notebook/train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import DataGenerator, read_annotation_lines\n",
    "from models import Yolov4\n",
    "from config import yolo_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_lines, val_lines = read_annotation_lines('../dataset/txt/anno-test.txt', test_size=0.1)\n",
    "FOLDER_PATH = '../dataset/img'\n",
    "class_name_path = '../class_names/bccd_classes.txt'\n",
    "data_gen_train = DataGenerator(train_lines, class_name_path, FOLDER_PATH)\n",
    "data_gen_val = DataGenerator(val_lines, class_name_path, FOLDER_PATH)\n",
    "\n",
    "model = Yolov4(weight_path=None, \n",
    "               class_name_path=class_name_path)\n",
    "\n",
    "model.fit(data_gen_train, \n",
    "          initial_epoch=0,\n",
    "          epochs=10000, \n",
    "          val_data_gen=data_gen_val,\n",
    "          callbacks=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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