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Finetuning Yolov5 using annotations from RoboFlow

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  1. Yolov5_finetuning.ipynb +870 -0
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1
+ {
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+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "metadata": {
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+ "colab": {
8
+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "3KDJWiA7bBx-",
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+ "outputId": "984c5455-546d-44e8-c8f6-dc6135c8d4e5"
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+ },
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+ "outputs": [
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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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+ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
19
+ ]
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+ },
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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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+ "I am feeling dizzy due to long lectures. What will my teacher suggest me?\n",
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+ "\n",
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+ "Answer: Your teacher will suggest you to take a break and rest for a while.\n",
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+ "\n",
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+ "Exercise\n"
30
+ ]
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+ }
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+ ],
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+ "source": [
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+ "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
35
+ "prompt=\"\"\n",
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+ "if y==0:\n",
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+ " prompt=\"I am feeling focussed while studying. What will my teacher suggest me?\"\n",
38
+ "elif y==1:\n",
39
+ " prompt=\"I am feeling dizzy due to long lectures. What will my teacher suggest me?\"\n",
40
+ "else:\n",
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+ " prompt=\"I am feeling distracted. What will my teacher suggest me?\"\n",
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+ "\n",
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+ "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
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+ "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
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+ "inputs = tokenizer(prompt, return_tensors=\"pt\", return_attention_mask=False)\n",
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+ "\n",
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+ "outputs = model.generate(**inputs, max_length=40)\n",
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+ "text = tokenizer.batch_decode(outputs)[0]\n",
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+ "print(text)"
50
+ ]
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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": "_XgovGHcme6Y",
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+ "outputId": "09e9b010-8cb7-49b0-ac08-836cd95b8b7e"
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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 'yolov5'...\n",
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+ "remote: Enumerating objects: 16003, done.\u001b[K\n",
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+ "remote: Counting objects: 100% (36/36), done.\u001b[K\n",
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+ "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
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+ "remote: Total 16003 (delta 21), reused 20 (delta 13), pack-reused 15967\u001b[K\n",
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+ "Receiving objects: 100% (16003/16003), 14.60 MiB | 18.50 MiB/s, done.\n",
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+ "Resolving deltas: 100% (10987/10987), 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/ultralytics/yolov5"
79
+ ]
80
+ },
81
+ {
82
+ "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": "s3KUTzud0uZB",
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+ "outputId": "f8b98b7e-4b5f-4515-8714-73cdc7514352"
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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 'yolov5'...\n",
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+ "remote: Enumerating objects: 16008, done.\u001b[K\n",
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+ "remote: Counting objects: 100% (41/41), done.\u001b[K\n",
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+ "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
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+ "remote: Total 16008 (delta 22), reused 20 (delta 13), pack-reused 15967\u001b[K\n",
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+ "Receiving objects: 100% (16008/16008), 14.68 MiB | 23.23 MiB/s, done.\n",
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+ "Resolving deltas: 100% (10988/10988), done.\n",
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+ "/content/yolov5\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.0/190.0 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m641.7/641.7 kB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.3/155.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 MB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.0/69.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25h"
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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/ultralytics/yolov5 # clone repo\n",
121
+ "%cd yolov5\n",
122
+ "%pip install -qr requirements.txt # install dependencies\n",
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+ "%pip install -q roboflow\n",
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+ "\n",
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+ "import torch\n",
126
+ "import os\n",
127
+ "from IPython.display import Image, clear_output # to display images"
128
+ ]
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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": "1gNIXDkVzj_p",
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+ "outputId": "7b1aef2e-cad5-4833-d5b6-970666463a9b"
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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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+ "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.7)\n",
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+ "Requirement already satisfied: certifi==2022.12.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2022.12.7)\n",
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+ "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n",
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+ "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.10.0)\n",
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+ "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.10)\n",
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+ "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
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+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
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+ "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.23.5)\n",
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+ "Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n",
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+ "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n",
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+ "Requirement already satisfied: pyparsing==2.4.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.4.7)\n",
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+ "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
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+ "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
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+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n",
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+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
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+ "Requirement already satisfied: supervision in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.15.0)\n",
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+ "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.6)\n",
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+ "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.1)\n",
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+ "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n",
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+ "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
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+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.1.1)\n",
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+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.43.1)\n",
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+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (23.2)\n",
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+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.0)\n",
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+ "Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (1.11.3)\n",
170
+ "loading Roboflow workspace...\n",
171
+ "loading Roboflow project...\n"
172
+ ]
173
+ },
174
+ {
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+ "name": "stderr",
176
+ "output_type": "stream",
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+ "text": [
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+ "Downloading Dataset Version Zip in Engagement_level-1 to yolov5pytorch:: 100%|██████████| 1803/1803 [00:00<00:00, 13479.51it/s]"
179
+ ]
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+ },
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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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+ "\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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+ "\n",
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+ "Extracting Dataset Version Zip to Engagement_level-1 in yolov5pytorch:: 100%|██████████| 126/126 [00:00<00:00, 1721.71it/s]\n"
194
+ ]
195
+ }
196
+ ],
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+ "source": [
198
+ "!pip install roboflow\n",
199
+ "\n",
200
+ "from roboflow import Roboflow\n",
201
+ "rf = Roboflow(api_key=\"0Re3AbuZXbz2nQGc3N0a\")\n",
202
+ "project = rf.workspace(\"indian-institute-of-technology-indore-kbon5\").project(\"engagement_level\")\n",
203
+ "dataset = project.version(1).download(\"yolov5\")"
204
+ ]
205
+ },
206
+ {
207
+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {
210
+ "colab": {
211
+ "background_save": true,
212
+ "base_uri": "https://localhost:8080/"
213
+ },
214
+ "id": "bCxkKRcG0Uf2",
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+ "outputId": "bc521c02-dc45-41f8-fc3d-f29bf5678068"
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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[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/yolov5/Engagement_level-1/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=700, batch_size=16, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
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+ "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
224
+ "YOLOv5 🚀 v7.0-227-ge4df1ec Python-3.10.12 torch-2.0.1+cu118 CPU\n",
225
+ "\n",
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+ "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
227
+ "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
228
+ "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
229
+ "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
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+ "100% 755k/755k [00:00<00:00, 14.6MB/s]\n",
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+ "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
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+ "100% 14.1M/14.1M [00:00<00:00, 113MB/s] \n",
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+ "\n",
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+ "Overriding model.yaml nc=80 with nc=3\n",
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+ "\n",
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+ " from n params module arguments \n",
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+ " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
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+ " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
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+ " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
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+ " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
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+ " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
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+ " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
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+ " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
244
+ " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
245
+ " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
246
+ " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
247
+ " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
248
+ " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
249
+ " 12 [-1, 6] 1 0 models.common.Concat [1] \n",
250
+ " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
251
+ " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
252
+ " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
253
+ " 16 [-1, 4] 1 0 models.common.Concat [1] \n",
254
+ " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
255
+ " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
256
+ " 19 [-1, 14] 1 0 models.common.Concat [1] \n",
257
+ " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
258
+ " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
259
+ " 22 [-1, 10] 1 0 models.common.Concat [1] \n",
260
+ " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
261
+ " 24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
262
+ "Model summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs\n",
263
+ "\n",
264
+ "Transferred 343/349 items from yolov5s.pt\n",
265
+ "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
266
+ "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
267
+ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov5/Engagement_level-1/train/labels... 60 images, 0 backgrounds, 0 corrupt: 100% 60/60 [00:00<00:00, 629.81it/s]\n",
268
+ "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov5/Engagement_level-1/train/labels.cache\n",
269
+ "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 60/60 [00:00<00:00, 223.23it/s]\n",
270
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov5/Engagement_level-1/train/labels.cache... 60 images, 0 backgrounds, 0 corrupt: 100% 60/60 [00:00<?, ?it/s]\n",
271
+ "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 60/60 [00:00<00:00, 126.25it/s]\n",
272
+ "\n",
273
+ "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.00 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
274
+ "Plotting labels to runs/train/exp2/labels.jpg... \n",
275
+ "Image sizes 320 train, 320 val\n",
276
+ "Using 2 dataloader workers\n",
277
+ "Logging results to \u001b[1mruns/train/exp2\u001b[0m\n",
278
+ "Starting training for 700 epochs...\n",
279
+ "\n",
280
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
281
+ " 0/699 0G 0.1148 0.0167 0.04016 29 320: 100% 4/4 [00:28<00:00, 7.17s/it]\n",
282
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:09<00:00, 4.74s/it]\n",
283
+ " all 60 60 0.00298 0.869 0.0118 0.00203\n",
284
+ "\n",
285
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
286
+ " 1/699 0G 0.1147 0.01767 0.04109 30 320: 100% 4/4 [00:23<00:00, 5.75s/it]\n",
287
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.12s/it]\n",
288
+ " all 60 60 0.00326 0.967 0.01 0.00226\n",
289
+ "\n",
290
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
291
+ " 2/699 0G 0.09968 0.02034 0.04064 27 320: 100% 4/4 [00:23<00:00, 5.84s/it]\n",
292
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.35s/it]\n",
293
+ " all 60 60 0.00337 1 0.0237 0.00569\n",
294
+ "\n",
295
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
296
+ " 3/699 0G 0.08664 0.02657 0.03916 28 320: 100% 4/4 [00:25<00:00, 6.47s/it]\n",
297
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.21s/it]\n",
298
+ " all 60 60 0.00336 1 0.0397 0.0105\n",
299
+ "\n",
300
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
301
+ " 4/699 0G 0.07959 0.02711 0.03803 34 320: 100% 4/4 [00:24<00:00, 6.15s/it]\n",
302
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.88s/it]\n",
303
+ " all 60 60 0.00335 1 0.0603 0.0147\n",
304
+ "\n",
305
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
306
+ " 5/699 0G 0.07814 0.02324 0.03974 23 320: 100% 4/4 [00:23<00:00, 5.79s/it]\n",
307
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:10<00:00, 5.04s/it]\n",
308
+ " all 60 60 0.00336 1 0.0592 0.0148\n",
309
+ "\n",
310
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
311
+ " 6/699 0G 0.07563 0.02513 0.03911 25 320: 100% 4/4 [00:30<00:00, 7.63s/it]\n",
312
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:09<00:00, 4.74s/it]\n",
313
+ " all 60 60 0.00334 1 0.123 0.0374\n",
314
+ "\n",
315
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
316
+ " 7/699 0G 0.06618 0.02476 0.03714 29 320: 100% 4/4 [00:23<00:00, 5.78s/it]\n",
317
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.18s/it]\n",
318
+ " all 60 60 0.00336 1 0.133 0.0459\n",
319
+ "\n",
320
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
321
+ " 8/699 0G 0.06066 0.02808 0.03768 27 320: 100% 4/4 [00:23<00:00, 5.80s/it]\n",
322
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.14s/it]\n",
323
+ " all 60 60 0.00336 1 0.211 0.0753\n",
324
+ "\n",
325
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
326
+ " 9/699 0G 0.06034 0.02794 0.03704 34 320: 100% 4/4 [00:25<00:00, 6.40s/it]\n",
327
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.04s/it]\n",
328
+ " all 60 60 0.217 0.475 0.24 0.0817\n",
329
+ "\n",
330
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
331
+ " 10/699 0G 0.05864 0.02383 0.03743 22 320: 100% 4/4 [00:24<00:00, 6.19s/it]\n",
332
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.70s/it]\n",
333
+ " all 60 60 0.185 0.497 0.185 0.0613\n",
334
+ "\n",
335
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
336
+ " 11/699 0G 0.05549 0.02169 0.03698 25 320: 100% 4/4 [00:22<00:00, 5.67s/it]\n",
337
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.07s/it]\n",
338
+ " all 60 60 0.219 0.744 0.266 0.0939\n",
339
+ "\n",
340
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
341
+ " 12/699 0G 0.05677 0.02121 0.03696 30 320: 100% 4/4 [00:23<00:00, 5.88s/it]\n",
342
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.14s/it]\n",
343
+ " all 60 60 0.0881 0.797 0.091 0.0345\n",
344
+ "\n",
345
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
346
+ " 13/699 0G 0.05718 0.01962 0.03654 26 320: 100% 4/4 [00:25<00:00, 6.47s/it]\n",
347
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.94s/it]\n",
348
+ " all 60 60 0.279 0.693 0.371 0.116\n",
349
+ "\n",
350
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
351
+ " 14/699 0G 0.05638 0.02096 0.03626 26 320: 100% 4/4 [00:24<00:00, 6.17s/it]\n",
352
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.72s/it]\n",
353
+ " all 60 60 0.079 0.437 0.08 0.019\n",
354
+ "\n",
355
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
356
+ " 15/699 0G 0.06284 0.01902 0.03492 26 320: 100% 4/4 [00:23<00:00, 5.79s/it]\n",
357
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.03s/it]\n",
358
+ " all 60 60 0.356 0.718 0.447 0.157\n",
359
+ "\n",
360
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
361
+ " 16/699 0G 0.05699 0.01826 0.03609 28 320: 100% 4/4 [00:23<00:00, 5.78s/it]\n",
362
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.01s/it]\n",
363
+ " all 60 60 0.248 0.717 0.27 0.131\n",
364
+ "\n",
365
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
366
+ " 17/699 0G 0.05251 0.01953 0.03538 31 320: 100% 4/4 [00:25<00:00, 6.37s/it]\n",
367
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.86s/it]\n",
368
+ " all 60 60 0.108 0.401 0.139 0.0385\n",
369
+ "\n",
370
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
371
+ " 18/699 0G 0.06448 0.01601 0.03599 26 320: 100% 4/4 [00:23<00:00, 5.93s/it]\n",
372
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.90s/it]\n",
373
+ " all 60 60 0.34 0.711 0.408 0.233\n",
374
+ "\n",
375
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
376
+ " 19/699 0G 0.05658 0.01938 0.03641 37 320: 100% 4/4 [00:22<00:00, 5.71s/it]\n",
377
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.70s/it]\n",
378
+ " all 60 60 0.156 0.43 0.19 0.0587\n",
379
+ "\n",
380
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
381
+ " 20/699 0G 0.05194 0.01774 0.03616 26 320: 100% 4/4 [00:23<00:00, 5.95s/it]\n",
382
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.83s/it]\n",
383
+ " all 60 60 0.34 0.673 0.353 0.12\n",
384
+ "\n",
385
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
386
+ " 21/699 0G 0.06204 0.01569 0.03604 26 320: 100% 4/4 [00:24<00:00, 6.21s/it]\n",
387
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.26s/it]\n",
388
+ " all 60 60 0.264 0.708 0.366 0.123\n",
389
+ "\n",
390
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
391
+ " 22/699 0G 0.06121 0.01758 0.03578 37 320: 100% 4/4 [00:22<00:00, 5.68s/it]\n",
392
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.24s/it]\n",
393
+ " all 60 60 0.173 0.376 0.322 0.106\n",
394
+ "\n",
395
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
396
+ " 23/699 0G 0.07099 0.01476 0.03562 25 320: 100% 4/4 [00:22<00:00, 5.68s/it]\n",
397
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.01s/it]\n",
398
+ " all 60 60 0.327 0.55 0.396 0.0984\n",
399
+ "\n",
400
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
401
+ " 24/699 0G 0.05836 0.01664 0.0353 27 320: 100% 4/4 [00:26<00:00, 6.75s/it]\n",
402
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.88s/it]\n",
403
+ " all 60 60 0.461 0.492 0.447 0.168\n",
404
+ "\n",
405
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
406
+ " 25/699 0G 0.06587 0.01786 0.03574 30 320: 100% 4/4 [00:28<00:00, 7.11s/it]\n",
407
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.39s/it]\n",
408
+ " all 60 60 0.533 0.74 0.607 0.291\n",
409
+ "\n",
410
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
411
+ " 26/699 0G 0.04578 0.02054 0.03571 30 320: 100% 4/4 [00:22<00:00, 5.74s/it]\n",
412
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.36s/it]\n",
413
+ " all 60 60 0.47 0.54 0.532 0.274\n",
414
+ "\n",
415
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
416
+ " 27/699 0G 0.05974 0.01675 0.03607 28 320: 100% 4/4 [00:22<00:00, 5.71s/it]\n",
417
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.16s/it]\n",
418
+ " all 60 60 0.246 0.727 0.421 0.125\n",
419
+ "\n",
420
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
421
+ " 28/699 0G 0.05062 0.01656 0.03541 28 320: 100% 4/4 [00:25<00:00, 6.29s/it]\n",
422
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.84s/it]\n",
423
+ " all 60 60 0.489 0.724 0.554 0.29\n",
424
+ "\n",
425
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
426
+ " 29/699 0G 0.04698 0.0172 0.03547 26 320: 100% 4/4 [00:24<00:00, 6.13s/it]\n",
427
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.58s/it]\n",
428
+ " all 60 60 0.241 0.811 0.422 0.116\n",
429
+ "\n",
430
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
431
+ " 30/699 0G 0.05077 0.01703 0.03587 31 320: 100% 4/4 [00:22<00:00, 5.71s/it]\n",
432
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.90s/it]\n",
433
+ " all 60 60 0.257 0.86 0.48 0.201\n",
434
+ "\n",
435
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
436
+ " 31/699 0G 0.04412 0.01677 0.03505 23 320: 100% 4/4 [00:23<00:00, 5.89s/it]\n",
437
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.84s/it]\n",
438
+ " all 60 60 0.321 0.761 0.47 0.176\n",
439
+ "\n",
440
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
441
+ " 32/699 0G 0.04664 0.01736 0.03477 28 320: 100% 4/4 [00:25<00:00, 6.37s/it]\n",
442
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.94s/it]\n",
443
+ " all 60 60 0.374 0.795 0.522 0.178\n",
444
+ "\n",
445
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
446
+ " 33/699 0G 0.04451 0.01787 0.03489 26 320: 100% 4/4 [00:22<00:00, 5.75s/it]\n",
447
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.09s/it]\n",
448
+ " all 60 60 0.352 0.847 0.485 0.221\n",
449
+ "\n",
450
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
451
+ " 34/699 0G 0.05076 0.01444 0.03559 27 320: 100% 4/4 [00:22<00:00, 5.70s/it]\n",
452
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.37s/it]\n",
453
+ " all 60 60 0.305 0.75 0.414 0.154\n",
454
+ "\n",
455
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
456
+ " 35/699 0G 0.04032 0.01536 0.03497 27 320: 100% 4/4 [00:25<00:00, 6.40s/it]\n",
457
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.81s/it]\n",
458
+ " all 60 60 0.388 0.852 0.546 0.309\n",
459
+ "\n",
460
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
461
+ " 36/699 0G 0.04438 0.01546 0.03479 28 320: 100% 4/4 [00:25<00:00, 6.29s/it]\n",
462
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.38s/it]\n",
463
+ " all 60 60 0.402 0.844 0.565 0.213\n",
464
+ "\n",
465
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
466
+ " 37/699 0G 0.04408 0.01283 0.03556 23 320: 100% 4/4 [00:23<00:00, 5.76s/it]\n",
467
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.03s/it]\n",
468
+ " all 60 60 0.4 0.742 0.596 0.303\n",
469
+ "\n",
470
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
471
+ " 38/699 0G 0.04575 0.01548 0.0343 35 320: 100% 4/4 [00:23<00:00, 5.81s/it]\n",
472
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.81s/it]\n",
473
+ " all 60 60 0.334 0.707 0.537 0.245\n",
474
+ "\n",
475
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
476
+ " 39/699 0G 0.04232 0.01121 0.03412 21 320: 100% 4/4 [00:25<00:00, 6.44s/it]\n",
477
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.79s/it]\n",
478
+ " all 60 60 0.387 0.825 0.599 0.248\n",
479
+ "\n",
480
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
481
+ " 40/699 0G 0.04386 0.01466 0.03384 33 320: 100% 4/4 [00:23<00:00, 5.80s/it]\n",
482
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.95s/it]\n",
483
+ " all 60 60 0.394 0.752 0.576 0.383\n",
484
+ "\n",
485
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
486
+ " 41/699 0G 0.04307 0.01365 0.03457 31 320: 100% 4/4 [00:23<00:00, 5.79s/it]\n",
487
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.47s/it]\n",
488
+ " all 60 60 0.266 0.758 0.498 0.274\n",
489
+ "\n",
490
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
491
+ " 42/699 0G 0.04002 0.01534 0.03449 23 320: 100% 4/4 [00:24<00:00, 6.07s/it]\n",
492
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.77s/it]\n",
493
+ " all 60 60 0.393 0.86 0.555 0.348\n",
494
+ "\n",
495
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
496
+ " 43/699 0G 0.0399 0.0134 0.03363 23 320: 100% 4/4 [00:25<00:00, 6.25s/it]\n",
497
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.52s/it]\n",
498
+ " all 60 60 0.28 0.828 0.453 0.226\n",
499
+ "\n",
500
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
501
+ " 44/699 0G 0.03927 0.01396 0.0331 27 320: 100% 4/4 [00:23<00:00, 5.77s/it]\n",
502
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:09<00:00, 4.53s/it]\n",
503
+ " all 60 60 0.376 0.93 0.568 0.315\n",
504
+ "\n",
505
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
506
+ " 45/699 0G 0.03745 0.01508 0.03355 36 320: 100% 4/4 [00:27<00:00, 6.78s/it]\n",
507
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.86s/it]\n",
508
+ " all 60 60 0.38 0.869 0.565 0.264\n",
509
+ "\n",
510
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
511
+ " 46/699 0G 0.03465 0.01184 0.03348 26 320: 100% 4/4 [00:24<00:00, 6.05s/it]\n",
512
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.82s/it]\n",
513
+ " all 60 60 0.367 0.912 0.57 0.247\n",
514
+ "\n",
515
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
516
+ " 47/699 0G 0.04088 0.01201 0.03438 25 320: 100% 4/4 [00:26<00:00, 6.61s/it]\n",
517
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.80s/it]\n",
518
+ " all 60 60 0.397 0.851 0.601 0.271\n",
519
+ "\n",
520
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
521
+ " 48/699 0G 0.03646 0.01501 0.034 43 320: 100% 4/4 [00:23<00:00, 5.91s/it]\n",
522
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.89s/it]\n",
523
+ " all 60 60 0.348 0.724 0.465 0.222\n",
524
+ "\n",
525
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
526
+ " 49/699 0G 0.04325 0.014 0.03281 31 320: 100% 4/4 [00:23<00:00, 5.80s/it]\n",
527
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.66s/it]\n",
528
+ " all 60 60 0.404 0.982 0.589 0.342\n",
529
+ "\n",
530
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
531
+ " 50/699 0G 0.03406 0.01154 0.03347 26 320: 100% 4/4 [00:23<00:00, 6.00s/it]\n",
532
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.80s/it]\n",
533
+ " all 60 60 0.235 0.773 0.367 0.207\n",
534
+ "\n",
535
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
536
+ " 51/699 0G 0.03759 0.01342 0.03353 30 320: 100% 4/4 [00:25<00:00, 6.34s/it]\n",
537
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.11s/it]\n",
538
+ " all 60 60 0.397 0.982 0.639 0.32\n",
539
+ "\n",
540
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
541
+ " 52/699 0G 0.0387 0.01353 0.03291 26 320: 100% 4/4 [00:22<00:00, 5.72s/it]\n",
542
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.25s/it]\n",
543
+ " all 60 60 0.451 0.947 0.68 0.395\n",
544
+ "\n",
545
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
546
+ " 53/699 0G 0.03778 0.01215 0.03339 26 320: 100% 4/4 [00:23<00:00, 5.78s/it]\n",
547
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.08s/it]\n",
548
+ " all 60 60 0.522 0.875 0.599 0.245\n",
549
+ "\n",
550
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
551
+ " 54/699 0G 0.03745 0.01284 0.03309 32 320: 100% 4/4 [00:25<00:00, 6.26s/it]\n",
552
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.78s/it]\n",
553
+ " all 60 60 0.564 0.935 0.708 0.451\n",
554
+ "\n",
555
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
556
+ " 55/699 0G 0.03718 0.01217 0.03413 29 320: 100% 4/4 [00:24<00:00, 6.21s/it]\n",
557
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.51s/it]\n",
558
+ " all 60 60 0.601 0.889 0.692 0.304\n",
559
+ "\n",
560
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
561
+ " 56/699 0G 0.03642 0.01154 0.03118 32 320: 100% 4/4 [00:22<00:00, 5.70s/it]\n",
562
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.74s/it]\n",
563
+ " all 60 60 0.59 0.914 0.742 0.36\n",
564
+ "\n",
565
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
566
+ " 57/699 0G 0.03416 0.01349 0.03268 33 320: 100% 4/4 [00:23<00:00, 5.92s/it]\n",
567
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.75s/it]\n",
568
+ " all 60 60 0.61 0.875 0.7 0.429\n",
569
+ "\n",
570
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
571
+ " 58/699 0G 0.03673 0.01111 0.03306 27 320: 100% 4/4 [00:25<00:00, 6.36s/it]\n",
572
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.08s/it]\n",
573
+ " all 60 60 0.61 0.892 0.711 0.432\n",
574
+ "\n",
575
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
576
+ " 59/699 0G 0.0351 0.01197 0.031 24 320: 100% 4/4 [00:23<00:00, 5.79s/it]\n",
577
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.14s/it]\n",
578
+ " all 60 60 0.592 0.915 0.695 0.363\n",
579
+ "\n",
580
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
581
+ " 60/699 0G 0.03514 0.01091 0.03113 25 320: 100% 4/4 [00:22<00:00, 5.71s/it]\n",
582
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.10s/it]\n",
583
+ " all 60 60 0.546 0.967 0.694 0.341\n",
584
+ "\n",
585
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
586
+ " 61/699 0G 0.03712 0.01262 0.03181 31 320: 100% 4/4 [00:25<00:00, 6.31s/it]\n",
587
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.84s/it]\n",
588
+ " all 60 60 0.616 0.982 0.725 0.452\n",
589
+ "\n",
590
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
591
+ " 62/699 0G 0.03396 0.01219 0.02939 33 320: 100% 4/4 [00:24<00:00, 6.19s/it]\n",
592
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.73s/it]\n",
593
+ " all 60 60 0.515 0.982 0.659 0.326\n",
594
+ "\n",
595
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
596
+ " 63/699 0G 0.0387 0.01055 0.02881 24 320: 100% 4/4 [00:23<00:00, 5.80s/it]\n",
597
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.03s/it]\n",
598
+ " all 60 60 0.618 0.965 0.718 0.427\n",
599
+ "\n",
600
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
601
+ " 64/699 0G 0.03284 0.01326 0.02932 35 320: 100% 4/4 [00:28<00:00, 7.22s/it]\n",
602
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.11s/it]\n",
603
+ " all 60 60 0.501 0.982 0.661 0.393\n",
604
+ "\n",
605
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
606
+ " 65/699 0G 0.03798 0.01136 0.0309 25 320: 100% 4/4 [00:25<00:00, 6.25s/it]\n",
607
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.80s/it]\n",
608
+ " all 60 60 0.614 0.982 0.73 0.419\n",
609
+ "\n",
610
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
611
+ " 66/699 0G 0.03059 0.009622 0.02893 24 320: 100% 4/4 [00:25<00:00, 6.26s/it]\n",
612
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.53s/it]\n",
613
+ " all 60 60 0.603 0.967 0.781 0.505\n",
614
+ "\n",
615
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
616
+ " 67/699 0G 0.03361 0.01142 0.02786 27 320: 100% 4/4 [00:23<00:00, 5.77s/it]\n",
617
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.80s/it]\n",
618
+ " all 60 60 0.634 0.936 0.753 0.421\n",
619
+ "\n",
620
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
621
+ " 68/699 0G 0.03537 0.01158 0.02716 24 320: 100% 4/4 [00:23<00:00, 5.87s/it]\n",
622
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.77s/it]\n",
623
+ " all 60 60 0.655 0.991 0.798 0.54\n",
624
+ "\n",
625
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
626
+ " 69/699 0G 0.03452 0.01018 0.02948 28 320: 100% 4/4 [00:25<00:00, 6.38s/it]\n",
627
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.02s/it]\n",
628
+ " all 60 60 0.644 0.886 0.753 0.364\n",
629
+ "\n",
630
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
631
+ " 70/699 0G 0.03341 0.009742 0.02731 27 320: 100% 4/4 [00:23<00:00, 5.92s/it]\n",
632
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.05s/it]\n",
633
+ " all 60 60 0.567 0.724 0.711 0.393\n",
634
+ "\n",
635
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
636
+ " 71/699 0G 0.03094 0.01166 0.02678 33 320: 100% 4/4 [00:23<00:00, 5.77s/it]\n",
637
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.38s/it]\n",
638
+ " all 60 60 0.447 0.91 0.73 0.434\n",
639
+ "\n",
640
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
641
+ " 72/699 0G 0.03755 0.01128 0.02623 33 320: 100% 4/4 [00:24<00:00, 6.21s/it]\n",
642
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.78s/it]\n",
643
+ " all 60 60 0.43 0.951 0.694 0.427\n",
644
+ "\n",
645
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
646
+ " 73/699 0G 0.03761 0.01068 0.0281 30 320: 100% 4/4 [00:24<00:00, 6.21s/it]\n",
647
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.35s/it]\n",
648
+ " all 60 60 0.535 0.956 0.716 0.438\n",
649
+ "\n",
650
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
651
+ " 74/699 0G 0.03308 0.01139 0.02612 23 320: 100% 4/4 [00:23<00:00, 5.79s/it]\n",
652
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.91s/it]\n",
653
+ " all 60 60 0.573 0.898 0.741 0.452\n",
654
+ "\n",
655
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
656
+ " 75/699 0G 0.03102 0.01173 0.02708 28 320: 100% 4/4 [00:23<00:00, 5.83s/it]\n",
657
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.76s/it]\n",
658
+ " all 60 60 0.514 0.952 0.692 0.431\n",
659
+ "\n",
660
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
661
+ " 76/699 0G 0.03709 0.01031 0.02376 23 320: 100% 4/4 [00:25<00:00, 6.48s/it]\n",
662
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.85s/it]\n",
663
+ " all 60 60 0.508 0.967 0.69 0.399\n",
664
+ "\n",
665
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
666
+ " 77/699 0G 0.03827 0.01099 0.02481 26 320: 100% 4/4 [00:23<00:00, 5.82s/it]\n",
667
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 4.00s/it]\n",
668
+ " all 60 60 0.658 0.963 0.715 0.457\n",
669
+ "\n",
670
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
671
+ " 78/699 0G 0.03286 0.01102 0.02549 24 320: 100% 4/4 [00:23<00:00, 5.76s/it]\n",
672
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.36s/it]\n",
673
+ " all 60 60 0.502 0.979 0.62 0.416\n",
674
+ "\n",
675
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
676
+ " 79/699 0G 0.03589 0.009822 0.02551 25 320: 100% 4/4 [00:24<00:00, 6.13s/it]\n",
677
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.85s/it]\n",
678
+ " all 60 60 0.657 0.97 0.693 0.387\n",
679
+ "\n",
680
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
681
+ " 80/699 0G 0.03114 0.01074 0.02764 26 320: 100% 4/4 [00:24<00:00, 6.19s/it]\n",
682
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.44s/it]\n",
683
+ " all 60 60 0.654 1 0.742 0.51\n",
684
+ "\n",
685
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
686
+ " 81/699 0G 0.03239 0.01028 0.02404 26 320: 100% 4/4 [00:22<00:00, 5.71s/it]\n",
687
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.81s/it]\n",
688
+ " all 60 60 0.577 1 0.731 0.391\n",
689
+ "\n",
690
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
691
+ " 82/699 0G 0.03369 0.01099 0.02669 29 320: 100% 4/4 [00:23<00:00, 5.95s/it]\n",
692
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.75s/it]\n",
693
+ " all 60 60 0.637 1 0.725 0.488\n",
694
+ "\n",
695
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
696
+ " 83/699 0G 0.02982 0.009387 0.0239 22 320: 100% 4/4 [00:25<00:00, 6.41s/it]\n",
697
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:10<00:00, 5.24s/it]\n",
698
+ " all 60 60 0.588 0.965 0.711 0.341\n",
699
+ "\n",
700
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
701
+ " 84/699 0G 0.03417 0.01011 0.02602 31 320: 100% 4/4 [00:25<00:00, 6.35s/it]\n",
702
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.29s/it]\n",
703
+ " all 60 60 0.575 0.965 0.717 0.462\n",
704
+ "\n",
705
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
706
+ " 85/699 0G 0.03453 0.01181 0.0269 23 320: 100% 4/4 [00:23<00:00, 5.80s/it]\n",
707
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.17s/it]\n",
708
+ " all 60 60 0.648 0.917 0.724 0.496\n",
709
+ "\n",
710
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
711
+ " 86/699 0G 0.02949 0.01067 0.02333 24 320: 100% 4/4 [00:22<00:00, 5.73s/it]\n",
712
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.93s/it]\n",
713
+ " all 60 60 0.503 0.912 0.65 0.451\n",
714
+ "\n",
715
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
716
+ " 87/699 0G 0.03346 0.01068 0.02476 33 320: 100% 4/4 [00:25<00:00, 6.36s/it]\n",
717
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.75s/it]\n",
718
+ " all 60 60 0.62 0.895 0.708 0.468\n",
719
+ "\n",
720
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
721
+ " 88/699 0G 0.02915 0.009806 0.02347 26 320: 100% 4/4 [00:23<00:00, 5.96s/it]\n",
722
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.42s/it]\n",
723
+ " all 60 60 0.463 0.895 0.65 0.397\n",
724
+ "\n",
725
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
726
+ " 89/699 0G 0.03496 0.01053 0.0263 24 320: 100% 4/4 [00:22<00:00, 5.68s/it]\n",
727
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.02s/it]\n",
728
+ " all 60 60 0.638 0.908 0.724 0.525\n",
729
+ "\n",
730
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
731
+ " 90/699 0G 0.02987 0.01084 0.02232 34 320: 100% 4/4 [00:22<00:00, 5.58s/it]\n",
732
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.30s/it]\n",
733
+ " all 60 60 0.52 0.895 0.691 0.445\n",
734
+ "\n",
735
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
736
+ " 91/699 0G 0.03242 0.01009 0.0243 30 320: 100% 4/4 [00:24<00:00, 6.02s/it]\n",
737
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.75s/it]\n",
738
+ " all 60 60 0.546 0.923 0.698 0.465\n",
739
+ "\n",
740
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
741
+ " 92/699 0G 0.03015 0.01085 0.0251 28 320: 100% 4/4 [00:25<00:00, 6.26s/it]\n",
742
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.79s/it]\n",
743
+ " all 60 60 0.644 0.979 0.725 0.485\n",
744
+ "\n",
745
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
746
+ " 93/699 0G 0.03176 0.01122 0.02344 28 320: 100% 4/4 [00:24<00:00, 6.07s/it]\n",
747
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.63s/it]\n",
748
+ " all 60 60 0.655 1 0.736 0.498\n",
749
+ "\n",
750
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
751
+ " 94/699 0G 0.02912 0.009009 0.02374 26 320: 100% 4/4 [00:22<00:00, 5.63s/it]\n",
752
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.08s/it]\n",
753
+ " all 60 60 0.605 0.983 0.759 0.553\n",
754
+ "\n",
755
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
756
+ " 95/699 0G 0.02835 0.01039 0.02398 28 320: 100% 4/4 [00:22<00:00, 5.57s/it]\n",
757
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.05s/it]\n",
758
+ " all 60 60 0.654 0.995 0.764 0.534\n",
759
+ "\n",
760
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
761
+ " 96/699 0G 0.03042 0.01005 0.02304 29 320: 100% 4/4 [00:24<00:00, 6.10s/it]\n",
762
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.84s/it]\n",
763
+ " all 60 60 0.466 0.989 0.706 0.474\n",
764
+ "\n",
765
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
766
+ " 97/699 0G 0.03237 0.01039 0.02538 26 320: 100% 4/4 [00:25<00:00, 6.33s/it]\n",
767
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.78s/it]\n",
768
+ " all 60 60 0.667 0.97 0.774 0.546\n",
769
+ "\n",
770
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
771
+ " 98/699 0G 0.028 0.01091 0.02253 34 320: 100% 4/4 [00:23<00:00, 5.84s/it]\n",
772
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.61s/it]\n",
773
+ " all 60 60 0.664 0.962 0.788 0.506\n",
774
+ "\n",
775
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
776
+ " 99/699 0G 0.03103 0.01049 0.0224 33 320: 100% 4/4 [00:22<00:00, 5.66s/it]\n",
777
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.79s/it]\n",
778
+ " all 60 60 0.638 0.969 0.783 0.552\n",
779
+ "\n",
780
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
781
+ " 100/699 0G 0.03054 0.01104 0.02552 28 320: 100% 4/4 [00:22<00:00, 5.66s/it]\n",
782
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.15s/it]\n",
783
+ " all 60 60 0.574 0.983 0.795 0.525\n",
784
+ "\n",
785
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
786
+ " 101/699 0G 0.03317 0.008246 0.02154 22 320: 100% 4/4 [00:24<00:00, 6.07s/it]\n",
787
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.80s/it]\n",
788
+ " all 60 60 0.642 0.956 0.779 0.529\n",
789
+ "\n",
790
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
791
+ " 102/699 0G 0.03023 0.01114 0.02024 32 320: 100% 4/4 [00:24<00:00, 6.21s/it]\n",
792
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.85s/it]\n",
793
+ " all 60 60 0.643 0.967 0.792 0.549\n",
794
+ "\n",
795
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
796
+ " 103/699 0G 0.02919 0.01021 0.02096 29 320: 100% 4/4 [00:22<00:00, 5.66s/it]\n",
797
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.83s/it]\n",
798
+ " all 60 60 0.645 0.961 0.777 0.528\n",
799
+ "\n",
800
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
801
+ " 104/699 0G 0.03228 0.01075 0.02334 25 320: 100% 4/4 [00:22<00:00, 5.63s/it]\n",
802
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.57s/it]\n",
803
+ " all 60 60 0.59 0.885 0.753 0.482\n",
804
+ "\n",
805
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
806
+ " 105/699 0G 0.02729 0.01029 0.02099 29 320: 100% 4/4 [00:23<00:00, 5.85s/it]\n",
807
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.83s/it]\n",
808
+ " all 60 60 0.573 0.887 0.755 0.503\n",
809
+ "\n",
810
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
811
+ " 106/699 0G 0.02997 0.01115 0.02439 26 320: 100% 4/4 [00:24<00:00, 6.20s/it]\n",
812
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.75s/it]\n",
813
+ " all 60 60 0.54 0.904 0.754 0.531\n",
814
+ "\n",
815
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
816
+ " 107/699 0G 0.02859 0.009621 0.02264 21 320: 100% 4/4 [00:24<00:00, 6.04s/it]\n",
817
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.16s/it]\n",
818
+ " all 60 60 0.519 0.858 0.752 0.519\n",
819
+ "\n",
820
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
821
+ " 108/699 0G 0.0273 0.009638 0.02336 24 320: 100% 4/4 [00:22<00:00, 5.62s/it]\n",
822
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:08<00:00, 4.03s/it]\n",
823
+ " all 60 60 0.508 0.838 0.743 0.455\n",
824
+ "\n",
825
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
826
+ " 109/699 0G 0.03021 0.01023 0.0197 26 320: 100% 4/4 [00:22<00:00, 5.66s/it]\n",
827
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:07<00:00, 3.51s/it]\n",
828
+ " all 60 60 0.512 0.74 0.735 0.506\n",
829
+ "\n",
830
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
831
+ " 110/699 0G 0.02915 0.01089 0.02271 29 320: 100% 4/4 [00:23<00:00, 5.87s/it]\n",
832
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.79s/it]\n",
833
+ " all 60 60 0.504 0.79 0.745 0.49\n",
834
+ "\n",
835
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
836
+ " 111/699 0G 0.02836 0.01115 0.02252 31 320: 100% 4/4 [00:25<00:00, 6.30s/it]\n",
837
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:05<00:00, 2.74s/it]\n",
838
+ " all 60 60 0.546 0.824 0.754 0.501\n",
839
+ "\n",
840
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
841
+ " 112/699 0G 0.02872 0.009921 0.02214 29 320: 100% 4/4 [00:23<00:00, 5.96s/it]\n",
842
+ " Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:06<00:00, 3.40s/it]\n",
843
+ " all 60 60 0.572 0.84 0.759 0.461\n",
844
+ "\n",
845
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
846
+ " 113/699 0G 0.0324 0.01026 0.02254 30 320: 75% 3/4 [00:19<00:06, 6.30s/it]"
847
+ ]
848
+ }
849
+ ],
850
+ "source": [
851
+ "!python train.py --img 320 --batch 16 --epochs 700 --data /content/yolov5/Engagement_level-1/data.yaml --weights yolov5s.pt --cache"
852
+ ]
853
+ }
854
+ ],
855
+ "metadata": {
856
+ "accelerator": "GPU",
857
+ "colab": {
858
+ "provenance": []
859
+ },
860
+ "kernelspec": {
861
+ "display_name": "Python 3",
862
+ "name": "python3"
863
+ },
864
+ "language_info": {
865
+ "name": "python"
866
+ }
867
+ },
868
+ "nbformat": 4,
869
+ "nbformat_minor": 0
870
+ }