diff --git "a/colab.ipynb" "b/colab.ipynb"
new file mode 100644--- /dev/null
+++ "b/colab.ipynb"
@@ -0,0 +1,6160 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8IoA9kz36w3N",
+ "outputId": "0f278eb1-9149-4209-87ab-21420c5bd78c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Fri Sep 30 06:40:35 2022 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
+ "| N/A 39C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |\n",
+ "| | | N/A |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "| No running processes found |\n",
+ "+-----------------------------------------------------------------------------+\n",
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
+ "Requirement already satisfied: torch==1.8.1+cu111 in /usr/local/lib/python3.7/dist-packages (1.8.1+cu111)\n",
+ "Requirement already satisfied: torchvision==0.9.1+cu111 in /usr/local/lib/python3.7/dist-packages (0.9.1+cu111)\n",
+ "Requirement already satisfied: torchaudio==0.8.1 in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu111) (4.1.1)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu111) (1.21.6)\n",
+ "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.1+cu111) (7.1.2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!nvidia-smi\n",
+ "!pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "os.chdir('/content')\n",
+ "!git clone https://github.com/xaiki/spoof-detect\n",
+ "!git clone https://github.com/meituan/YOLOv6\n",
+ "!wget -c https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt\n",
+ "!mkdir /content/YOLOv6/weights/\n",
+ "!mv yolov6s.pt /content/YOLOv6/weights/"
+ ],
+ "metadata": {
+ "id": "bQd8r4hv68cd",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8d7b1ba2-b10a-4a42-9e9f-c77bb4be4fdb"
+ },
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "fatal: destination path 'spoof-detect' already exists and is not an empty directory.\n",
+ "fatal: destination path 'YOLOv6' already exists and is not an empty directory.\n",
+ "--2022-09-30 07:11:35-- https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt\n",
+ "Resolving github.com (github.com)... 20.205.243.166\n",
+ "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n",
+ "HTTP request sent, awaiting response... 302 Found\n",
+ "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/7af0d5c3-a8e5-456e-ae8c-29b7fc22753e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220930%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220930T071135Z&X-Amz-Expires=300&X-Amz-Signature=9b25436af1c1ca00c733f52dcb3dacb3606658be832729e4639f709023d1e2a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream [following]\n",
+ "--2022-09-30 07:11:36-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/7af0d5c3-a8e5-456e-ae8c-29b7fc22753e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220930%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220930T071135Z&X-Amz-Expires=300&X-Amz-Signature=9b25436af1c1ca00c733f52dcb3dacb3606658be832729e4639f709023d1e2a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream\n",
+ "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 416 Range Not Satisfiable\n",
+ "\n",
+ " The file is already fully retrieved; nothing to do.\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 56
+ },
+ "id": "dy-9m16jBF6t",
+ "outputId": "3a8b6f9d-f885-4bbe-f112-2f155536086a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <input type=\"file\" id=\"files-25404031-1291-4d7a-9d8e-f71c2c60467c\" name=\"files[]\" multiple disabled\n",
+ " style=\"border:none\" />\n",
+ " <output id=\"result-25404031-1291-4d7a-9d8e-f71c2c60467c\">\n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " </output>\n",
+ " <script>// Copyright 2017 Google LLC\n",
+ "//\n",
+ "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "// you may not use this file except in compliance with the License.\n",
+ "// You may obtain a copy of the License at\n",
+ "//\n",
+ "// http://www.apache.org/licenses/LICENSE-2.0\n",
+ "//\n",
+ "// Unless required by applicable law or agreed to in writing, software\n",
+ "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "// See the License for the specific language governing permissions and\n",
+ "// limitations under the License.\n",
+ "\n",
+ "/**\n",
+ " * @fileoverview Helpers for google.colab Python module.\n",
+ " */\n",
+ "(function(scope) {\n",
+ "function span(text, styleAttributes = {}) {\n",
+ " const element = document.createElement('span');\n",
+ " element.textContent = text;\n",
+ " for (const key of Object.keys(styleAttributes)) {\n",
+ " element.style[key] = styleAttributes[key];\n",
+ " }\n",
+ " return element;\n",
+ "}\n",
+ "\n",
+ "// Max number of bytes which will be uploaded at a time.\n",
+ "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+ "\n",
+ "function _uploadFiles(inputId, outputId) {\n",
+ " const steps = uploadFilesStep(inputId, outputId);\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " // Cache steps on the outputElement to make it available for the next call\n",
+ " // to uploadFilesContinue from Python.\n",
+ " outputElement.steps = steps;\n",
+ "\n",
+ " return _uploadFilesContinue(outputId);\n",
+ "}\n",
+ "\n",
+ "// This is roughly an async generator (not supported in the browser yet),\n",
+ "// where there are multiple asynchronous steps and the Python side is going\n",
+ "// to poll for completion of each step.\n",
+ "// This uses a Promise to block the python side on completion of each step,\n",
+ "// then passes the result of the previous step as the input to the next step.\n",
+ "function _uploadFilesContinue(outputId) {\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " const steps = outputElement.steps;\n",
+ "\n",
+ " const next = steps.next(outputElement.lastPromiseValue);\n",
+ " return Promise.resolve(next.value.promise).then((value) => {\n",
+ " // Cache the last promise value to make it available to the next\n",
+ " // step of the generator.\n",
+ " outputElement.lastPromiseValue = value;\n",
+ " return next.value.response;\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Generator function which is called between each async step of the upload\n",
+ " * process.\n",
+ " * @param {string} inputId Element ID of the input file picker element.\n",
+ " * @param {string} outputId Element ID of the output display.\n",
+ " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+ " */\n",
+ "function* uploadFilesStep(inputId, outputId) {\n",
+ " const inputElement = document.getElementById(inputId);\n",
+ " inputElement.disabled = false;\n",
+ "\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " outputElement.innerHTML = '';\n",
+ "\n",
+ " const pickedPromise = new Promise((resolve) => {\n",
+ " inputElement.addEventListener('change', (e) => {\n",
+ " resolve(e.target.files);\n",
+ " });\n",
+ " });\n",
+ "\n",
+ " const cancel = document.createElement('button');\n",
+ " inputElement.parentElement.appendChild(cancel);\n",
+ " cancel.textContent = 'Cancel upload';\n",
+ " const cancelPromise = new Promise((resolve) => {\n",
+ " cancel.onclick = () => {\n",
+ " resolve(null);\n",
+ " };\n",
+ " });\n",
+ "\n",
+ " // Wait for the user to pick the files.\n",
+ " const files = yield {\n",
+ " promise: Promise.race([pickedPromise, cancelPromise]),\n",
+ " response: {\n",
+ " action: 'starting',\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " cancel.remove();\n",
+ "\n",
+ " // Disable the input element since further picks are not allowed.\n",
+ " inputElement.disabled = true;\n",
+ "\n",
+ " if (!files) {\n",
+ " return {\n",
+ " response: {\n",
+ " action: 'complete',\n",
+ " }\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " for (const file of files) {\n",
+ " const li = document.createElement('li');\n",
+ " li.append(span(file.name, {fontWeight: 'bold'}));\n",
+ " li.append(span(\n",
+ " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+ " `last modified: ${\n",
+ " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+ " 'n/a'} - `));\n",
+ " const percent = span('0% done');\n",
+ " li.appendChild(percent);\n",
+ "\n",
+ " outputElement.appendChild(li);\n",
+ "\n",
+ " const fileDataPromise = new Promise((resolve) => {\n",
+ " const reader = new FileReader();\n",
+ " reader.onload = (e) => {\n",
+ " resolve(e.target.result);\n",
+ " };\n",
+ " reader.readAsArrayBuffer(file);\n",
+ " });\n",
+ " // Wait for the data to be ready.\n",
+ " let fileData = yield {\n",
+ " promise: fileDataPromise,\n",
+ " response: {\n",
+ " action: 'continue',\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+ " let position = 0;\n",
+ " do {\n",
+ " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+ " const chunk = new Uint8Array(fileData, position, length);\n",
+ " position += length;\n",
+ "\n",
+ " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+ " yield {\n",
+ " response: {\n",
+ " action: 'append',\n",
+ " file: file.name,\n",
+ " data: base64,\n",
+ " },\n",
+ " };\n",
+ "\n",
+ " let percentDone = fileData.byteLength === 0 ?\n",
+ " 100 :\n",
+ " Math.round((position / fileData.byteLength) * 100);\n",
+ " percent.textContent = `${percentDone}% done`;\n",
+ "\n",
+ " } while (position < fileData.byteLength);\n",
+ " }\n",
+ "\n",
+ " // All done.\n",
+ " yield {\n",
+ " response: {\n",
+ " action: 'complete',\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "scope.google = scope.google || {};\n",
+ "scope.google.colab = scope.google.colab || {};\n",
+ "scope.google.colab._files = {\n",
+ " _uploadFiles,\n",
+ " _uploadFilesContinue,\n",
+ "};\n",
+ "})(self);\n",
+ "</script> "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "for d in ['/content/spoof-detect/python', '/content/YOLOv6']:\n",
+ " os.chdir(d) \n",
+ " print(os.system('pip install -r requirements.txt'))\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "okjQpF7i7Q6f",
+ "outputId": "e57e2b5e-a116-4c41-dafd-cd24e5c0fb9a"
+ },
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "256\n",
+ "0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pwd"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eC9UMx6C8N2h",
+ "outputId": "fa1fdac9-8d5f-4505-de5f-820740ab7511"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "/content/YOLOv6\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/gdrive\")"
+ ],
+ "metadata": {
+ "id": "9Dw-WaObDRSK",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "3aa91913-9f21-41d3-a17b-1dec73c26af2"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!ls /content/gdrive/MyDrive/data.tar.gz\n",
+ "!cd /content && tar xvzf /content/gdrive/MyDrive/data.tar.gz"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_NZCIqEPAMYO",
+ "outputId": "f296adfa-8aab-4b5f-eed0-098843eec2f7"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+ "data/split/labels/train/00431.12.69-x0y6.txt\n",
+ "data/split/labels/train/00431.12.69-x1y1.txt\n",
+ "data/split/labels/train/00431.12.69-x1y2.txt\n",
+ "data/split/labels/train/00431.12.69-x2y1.txt\n",
+ "data/split/labels/train/00431.12.69-x2y3.txt\n",
+ "data/split/labels/train/00431.12.69-x2y4.txt\n",
+ "data/split/labels/train/00319.12.70-x0y1.txt\n",
+ "data/split/labels/train/00319.12.70-x0y3.txt\n",
+ "data/split/labels/train/00319.12.70-x0y7.txt\n",
+ "data/split/labels/train/00321.13.3-x0y6.txt\n",
+ "data/split/labels/train/00321.13.3-x0y7.txt\n",
+ "data/split/labels/train/00321.13.3-x0y8.txt\n",
+ "data/split/labels/train/00321.13.3-x1y6.txt\n",
+ "data/split/labels/train/00321.13.3-x1y7.txt\n",
+ "data/split/labels/train/00321.13.3-x1y8.txt\n",
+ "data/split/labels/train/00321.13.3-x3y3.txt\n",
+ "data/split/labels/train/00321.13.3-x3y7.txt\n",
+ "data/split/labels/train/00321.13.3-x5y7.txt\n",
+ "data/split/labels/train/00007.13.4-x0y3.txt\n",
+ "data/split/labels/train/00007.13.4-x0y8.txt\n",
+ "data/split/labels/train/00007.13.4-x0y9.txt\n",
+ "data/split/labels/train/00007.13.4-x0y11.txt\n",
+ "data/split/labels/train/00007.13.4-x0y12.txt\n",
+ "data/split/labels/train/00007.13.4-x1y3.txt\n",
+ "data/split/labels/train/00007.13.4-x1y7.txt\n",
+ "data/split/labels/train/00007.13.4-x1y8.txt\n",
+ "data/split/labels/train/00007.13.4-x1y9.txt\n",
+ "data/split/labels/train/00007.13.4-x2y6.txt\n",
+ "data/split/labels/train/00007.13.4-x2y7.txt\n",
+ "data/split/labels/train/00007.13.4-x2y8.txt\n",
+ "data/split/labels/train/00007.13.4-x2y9.txt\n",
+ "data/split/labels/train/00330.14.56-x1y12.txt\n",
+ "data/split/labels/train/00332.13.5-x2y7.txt\n",
+ "data/split/labels/train/00014.13.6-x0y9.txt\n",
+ "data/split/labels/train/00014.13.6-x0y10.txt\n",
+ "data/split/labels/train/00014.13.6-x0y12.txt\n",
+ "data/split/labels/train/00014.13.6-x1y9.txt\n",
+ "data/split/labels/train/00339.12.53-x0y0.txt\n",
+ "data/split/labels/train/00339.12.53-x0y1.txt\n",
+ "data/split/labels/train/00339.12.53-x0y2.txt\n",
+ "data/split/labels/train/00339.12.53-x0y3.txt\n",
+ "data/split/labels/train/00339.12.53-x1y0.txt\n",
+ "data/split/labels/train/00339.12.53-x1y1.txt\n",
+ "data/split/labels/train/00339.12.53-x1y4.txt\n",
+ "data/split/labels/train/00339.12.53-x2y0.txt\n",
+ "data/split/labels/train/00339.12.53-x2y1.txt\n",
+ "data/split/labels/train/00339.12.53-x2y4.txt\n",
+ "data/split/labels/train/00339.12.53-x2y7.txt\n",
+ "data/split/labels/train/44098.12.54-x0y4.txt\n",
+ "data/split/labels/train/44098.12.54-x0y7.txt\n",
+ "data/split/labels/train/44098.12.54-x0y9.txt\n",
+ "data/split/labels/train/44098.12.54-x0y10.txt\n",
+ "data/split/labels/train/44098.12.54-x1y0.txt\n",
+ "data/split/labels/train/44098.12.54-x1y4.txt\n",
+ "data/split/labels/train/44098.12.54-x1y7.txt\n",
+ "data/split/labels/train/44098.12.54-x1y8.txt\n",
+ "data/split/labels/train/44098.12.54-x1y9.txt\n",
+ "data/split/labels/train/44098.12.54-x1y10.txt\n",
+ "data/split/labels/train/44098.12.54-x2y0.txt\n",
+ "data/split/labels/train/44098.12.54-x2y8.txt\n",
+ "data/split/labels/train/44099.12.55-x0y2.txt\n",
+ "data/split/labels/train/44099.12.55-x1y0.txt\n",
+ "data/split/labels/train/44099.12.55-x1y4.txt\n",
+ "data/split/labels/train/44099.12.55-x2y0.txt\n",
+ "data/split/labels/train/44099.12.55-x2y2.txt\n",
+ "data/split/labels/train/44099.12.55-x2y4.txt\n",
+ "data/split/labels/train/00330.12.56-x0y1.txt\n",
+ "data/split/labels/train/00330.12.56-x0y3.txt\n",
+ "data/split/labels/train/00330.12.56-x1y1.txt\n",
+ "data/split/labels/train/00330.12.56-x1y3.txt\n",
+ "data/split/labels/train/00330.12.56-x1y4.txt\n",
+ "data/split/labels/train/00330.12.56-x1y12.txt\n",
+ "data/split/labels/train/00330.12.56-x2y3.txt\n",
+ "data/split/labels/train/00330.12.56-x2y4.txt\n",
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+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "%%writefile /content/data/data.yaml\n",
+ "# this file is autogenerated by write_data.py for YOLO version 6\n",
+ "\n",
+ "\n",
+ "train: /content/data/split/images/train\n",
+ "val: /content/data/split/images/val\n",
+ "test: /content/data/split/images/test\n",
+ "\n",
+ "is_coco: False\n",
+ " \n",
+ "\n",
+ "nc: 55\n",
+ "num_classes: 55\n",
+ "names: ['BACS BANCO DE CREDITO Y SECURITIZAC', 'BANCO BBVA ARGENTINA S.A.', 'BANCO BICA S.A.', 'BANCO CETELEM ARGENTINA S.A.', 'BANCO CMF S.A.', 'BANCO COINAG S.A.', 'BANCO COLUMBIA S.A.', 'BANCO COMAFI SOCIEDAD ANONIMA', 'BANCO CREDICOOP COOPERATIVO LIMITAD', 'BANCO DE COMERCIO S.A.', 'BANCO DE CORRIENTES S.A.', 'BANCO DE FORMOSA S.A.', 'BANCO DE GALICIA Y BUENOS AIRES S.A', 'BANCO DE INVERSION Y COMERCIO EXTER', 'BANCO DE LA CIUDAD DE BUENOS AIRES', 'BANCO DE LA NACION ARGENTINA', 'BANCO DE LA PAMPA SOCIEDAD DE ECONO', 'BANCO DE LA PROVINCIA DE BUENOS AIR', 'BANCO DE LA PROVINCIA DE CORDOBA S.', 'BANCO DE SAN JUAN S.A.', 'BANCO DE SANTA CRUZ S.A.', 'BANCO DE SANTIAGO DEL ESTERO S.A.', 'BANCO DE SERVICIOS FINANCIEROS S.A.', 'BANCO DE SERVICIOS Y TRANSACCIONES ', 'BANCO DE VALORES S.A.', 'BANCO DEL CHUBUT S.A.', 'BANCO DEL SOL S.A.', 'BANCO DINO S.A.', 'BANCO HIPOTECARIO S.A.', 'BANCO INDUSTRIAL S.A.', 'BANCO INTERFINANZAS S.A.', 'BANCO ITAU ARGENTINA S.A.', 'BANCO JULIO SOCIEDAD ANONIMA', 'BANCO MACRO S.A.', 'BANCO MARIVA S.A.', 'BANCO MASVENTAS S.A.', 'BANCO MERIDIAN S.A.', 'BANCO MUNICIPAL DE ROSARIO', 'BANCO PATAGONIA S.A.', 'BANCO PIANO S.A.', 'BANCO PROVINCIA DE TIERRA DEL FUEGO', 'BANCO PROVINCIA DEL NEUQUÉN SOCIEDA', 'BANCO RIOJA SOCIEDAD ANONIMA UNIPER', 'BANCO ROELA S.A.', 'BANCO SAENZ S.A.', 'BANCO SANTANDER RIO S.A.', 'BANCO SUCREDITO REGIONAL S.A.U.', 'BANCO SUPERVIELLE S.A.', 'BANCO VOII S.A.', 'BNP PARIBAS', 'BRUBANK S.A.U.', 'CITIBANK N.A.', 'COMPAÑIA FINANCIERA ARGENTINA S.A.', 'CREDITO REGIONAL COMPAÑIA FINANCIER', 'FCA COMPAÑIA FINANCIERA S.A.']\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "0KARBCTAB-oj",
+ "outputId": "d5ef877a-d671-4219-90ca-9191ad683c7c"
+ },
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Overwriting /content/data/data.yaml\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# run this cell to begin training\n",
+ "!python /content/YOLOv6/tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --epochs 100 --img-size 416 --data /content/data/data.yaml --device 0 "
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ie9WgRG4D3q0",
+ "outputId": "aad591cb-36ad-4809-a0ad-8d66caa793bc"
+ },
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Using 1 GPU for training... \n",
+ "training args are: Namespace(batch_size=32, calib=False, check_images=False, check_labels=False, conf_file='configs/yolov6s_finetune.py', data_path='/content/data/data.yaml', device='0', dist_url='env://', distill=False, distill_feat=False, epochs=100, eval_final_only=False, eval_interval=20, gpu_count=0, heavy_eval_range=50, img_size=416, local_rank=-1, name='exp', output_dir='./runs/train', quant=False, rank=-1, resume=False, save_ckpt_on_last_n_epoch=-1, save_dir='runs/train/exp6', stop_aug_last_n_epoch=15, teacher_model_path=None, temperature=20, workers=8, world_size=1, write_trainbatch_tb=False)\n",
+ "\n",
+ "Train: Final numbers of valid images: 51942/ labels: 51942. \n",
+ "1.9s for dataset initialization.\n",
+ "Convert to COCO format\n",
+ "100% 6433/6433 [00:00<00:00, 76764.95it/s]\n",
+ "Convert to COCO format finished. Resutls saved in /content/data/split/annotations/instances_val.json\n",
+ "Val: Final numbers of valid images: 6433/ labels: 6433. \n",
+ "1.0s for dataset initialization.\n",
+ "Loading state_dict from weights/yolov6s.pt for fine-tuning...\n",
+ "Model: Model(\n",
+ " (backbone): EfficientRep(\n",
+ " (stem): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (ERBlock_2): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (ERBlock_3): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (ERBlock_4): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (3): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (4): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (ERBlock_5): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (2): SimSPPF(\n",
+ " (cv1): SimConv(\n",
+ " (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " (cv2): SimConv(\n",
+ " (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (neck): RepPANNeck(\n",
+ " (Rep_p4): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (Rep_p3): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (Rep_n3): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (Rep_n4): RepBlock(\n",
+ " (conv1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (block): Sequential(\n",
+ " (0): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (1): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " (2): RepVGGBlock(\n",
+ " (nonlinearity): ReLU(inplace=True)\n",
+ " (se): Identity()\n",
+ " (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (rbr_dense): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (rbr_1x1): Sequential(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (reduce_layer0): SimConv(\n",
+ " (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " (upsample0): Transpose(\n",
+ " (upsample_transpose): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
+ " )\n",
+ " (reduce_layer1): SimConv(\n",
+ " (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " (upsample1): Transpose(\n",
+ " (upsample_transpose): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
+ " )\n",
+ " (downsample2): SimConv(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " (downsample1): SimConv(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): ReLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (detect): Detect(\n",
+ " (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (stems): ModuleList(\n",
+ " (0): Conv(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (1): Conv(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (2): Conv(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (cls_convs): ModuleList(\n",
+ " (0): Conv(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (1): Conv(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (2): Conv(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (reg_convs): ModuleList(\n",
+ " (0): Conv(\n",
+ " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (1): Conv(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (2): Conv(\n",
+ " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (cls_preds): ModuleList(\n",
+ " (0): Conv2d(64, 55, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (1): Conv2d(128, 55, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (2): Conv2d(256, 55, kernel_size=(1, 1), stride=(1, 1))\n",
+ " )\n",
+ " (reg_preds): ModuleList(\n",
+ " (0): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (2): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
+ " )\n",
+ " )\n",
+ ")\n",
+ "Training start...\n",
+ "\n",
+ " Epoch iou_loss dfl_loss cls_loss\n",
+ " 0% 0/1624 [00:00<?, ?it/s]\n",
+ "ERROR in training steps.\n",
+ "ERROR in training loop or eval/save model.\n",
+ "Traceback (most recent call last):\n",
+ " File \"/content/YOLOv6/tools/train.py\", line 126, in <module>\n",
+ " main(args)\n",
+ " File \"/content/YOLOv6/tools/train.py\", line 116, in main\n",
+ " trainer.train()\n",
+ " File \"/content/YOLOv6/yolov6/core/engine.py\", line 99, in train\n",
+ " self.train_in_loop(self.epoch)\n",
+ " File \"/content/YOLOv6/yolov6/core/engine.py\", line 113, in train_in_loop\n",
+ " self.train_in_steps(epoch_num, self.step)\n",
+ " File \"/content/YOLOv6/yolov6/core/engine.py\", line 142, in train_in_steps\n",
+ " total_loss, loss_items = self.compute_loss(preds, targets, epoch_num, step_num)\n",
+ " File \"/content/YOLOv6/yolov6/models/loss.py\", line 85, in __call__\n",
+ " pred_bboxes.detach() * stride_tensor)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
+ " result = self.forward(*input, **kwargs)\n",
+ " File \"/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
+ " return func(*args, **kwargs)\n",
+ " File \"/content/YOLOv6/yolov6/assigners/atss_assigner.py\", line 78, in forward\n",
+ " gt_labels, gt_bboxes, target_gt_idx, fg_mask)\n",
+ " File \"/content/YOLOv6/yolov6/assigners/atss_assigner.py\", line 158, in get_targets\n",
+ " target_scores = F.one_hot(target_labels.long(), self.num_classes + 1).float()\n",
+ "RuntimeError: Class values must be smaller than num_classes.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!nvidia-smi"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7cRtRY6XG3Yp",
+ "outputId": "70c3f664-9c6e-4587-a595-d57104089387"
+ },
+ "execution_count": 28,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Fri Sep 30 06:35:51 2022 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
+ "| N/A 44C P8 10W / 70W | 0MiB / 15109MiB | 0% Default |\n",
+ "| | | N/A |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "| No running processes found |\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file