0001
+
+---
+
+## Usage
+
+### Download
+```bash
+git clone https://github.com/TonyAssi/IGPrediction.git
+cd IGPrediction
+```
+
+### Installation
+```bash
+pip install -r requirements.txt
+```
+
+### Import 
+```python
+from IGPredict import ig_download, upload_dataset, train_ig_model, upload_ig_model, predict_ig
+```
+
+### Download Instagram Images
+- **username** Instagram username
+- **num_images** maximum number of images to download
+```python
+ig_download(username='instagarm_username', num_images=100)
+```
+Instagram images will be downloaded to *'./images'* folder, each one named like so *"index-likes.jpg"*. E.g. *"3-17.jpg"* is the third image and has 17 likes.
+
+### Upload Dataset
+- **dataset_name** name of dataset to be uploaded
+- **token** go [here](https://huggingface.co/settings/tokens) to create a new 🤗 token
+```python
+upload_dataset(dataset_name='tonyassi/tony__assi-ig-ds200', token='YOUR_HF_TOKEN')
+```
+Go to your  🤗 profile to find your uploaded dataset, it should look similar to [tonyassi/tony__assi-ig-ds](https://huggingface.co/datasets/tonyassi/tony__assi-ig-ds).
+
+
+### Train Model
+- **dataset_id** 🤗 dataset id
+- **test_split** test split of the train/test split
+- **num_train_epochs** training epochs
+- **learning_rate** learning rate
+```python
+train_ig_model(dataset_id='tonyassi/tony__assi-ig-ds200',
+               test_split=0.2,
+               num_train_epochs=20,
+               learning_rate=0.0001)
+
+```
+The trainer will save the checkpoints in the 'results' folder. 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_ig_model(model_id='tony__assi-ig-prediction200',
+             token='YOUR_HF_TOKEN',
+             checkpoint_dir='./results/checkpoint-940')
+```
+
+### Inference (Prediction)
+- **repo_id** 🤗 repo id of the model
+- **image_path** path to image
+```python
+predict_ig(repo_id='tonyassi/tony__assi-ig-prediction200',
+        image_path='image.jpg')
+```
+The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster.

metadata.json

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+    "dataset_id": "tonyassi/tony__assi-ig-ds200",
+    "value_column_name": "likes",
+    "test_split": 0.2,
+    "num_train_epochs": 20,
+    "learning_rate": 0.0001,
+    "max_value": 177
+}

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