yolo object detection

examples/Yolo-object-detection/-metadata

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+{"num_examples": 1841, "batch_size": 64, "epochs": 1, "lr": 0.00261}

examples/Yolo-object-detection/Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d545b1ea-9c0f-486f-aeac-9c566d3ca6bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[    0     2     5 38400     0]\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "darkfile1 = \"/home/sowmya/yolo_computer/darknet/yolov4_tiny/yolov4-tiny_final.weights\"  # Pretrained weights file\n",
+    "fp = open(darkfile1, \"rb\")\n",
+    "ww = np.fromfile(fp,dtype=np.int32)\n",
+    "print(ww[:5])\n",
+    "header=np.fromfile(fp,dtype=np.int32,count=5)\n",
+    "print(header)\n",
+    "w1 = np.fromfile(fp, dtype=np.float32)\n",
+    "fp.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bbd79c02-ad6b-4dcd-9bf6-ff8af25f3720",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "darkfile2 = \"/home/sowmya/yolo_computer/darknet/yolov4_tiny/yolov4-tiny_final2.weights\"  # Pretrained weights file\n",
+    "fp = open(darkfile2, \"rb\")\n",
+    "header=np.fromfile(fp,dtype=np.int32,count=5)\n",
+    "print(header)\n",
+    "w2 = np.fromfile(fp, dtype=np.float32)\n",
+    "fp.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "18c12401-a625-433d-a8a1-fed5da898e47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(w1-w2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "82e07c0a-651c-4195-8f62-9e2b2bc0ce85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.00705129,  0.01110012, -0.01678175, -0.00561042], dtype=float32)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w2[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "16badc7a-7ceb-4972-b04b-5c82ab5a295c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.00705129,  0.01110012, -0.01678175, -0.00561042], dtype=float32)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w1[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fecca75c-07ed-4bdc-903d-f61e66daddee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mattias kernel",
+   "language": "python",
+   "name": "mattiaskernel"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/Yolo-object-detection/client/darknet/.circleci/config.yml

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+version: 2.0
+jobs:
+  build:
+    docker:
+       - image: datamachines/cudnn_tensorflow_opencv:11.2.0_2.4.1_4.5.1-20210211
+#      - image: alexeyab84/dockerfiles:latest
+#      - image: alantrrs/cuda-opencv:latest
+#      - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+    working_directory: ~/work
+    steps:
+      - checkout
+      - run: nvcc --version
+      - run: gcc --version
+      - run: export PATH=$PATH:/usr/local/include/opencv4/
+      - run: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/:/usr/lib/:/usr/lib64/
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=0 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8
+      - run: make clean
+      - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8

examples/Yolo-object-detection/client/darknet/.github/FUNDING.yml

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+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: ['https://paypal.me/alexeyab84', 'https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp', 'https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#', 'https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

examples/Yolo-object-detection/client/darknet/.github/ISSUE_TEMPLATE/any-other-question-or-issue.md

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+---
+name: Any other question or issue
+about: Any other question or issue
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+If something doesn’t work for you, then show 2 screenshots:
+1. screenshots of your issue
+2. screenshots with such information
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF
+```
+
+If you do not get an answer for a long time, try to find the answer among Issues with a Solved label: https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3ASolved

examples/Yolo-object-detection/client/darknet/.github/ISSUE_TEMPLATE/bug-report.md

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+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+If you want to report a bug - provide:
+    * description of a bug
+    * what command do you use?
+    * do you use Win/Linux/Mac?
+    * attach screenshot of a bug with previous messages in terminal
+    * in what cases a bug occurs, and in which not?
+    * if possible, specify date/commit of Darknet that works without this bug
+    * show such screenshot with info
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+```

examples/Yolo-object-detection/client/darknet/.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: Feature-request
+assignees: ''
+
+---
+
+For Feature-request:
+    * describe your feature as detailed as possible
+    * provide link to the paper and/or source code if it exist
+    * attach chart/table with comparison that shows improvement

examples/Yolo-object-detection/client/darknet/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md

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+---
+name: Training issue - no-detections / Nan avg-loss / low accuracy
+about: Training issue - no-detections / Nan avg-loss / low accuracy
+title: ''
+labels: Training issue
+assignees: ''
+
+---
+
+If you have an issue with training - no-detections / Nan avg-loss / low accuracy:
+    * read FAQ: https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions
+    * what command do you use?
+    * what dataset do you use?   
+    * what Loss and mAP did you get?
+    * show chart.png with Loss and mAP    
+    * check your dataset - run training with flag `-show_imgs` i.e. `./darknet detector train ... -show_imgs` and look at the `aug_...jpg` images, do you see correct truth bounded boxes?
+    * rename your cfg-file to txt-file and drag-n-drop (attach) to your message here
+    * show content of generated files `bad.list` and `bad_label.list` if they exist
+    * Read `How to train (to detect your custom objects)` and `How to improve object detection` in the Readme: https://github.com/AlexeyAB/darknet/blob/master/README.md
+    * show such screenshot with info
+```
+./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
+ CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
+ CUDNN_HALF=1
+ OpenCV version: 4.2.0
+ 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
+net.optimized_memory = 0
+mini_batch = 1, batch = 8, time_steps = 1, train = 0
+   layer   filters  size/strd(dil)      input                output
+```