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Merge pull request #99 from zc277584121/main
repair map mistake
2 parents ad0c727 + 0a2740b commit d154130

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‎video/video_deduplication/1_video_deduplication_engine.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "2a5baace",
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"id": "e06275b8",
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"metadata": {},
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"source": [
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"# How to Build a Video Deduplication System\n",
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},
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{
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"cell_type": "markdown",
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"id": "9d953a08",
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"metadata": {},
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"source": [
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"## Preparation\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "469282e9",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "e4f5a71c",
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"metadata": {},
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"source": [
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"### Prepare the data\n",
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5023c1df",
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"id": "22543226",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "091912bc",
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"metadata": {
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"scrolled": true
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},
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{
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"cell_type": "markdown",
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"id": "ef4670d6",
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"metadata": {},
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"Define some helper function to convert video to gif so that we can have a look at these videos. "
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3ea961d6",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "97440442",
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"metadata": {},
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"source": [
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"Positive denotes a video that is contain same content event in anchor video, while negative denotes an inconsistent."
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "88519061",
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"metadata": {
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"scrolled": true
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},
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"cell_type": "markdown",
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"id": "5c707302",
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"metadata": {},
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"source": [
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"### Create a Milvus Collection\n",
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "208ab462",
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"id": "df83801e",
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"metadata": {},
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"source": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e9c9e6d8",
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"id": "ecd908ad",
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{
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"cell_type": "markdown",
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"id": "7acaf86b",
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"metadata": {},
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"source": [
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"## Video Copy Detection\n",
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},
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{
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"cell_type": "markdown",
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"id": "5fdcd9b8",
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"metadata": {},
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"source": [
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"### Load Video Embeddings into Milvus\n",
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "9f36c5df",
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"id": "1dc07d2d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 26min 27s, sys: 15.9 s, total: 26min 42s\n",
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"Wall time: 1min 56s\n"
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"CPU times: user 26min 10s, sys: 14.1 s, total: 26min 24s\n",
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"Wall time: 1min 55s\n"
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},
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{
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"cell_type": "markdown",
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"id": "e6c9fe9a",
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"id": "f96e5c4b",
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"metadata": {},
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"Here is detailed explanation for each line of the code:\n",
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "5102998a",
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"id": "2738a2b3",
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"metadata": {
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"scrolled": true
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "441801e0",
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"id": "97e3f3f7",
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{
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"cell_type": "markdown",
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"id": "4af2a4e3",
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"metadata": {},
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"source": [
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"## Evaluation\n",
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "b36f69ca",
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"id": "81b8b5ed",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 5min 20s, sys: 2.28 s, total: 5min 23s\n",
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"Wall time: 21.9 s\n"
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"CPU times: user 4min 16s, sys: 3.15 s, total: 4min 19s\n",
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"Wall time: 20.9 s\n"
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],
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "eb79042a",
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"id": "249ab799",
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "a026d731",
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{
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>recall_at_k</th>\n",
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" <th>map_at_k</th>\n",
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" <td>0.973977</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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"text/plain": [
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" mean_average_precision\n",
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"recall_at_k 0.973977"
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" mean_average_precision\n",
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"map_at_k 0.973977"
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]
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"metadata": {},
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"benchmark = (\n",
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" dc.with_metrics(['mean_average_precision',]) \\\n",
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" .evaluate['ground_truth_event', 'topk_events'](name='recall_at_k') \\\n",
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" .evaluate['ground_truth_event', 'topk_events'](name='map_at_k') \\\n",
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" .report()\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a2003b8d",
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"We found that we achieved an excellent topk metric on this easy small dataset, which means that if we limit each event to have k duplicate videos, then they can all be almost recalled and they are almost true positive ."
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"cell_type": "markdown",
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"metadata": {},
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"## Show query results\n",
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"id": "6d461033",
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"metadata": {
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"scrolled": true
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