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Merge pull request #933 from Jaykumaran/master
cvpr2024-part-2
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‎README.md‎

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@@ -15,6 +15,7 @@ Want to become an expert in AI? [AI Courses by OpenCV](https://opencv.org/course
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| Blog Post | Code|
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| ------------- |:-------------|
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| [CVPR 2024 Research Papers - Part- 2](https://learnopencv.com/cvpr-2024-research-papers) | [Code](https://github.com/spmallick/learnopencv/tree/master/cvpr-2024-research-papers-part2) |
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| [CVPR 2024: An Overview and Key Papers](https://learnopencv.com/cvpr2024/) | [Code](https://github.com/spmallick/learnopencv/tree/master/CVPR-2024) |
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| [Object Detection on Edge Device - OAK-D-Lite](https://learnopencv.com/object-detection-on-edge-device) | [Code](https://github.com/spmallick/learnopencv/tree/master/Object-Detection-on-Edge-Devices) |
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| [Fine-Tuning YOLOv10 Models on Custom Dataset](https://learnopencv.com/fine-tuning-yolov10/) | [Code](https://github.com/spmallick/learnopencv/tree/master/Fine-Tuning-YOLOv10-Models-Custom-Dataset) |

‎cvpr-2024-research-papers-part2/Docres/DocRes.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "_OcPAA8FUqJ8",
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"outputId": "30b4dc55-9365-4336-ea3a-5ec100542a29"
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},
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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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"Cloning into 'DocRes'...\n",
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"remote: Enumerating objects: 243, done.\u001b[K\n",
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"remote: Counting objects: 100% (30/30), done.\u001b[K\n",
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"remote: Compressing objects: 100% (24/24), done.\u001b[K\n",
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"remote: Total 243 (delta 11), reused 15 (delta 6), pack-reused 213\u001b[K\n",
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"Receiving objects: 100% (243/243), 223.36 MiB | 2.90 MiB/s, done.\n",
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"Resolving deltas: 100% (22/22), done.\n",
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"Updating files: 100% (137/137), done.\n"
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]
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}
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],
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"source": [
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"!git clone https://github.com/ZZZHANG-jx/DocRes.git"
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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": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "mbmRFo-mUu3y",
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"outputId": "94bfcce6-4d41-45f6-8864-38b1c42e65b2"
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},
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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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"/home/jaykumaran/Blogs/CVPR 2024/DocRes/DocRes\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/jaykumaran/.local/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n",
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" self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
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]
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}
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],
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"source": [
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"%cd DocRes"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "jM6ZYPA6VCNn",
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"outputId": "dc4e9ca9-48d4-4942-fc2d-4c0903261be9"
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},
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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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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 GB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.3/22.3 MB\u001b[0m \u001b[31m50.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.9/15.9 MB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m101.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m103.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.21.6 which is incompatible.\n",
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"cudf-cu12 24.4.1 requires numpy<2.0a0,>=1.23, but you have numpy 1.21.6 which is incompatible.\n",
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"flax 0.8.4 requires numpy>=1.22, but you have numpy 1.21.6 which is incompatible.\n",
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"jax 0.4.26 requires numpy>=1.22, but you have numpy 1.21.6 which is incompatible.\n",
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"jaxlib 0.4.26+cuda12.cudnn89 requires numpy>=1.22, but you have numpy 1.21.6 which is incompatible.\n",
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"numba 0.58.1 requires numpy<1.27,>=1.22, but you have numpy 1.21.6 which is incompatible.\n",
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"numexpr 2.10.1 requires numpy>=1.23.0, but you have numpy 1.21.6 which is incompatible.\n",
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"pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.21.6 which is incompatible.\n",
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"plotnine 0.12.4 requires numpy>=1.23.0, but you have numpy 1.21.6 which is incompatible.\n",
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"rmm-cu12 24.4.0 requires numpy<2.0a0,>=1.23, but you have numpy 1.21.6 which is incompatible.\n",
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"statsmodels 0.14.2 requires numpy>=1.22.3, but you have numpy 1.21.6 which is incompatible.\n",
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"tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.21.6 which is incompatible.\n",
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"torchaudio 2.3.0+cu121 requires torch==2.3.0, but you have torch 1.11.0+cu113 which is incompatible.\n",
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"torchtext 0.18.0 requires torch>=2.3.0, but you have torch 1.11.0+cu113 which is incompatible.\n",
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"xarray-einstats 0.7.0 requires numpy>=1.22, but you have numpy 1.21.6 which is incompatible.\u001b[0m\u001b[31m\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"# !pip install -r requirements.txt -q"
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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": 6,
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"metadata": {
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"id": "p_DZoipl8OIL"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.makedirs(\"data/MBD/checkpoint\", exist_ok=True)\n",
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"os.makedirs(\"checkpoints\", exist_ok=True)"
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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": 11,
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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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"\u001b[0m\u001b[01;34mcheckpoints\u001b[0m/ inference.py \u001b[01;34mmodels\u001b[0m/ \u001b[01;34mrestorted\u001b[0m/\n",
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"\u001b[01;34mdata\u001b[0m/ \u001b[01;34minput\u001b[0m/ \u001b[01;34m__pycache__\u001b[0m/ start_train.sh\n",
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"eval.py LICENSE README.md train.py\n",
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"\u001b[01;34mimages\u001b[0m/ \u001b[01;34mloaders\u001b[0m/ requirements.txt utils.py\n"
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]
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}
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],
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"source": [
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"ls"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wzrFPppj0bqF",
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"outputId": "82ac1852-9ecc-42b1-c801-9dbbe093de6a"
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},
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"outputs": [],
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"source": [
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"!wget -O checkpoints/docres.pkl https://www.dropbox.com/scl/fi/7jy1040wa6rkaj74blbla/docres.pkl?rlkey=by6wjr8en5r3wxrrx4don2911&st=4tawdvez&dl=1"
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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": 9,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "CsN8CPCd71k4",
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"outputId": "be0f48fb-2080-44c8-bc65-ea85179b1055"
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},
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"outputs": [],
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"source": [
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"!wget -O data/MBD/checkpoints/mbd.pkl https://www.dropbox.com/scl/fi/xtbxj8qn3qofuo9qcmqrt/mbd.pkl?rlkey=fa4fvzjtmwtd85s00eqdagufm&st=i1okanou&dl=1"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 35
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},
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"id": "EZzoKoq78mtj",
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"outputId": "3cba9c60-13df-4cb5-bdd4-d9bd4b054074"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'/home/jaykumaran/Blogs/CVPR 2024/DocRes/DocRes'"
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]
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},
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"execution_count": 3,
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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": [
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"pwd"
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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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"# TASK: dewarping"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Pk4ZSRMeVDK7",
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"outputId": "0b8f888b-39e4-4506-8d6d-86a25e03be20",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"!python inference.py --im_path ./input/218_in.png --task dewarping --save_dtsprompt 1"
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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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"# TASK: deshadowing"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"^C\n"
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]
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}
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],
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"source": [
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"!python inference.py --im_path ./input/218_in.png --task deshadowing --save_dtsprompt 1"
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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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"# TASK: appearance enhancement"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!python inference.py --im_path ./input/218_in.png --task appearance --save_dtsprompt 1"
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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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"# TASK: deblurring"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!python inference.py --im_path ./input/218_in.png --task deblurring --save_dtsprompt d"
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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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"# TASK: binarization"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!python inference.py --im_path ./input/218_in.png --task deblurring --save_dtsprompt d"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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