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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile config.yml\n",
"task: image_classification # do not change\n",
"base_model: google/vit-base-patch16-224 # the model to be used from hugging face hub\n",
"project_name: autotrain-image-classification-model # the name of the project, must be unique\n",
"log: tensorboard # do not change\n",
"backend: local # do not change\n",
"\n",
"data:\n",
" path: data/ # the path to the data folder, this folder consists of `train` and `valid` (if any) folders\n",
" train_split: train # this folder inside data/ will be used for training, it contains the images in subfolders.\n",
" valid_split: null # this folder inside data/ will be used for validation, it contains the images in subfolders. If not available, set it to null\n",
" column_mapping: # do not change\n",
" image_column: image\n",
" target_column: labels\n",
"\n",
"params:\n",
" epochs: 2\n",
" batch_size: 4\n",
" lr: 2e-5\n",
" optimizer: adamw_torch\n",
" scheduler: linear\n",
" gradient_accumulation: 1\n",
" mixed_precision: fp16\n",
"\n",
"hub:\n",
" username: ${HF_USERNAME} # please set HF_USERNAME in colab secrets\n",
" token: ${HF_TOKEN} # please set HF_TOKEN in colab secrets, must be valid hugging face write token\n",
" push_to_hub: true # set to true if you want to push the model to the hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from google.colab import userdata\n",
"HF_USERNAME = userdata.get('HF_USERNAME')\n",
"HF_TOKEN = userdata.get('HF_TOKEN')\n",
"os.environ['HF_USERNAME'] = HF_USERNAME\n",
"\n",
"os.environ['HF_TOKEN'] = HF_TOKEN\n",
"!autotrain --config config.yml"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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