{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNyqbZ6ZZ77buTUipo8qr9U"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["## 1. 掛載雲端硬碟"],"metadata":{"id":"JvFnrA5V65pO"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vu6SEPk764ES","executionInfo":{"status":"ok","timestamp":1694140704552,"user_tz":-480,"elapsed":18182,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"222bb1d7-0fa8-4e8e-c577-c8b67651929d"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["## 2. 安裝套件"],"metadata":{"id":"eOlb0q627EZI"}},{"cell_type":"code","source":["!pip install --upgrade pyyaml==5.3.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5SCKBEDB7EFG","executionInfo":{"status":"ok","timestamp":1693951265787,"user_tz":-480,"elapsed":5277,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"c07342bd-c403-444f-bbd0-29c10088837b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pyyaml==5.3.1 in /usr/local/lib/python3.10/dist-packages (5.3.1)\n"]}]},{"cell_type":"markdown","source":["## 3. 下載程式碼"],"metadata":{"id":"ngH7Q6Kx6f7m"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QA5CaOZY6aMM","executionInfo":{"status":"ok","timestamp":1693951269398,"user_tz":-480,"elapsed":4,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"d80494b0-1479-476b-f8bd-f4caa85b1998"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7\n","/content/drive/MyDrive/高科大合作案/ObjectDetection\n"]}],"source":["#顯示當前目錄\n","!pwd\n","\n","#切換目錄\n","%cd /content/drive/MyDrive/高科大合作案/ObjectDetection"]},{"cell_type":"code","source":["# 從git上面下載程式碼(只要執行一次)\n","!git clone https://github.com/WongKinYiu/yolov7.git"],"metadata":{"id":"VGe25V0bVkdb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 4. 下載已經使用coco dataset預先訓練好的權重\n","* 從 https://github.com/WongKinYiu/yolov7.git 上面去尋找連結"],"metadata":{"id":"zdM561AuAUZc"}},{"cell_type":"code","source":["!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ffh4H-2E6eEo","executionInfo":{"status":"ok","timestamp":1693945330488,"user_tz":-480,"elapsed":1494,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"c8ee1634-60a4-4794-ce4f-0c654283f921"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2023-09-05 20:22:08--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt\n","Resolving github.com (github.com)... 140.82.112.4\n","Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/b0243edf-9fb0-4337-95e1-42555f1b37cf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230905%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230905T202208Z&X-Amz-Expires=300&X-Amz-Signature=1f437406d5699676ef115e7090dc09dae60ec91b7f825747ea3a97de403b1866&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7.pt&response-content-type=application%2Foctet-stream [following]\n","--2023-09-05 20:22:08--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/b0243edf-9fb0-4337-95e1-42555f1b37cf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230905%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230905T202208Z&X-Amz-Expires=300&X-Amz-Signature=1f437406d5699676ef115e7090dc09dae60ec91b7f825747ea3a97de403b1866&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7.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... 200 OK\n","Length: 75587165 (72M) [application/octet-stream]\n","Saving to: ‘yolov7.pt’\n","\n","yolov7.pt           100%[===================>]  72.08M  64.4MB/s    in 1.1s    \n","\n","2023-09-05 20:22:10 (64.4 MB/s) - ‘yolov7.pt’ saved [75587165/75587165]\n","\n"]}]},{"cell_type":"markdown","source":["## 5. 上傳資料與新增設定檔"],"metadata":{"id":"usnmC9u8AtTw"}},{"cell_type":"code","source":["#切換目錄到剛下載的git專案\n","%cd yolov7"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0FaLnNRP6eB9","executionInfo":{"status":"ok","timestamp":1693951277345,"user_tz":-480,"elapsed":255,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"a783c10f-0ee5-4b29-e123-be96d1271bfd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7\n"]}]},{"cell_type":"markdown","source":["## 6-1. 在./yolo/data/目錄底下手動建立資料夾 [project名稱]\n","## 6-2. 上傳訓練資料到./yolo/data/train跟 ./yolo/data/val/\n","## 6-3. 複製data目錄底下的coco.yaml, 改名為[project名稱].yaml\n","## 6-4. 編輯[project名稱].yaml裡面的參數\n","* train: ./data/[project名稱]/train\n","* val: ./data/[project名稱]/val\n","* test: ./data/[project名稱]/test\n","* nc: [總共有多少類別]\n","* names: [每個類別代表的英文名稱]\n","\n","## 6-5. 複製cfg/training目錄底下的yolov7.yaml 並改名為yolov7_[project名稱].yaml\n","## 6-6. 編輯yolov7_[project名稱].yaml\n","* nc: [總共有多少類別]"],"metadata":{"id":"px5Ui1z691Gf"}},{"cell_type":"markdown","source":["## 7. 訓練模型"],"metadata":{"id":"o1qlx6w0BIY4"}},{"cell_type":"code","source":["!pwd\n","\n","%cd /content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z-vgRl_mVUGk","executionInfo":{"status":"ok","timestamp":1693951280333,"user_tz":-480,"elapsed":426,"user":{"displayName":"Hung-Hsun Chen","userId":"00679635348522298932"}},"outputId":"ede2301a-4698-4334-8539-890661dff746"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7\n","/content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7\n"]}]},{"cell_type":"code","source":["!python train.py --device 0 --batch-size 16 --epochs 100 --data data/fish.yaml --img 640 640 --hyp data/hyp.scratch.custom.yaml --cfg cfg/training/yolov7_fish.yaml --weights 'yolov7.pt' --name yolov7-fish"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TtHOAGy56c2Q","outputId":"34517cf3-5252-4083-eadd-e0de4a439987"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2023-09-05 22:01:25.865011: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n","To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2023-09-05 22:01:26.996060: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n","YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n","\n","Namespace(weights='yolov7.pt', cfg='cfg/training/yolov7_fish.yaml', data='data/fish.yaml', hyp='data/hyp.scratch.custom.yaml', epochs=100, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7-fish', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7-fish4', total_batch_size=16)\n","\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n","\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, 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=0.2, 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, paste_in=0.0, loss_ota=1\n","\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n","\n","                 from  n    params  module                                  arguments                     \n","  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n","  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n","  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n","  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n","  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n","  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n","  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n"," 12                -1  1         0  models.common.MP                        []                            \n"," 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n"," 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n"," 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n"," 25                -1  1         0  models.common.MP                        []                            \n"," 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n"," 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n"," 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n"," 38                -1  1         0  models.common.MP                        []                            \n"," 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n"," 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n"," 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n"," 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n"," 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n"," 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n"," 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n"," 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n"," 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n"," 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n"," 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n"," 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n"," 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n"," 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n"," 76                -1  1         0  models.common.MP                        []                            \n"," 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n"," 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n"," 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n"," 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n"," 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n"," 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n"," 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n"," 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n"," 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n"," 89                -1  1         0  models.common.MP                        []                            \n"," 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n"," 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n"," 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n"," 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n"," 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n"," 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n"," 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n"," 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n","100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n","101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n","102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              \n","103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              \n","104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             \n","105   [102, 103, 104]  1     44944  models.yolo.IDetect                     [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n","Model Summary: 415 layers, 37207344 parameters, 37207344 gradients\n","\n","Transferred 552/566 items from yolov7.pt\n","Scaled weight_decay = 0.0005\n","Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'data/fish/train.cache' images and labels... 640 found, 1 missing, 0 empty, 0 corrupted: 100% 641/641 [00:00<?, ?it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mScanning 'data/fish/val.cache' images and labels... 160 found, 0 missing, 0 empty, 0 corrupted: 100% 160/160 [00:00<?, ?it/s]\n","\n","\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 5.66, Best Possible Recall (BPR) = 1.0000\n","Image sizes 640 train, 640 test\n","Using 2 dataloader workers\n","Logging results to runs/train/yolov7-fish4\n","Starting training for 100 epochs...\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      0/99    0.958G   0.06955   0.01966   0.02138    0.1106         4       640: 100% 41/41 [11:53<00:00, 17.40s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:   0% 0/5 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n","  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:54<00:00, 10.81s/it]\n","                 all         160         477     0.00752       0.266     0.00438    0.000785\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      1/99     13.4G     0.056   0.01921   0.01489    0.0901         2       640: 100% 41/41 [10:21<00:00, 15.17s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:55<00:00, 11.09s/it]\n","                 all         160         477       0.196        0.49       0.205      0.0439\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      2/99     13.4G   0.05258   0.01616   0.01005    0.0788         7       640: 100% 41/41 [10:44<00:00, 15.72s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:58<00:00, 11.62s/it]\n","                 all         160         477       0.506       0.826       0.644       0.208\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      3/99     13.4G   0.04874   0.01304  0.007283   0.06906         8       640: 100% 41/41 [10:52<00:00, 15.92s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:58<00:00, 11.75s/it]\n","                 all         160         477        0.52        0.71       0.538       0.171\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      4/99     13.4G   0.06017   0.01107  0.008799   0.08004         6       640: 100% 41/41 [11:11<00:00, 16.38s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:50<00:00, 10.15s/it]\n","                 all         160         477       0.618       0.743       0.722       0.289\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      5/99     13.4G   0.05242   0.01221  0.009054   0.07368         4       640: 100% 41/41 [11:14<00:00, 16.45s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:54<00:00, 10.83s/it]\n","                 all         160         477       0.317        0.51       0.367       0.124\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      6/99     13.4G   0.05031   0.01146  0.006522   0.06829        10       640: 100% 41/41 [11:09<00:00, 16.34s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:57<00:00, 11.41s/it]\n","                 all         160         477       0.536       0.633       0.597       0.206\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      7/99     13.4G   0.05256    0.0113  0.007958   0.07182         3       640: 100% 41/41 [11:34<00:00, 16.93s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:58<00:00, 11.61s/it]\n","                 all         160         477      0.0831      0.0903      0.0396     0.00924\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      8/99     13.4G   0.05412   0.01239  0.007514   0.07403         4       640: 100% 41/41 [11:21<00:00, 16.62s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:55<00:00, 11.09s/it]\n","                 all         160         477        0.35       0.195       0.153      0.0755\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","      9/99     13.4G   0.05468   0.01288  0.008852   0.07641        14       640: 100% 41/41 [11:27<00:00, 16.76s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:54<00:00, 10.96s/it]\n","                 all         160         477      0.0789       0.199      0.0227     0.00658\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     10/99     13.4G   0.05418   0.01519  0.009549   0.07893        10       640: 100% 41/41 [11:22<00:00, 16.64s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:55<00:00, 11.13s/it]\n","                 all         160         477    3.82e-05     0.00417    1.08e-06    1.08e-07\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     11/99     13.4G   0.05705   0.01412  0.007799   0.07897         4       640: 100% 41/41 [11:41<00:00, 17.10s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [01:01<00:00, 12.24s/it]\n","                 all         160         477      0.0295        0.27      0.0159     0.00583\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     12/99     13.4G     0.058   0.01271  0.007889    0.0786         4       640: 100% 41/41 [10:54<00:00, 15.97s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:54<00:00, 10.85s/it]\n","                 all         160         477       0.535       0.523       0.477       0.265\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     13/99     13.4G   0.05764   0.01214  0.006766   0.07655         5       640: 100% 41/41 [11:23<00:00, 16.67s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:51<00:00, 10.29s/it]\n","                 all         160         477       0.587       0.625       0.626       0.315\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     14/99     13.4G   0.04956   0.01227  0.005329   0.06715        14       640: 100% 41/41 [11:30<00:00, 16.84s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:59<00:00, 11.98s/it]\n","                 all         160         477       0.848        0.77       0.856       0.475\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     15/99     13.4G   0.04671   0.01072   0.00586   0.06329         2       640: 100% 41/41 [11:42<00:00, 17.14s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 5/5 [00:56<00:00, 11.25s/it]\n","                 all         160         477       0.895       0.823       0.911       0.552\n","\n","     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n","     16/99     13.4G    0.0414    0.0102  0.004334   0.05593         8       640: 100% 41/41 [11:44<00:00, 17.18s/it]\n","               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:  80% 4/5 [00:03<00:00,  1.02it/s]"]}]},{"cell_type":"code","source":[],"metadata":{"id":"NQKA4hYUX-CE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 8. 預測影像"],"metadata":{"id":"dAiCCP5SE5TR"}},{"cell_type":"code","source":["!python detect.py --weight /content/drive/MyDrive/高科大合作案/ObjectDetection/yolov7/runs/train/yolov7-fish4/weights/best.pt --conf 0.4 --img -size 640 --source ./data/fish/test/1.jpg --no-trace"],"metadata":{"id":"fRG73MdTE6zT"},"execution_count":null,"outputs":[]}]}