{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# core\n", "\n", "> Fill in a module description here" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| default_exp core" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| hide\n", "from nbdev.showdoc import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| export\n", "from fastai.vision.all import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(#9) [Path('/home/chris/.fastai/data/pascal_2007/segmentation'),Path('/home/chris/.fastai/data/pascal_2007/test.json'),Path('/home/chris/.fastai/data/pascal_2007/train.json'),Path('/home/chris/.fastai/data/pascal_2007/valid.json'),Path('/home/chris/.fastai/data/pascal_2007/test.csv'),Path('/home/chris/.fastai/data/pascal_2007/models'),Path('/home/chris/.fastai/data/pascal_2007/test'),Path('/home/chris/.fastai/data/pascal_2007/train.csv'),Path('/home/chris/.fastai/data/pascal_2007/train')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.PASCAL_2007)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | fname | \n", "labels | \n", "is_valid | \n", "
---|---|---|---|
5006 | \n", "009954.jpg | \n", "horse person | \n", "True | \n", "
5007 | \n", "009955.jpg | \n", "boat | \n", "True | \n", "
5008 | \n", "009958.jpg | \n", "person bicycle | \n", "True | \n", "
5009 | \n", "009959.jpg | \n", "car | \n", "False | \n", "
5010 | \n", "009961.jpg | \n", "dog | \n", "False | \n", "
epoch | \n", "train_loss | \n", "valid_loss | \n", "accuracy_multi | \n", "F1(macro) | \n", "F1(samples) | \n", "time | \n", "
---|---|---|---|---|---|---|
0 | \n", "0.151220 | \n", "0.146803 | \n", "0.949363 | \n", "0.534384 | \n", "0.586577 | \n", "25:18 | \n", "
epoch | \n", "train_loss | \n", "valid_loss | \n", "accuracy_multi | \n", "F1(macro) | \n", "F1(samples) | \n", "time | \n", "
---|---|---|---|---|---|---|
0 | \n", "0.182089 | \n", "0.182682 | \n", "0.947689 | \n", "0.424478 | \n", "0.486590 | \n", "32:25 | \n", "
MultiCategory(items=None, *rest, use_list=False, match=None)
Behaves like a list of `items` but can also index with list of indices or masks
\n", "" ], "text/plain": [ "nbdev_export(path:str=None, symlinks:bool=False, file_glob:str='*.ipynb', file_re:str=None, folder_re:str=None, skip_file_glob:str=None, skip_file_re:str='^[_.]', skip_folder_re:str='^[_.]', recursive:bool=True)
Export notebooks in `path` to Python modules
\n", "" ], "text/plain": [ "