661 lines
1.1 MiB
Plaintext
661 lines
1.1 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bear Classifier\n",
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"\n",
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"Grizzly, Black, or Teddy Bear?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastai.data.all import *\n",
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"from fastai.vision.all import *\n",
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"from fastcore.all import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Image Download"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from duckduckgo_search import DDGS\n",
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"\n",
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"def search_images(search_term, max_images = 100):\n",
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" return L(DDGS().images(search_term, max_results = max_images)).itemgot('image')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"\n",
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"<style>\n",
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" /* Turns off some styling */\n",
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" progress {\n",
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" /* gets rid of default border in Firefox and Opera. */\n",
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" border: none;\n",
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" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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" background-size: auto;\n",
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" }\n",
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" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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" }\n",
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" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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" background: #F44336;\n",
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" }\n",
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"</style>\n"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <div>\n",
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" <progress value='335872' class='' max='332689' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" 100.96% [335872/332689 00:00<00:00]\n",
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" </div>\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/jpeg": "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"image/png": "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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=256x171>"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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||
|
|
"source": [
|
||
|
|
"from fastdownload import download_url\n",
|
||
|
|
"\n",
|
||
|
|
"image_urls = search_images('grizzly', max_images = 1)\n",
|
||
|
|
"\n",
|
||
|
|
"destination = 'bears/grizzly.jpg'\n",
|
||
|
|
"download_url(image_urls[0], destination)\n",
|
||
|
|
"\n",
|
||
|
|
"image = Image.open(destination)\n",
|
||
|
|
"image.to_thumb(256, 256)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from pathlib import Path\n",
|
||
|
|
"data_set_path = Path('bears')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from fastai.vision.utils import download_images\n",
|
||
|
|
"\n",
|
||
|
|
"bear_types = 'grizzly', 'black', 'teddy'\n",
|
||
|
|
"\n",
|
||
|
|
"for bear_type in bear_types:\n",
|
||
|
|
" destination = data_set_path/bear_type\n",
|
||
|
|
" destination.mkdir(exist_ok = True)\n",
|
||
|
|
" search_results = search_images(f'{bear_type} bear', max_images = 100)\n",
|
||
|
|
" download_images(destination, urls = search_results)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Verify Images"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"12"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from fastai.vision.utils import get_image_files, verify_images\n",
|
||
|
|
"\n",
|
||
|
|
"failed_images = verify_images(get_image_files(data_set_path))\n",
|
||
|
|
"failed_images.map(Path.unlink)\n",
|
||
|
|
"len(failed_images)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Data Sets"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"bears_data = DataBlock(\n",
|
||
|
|
" blocks = (ImageBlock, CategoryBlock),\n",
|
||
|
|
" get_items = get_image_files,\n",
|
||
|
|
" splitter = RandomSplitter(valid_pct = 0.2, seed = 42),\n",
|
||
|
|
" get_y = parent_label,\n",
|
||
|
|
" item_tfms = Resize(128),\n",
|
||
|
|
" batch_tfms = aug_transforms(mult = 2)\n",
|
||
|
|
").dataloaders(data_set_path)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x600 with 8 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"bears_data.train.show_batch(max_n = 8, nrows = 2, unique = True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Train Model"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"\n",
|
||
|
|
"<style>\n",
|
||
|
|
" /* Turns off some styling */\n",
|
||
|
|
" progress {\n",
|
||
|
|
" /* gets rid of default border in Firefox and Opera. */\n",
|
||
|
|
" border: none;\n",
|
||
|
|
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
||
|
|
" background-size: auto;\n",
|
||
|
|
" }\n",
|
||
|
|
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
||
|
|
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
||
|
|
" }\n",
|
||
|
|
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
||
|
|
" background: #F44336;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: left;\">\n",
|
||
|
|
" <th>epoch</th>\n",
|
||
|
|
" <th>train_loss</th>\n",
|
||
|
|
" <th>valid_loss</th>\n",
|
||
|
|
" <th>error_rate</th>\n",
|
||
|
|
" <th>time</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" <td>1.775698</td>\n",
|
||
|
|
" <td>0.452870</td>\n",
|
||
|
|
" <td>0.185185</td>\n",
|
||
|
|
" <td>00:11</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"\n",
|
||
|
|
"<style>\n",
|
||
|
|
" /* Turns off some styling */\n",
|
||
|
|
" progress {\n",
|
||
|
|
" /* gets rid of default border in Firefox and Opera. */\n",
|
||
|
|
" border: none;\n",
|
||
|
|
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
||
|
|
" background-size: auto;\n",
|
||
|
|
" }\n",
|
||
|
|
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
||
|
|
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
||
|
|
" }\n",
|
||
|
|
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
||
|
|
" background: #F44336;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: left;\">\n",
|
||
|
|
" <th>epoch</th>\n",
|
||
|
|
" <th>train_loss</th>\n",
|
||
|
|
" <th>valid_loss</th>\n",
|
||
|
|
" <th>error_rate</th>\n",
|
||
|
|
" <th>time</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" <td>0.647827</td>\n",
|
||
|
|
" <td>0.233713</td>\n",
|
||
|
|
" <td>0.111111</td>\n",
|
||
|
|
" <td>00:10</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>0.504331</td>\n",
|
||
|
|
" <td>0.249407</td>\n",
|
||
|
|
" <td>0.055556</td>\n",
|
||
|
|
" <td>00:10</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" <td>0.387142</td>\n",
|
||
|
|
" <td>0.229750</td>\n",
|
||
|
|
" <td>0.055556</td>\n",
|
||
|
|
" <td>00:10</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" <td>0.331931</td>\n",
|
||
|
|
" <td>0.204678</td>\n",
|
||
|
|
" <td>0.055556</td>\n",
|
||
|
|
" <td>00:10</td>\n",
|
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"source": [
|
||
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"model = vision_learner(bears_data, resnet18, metrics = error_rate)\n",
|
||
|
|
"model.fine_tune(4)"
|
||
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]
|
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|
|
"source": [
|
||
|
|
"interpretation = ClassificationInterpretation.from_learner(model)\n",
|
||
|
|
"interpretation.plot_confusion_matrix()"
|
||
|
|
]
|
||
|
|
},
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{
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x300 with 5 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"interpretation.plot_top_losses(5, nrows = 1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Clean Data Set\n",
|
||
|
|
"\n",
|
||
|
|
"**NOTE:** This currently doesn't work in Visual Studio Code.\n",
|
||
|
|
"\n",
|
||
|
|
"**TODO:** Jupyter Notebook instead?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"\n",
|
||
|
|
"<style>\n",
|
||
|
|
" /* Turns off some styling */\n",
|
||
|
|
" progress {\n",
|
||
|
|
" /* gets rid of default border in Firefox and Opera. */\n",
|
||
|
|
" border: none;\n",
|
||
|
|
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
||
|
|
" background-size: auto;\n",
|
||
|
|
" }\n",
|
||
|
|
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
||
|
|
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
||
|
|
" }\n",
|
||
|
|
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
||
|
|
" background: #F44336;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"\n",
|
||
|
|
"<style>\n",
|
||
|
|
" /* Turns off some styling */\n",
|
||
|
|
" progress {\n",
|
||
|
|
" /* gets rid of default border in Firefox and Opera. */\n",
|
||
|
|
" border: none;\n",
|
||
|
|
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
||
|
|
" background-size: auto;\n",
|
||
|
|
" }\n",
|
||
|
|
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
||
|
|
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
||
|
|
" }\n",
|
||
|
|
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
||
|
|
" background: #F44336;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from fastai.vision.widgets import ImageClassifierCleaner\n",
|
||
|
|
"\n",
|
||
|
|
"cleaner = ImageClassifierCleaner(model)\n",
|
||
|
|
"cleaner"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Export Model"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"model.export()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.11.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|