Creado con Colaboratory

tutorial.ipynb

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       "cell_type": "code",
       "metadata": {
         "id": "wbvMlHd_QwMG",
-        "outputId": "7e5e09a2-892e-4999-9e6c-567ea329eb38",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
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+        "outputId": "888d5c41-00e9-47d8-d230-dded99325bea"
       },
       "source": [
         "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
@@ -563,7 +563,7 @@
         "clear_output()\n",
         "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
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       "cell_type": "code",
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           "height": 534
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+        "outputId": "c9a308f7-2216-4805-8003-eca8dd0dc30d"
       },
       "source": [
         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
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       "source": [
         "# Download COCO val2017\n",
         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
         "!unzip -q tmp.zip -d ../ && rm tmp.zip"
       ],
-      "execution_count": null,
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       "outputs": [
         {
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       "cell_type": "code",
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-        "outputId": "30601131-102c-4702-fc13-572cc6087330",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79"
       },
       "source": [
         "# Run YOLOv5x on COCO val2017\n",
         "!python test.py --weights yolov5x.pt --data coco.yaml --img 640"
       ],
-      "execution_count": null,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_conf=False, save_dir='runs/test', save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
+            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
             "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
             "\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
-            "100% 170M/170M [00:05<00:00, 32.2MB/s]\n",
+            "100% 170M/170M [00:05<00:00, 32.6MB/s]\n",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 284 layers, 8.89222e+07 parameters, 0 gradients\n",
-            "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 16239.02it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:22<00:00,  1.89it/s]\n",
-            "                 all       5e+03    3.63e+04       0.409       0.754       0.672       0.483\n",
-            "Speed: 5.9/2.0/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
+            "Model Summary: 484 layers, 88922205 parameters, 0 gradients\n",
+            "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n",
+            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:30<00:00,  1.74it/s]\n",
+            "                 all       5e+03    3.63e+04       0.409       0.754       0.672       0.484\n",
+            "Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
             "\n",
-            "COCO mAP with pycocotools... saving runs/test/detections_val2017_yolov5x_results.json...\n",
+            "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
             "loading annotations into memory...\n",
-            "Done (t=0.73s)\n",
+            "Done (t=0.43s)\n",
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=4.48s)\n",
+            "DONE (t=4.67s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=86.55s).\n",
+            "DONE (t=92.11s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=13.15s).\n",
+            "DONE (t=13.24s).\n",
             " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.492\n",
             " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.676\n",
             " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.534\n",
@@ -773,7 +773,7 @@
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
-            "Results saved to runs/test\n"
+            "Results saved to runs/test/exp\n"
           ],
           "name": "stdout"
         }
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       "source": [
         "# Download COCO128\n",
         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
         "!unzip -q tmp.zip -d ../ && rm tmp.zip"
       ],
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b434b178be4b41b3881e237e19f49b45",
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@@ -907,27 +907,29 @@
       "cell_type": "code",
       "metadata": {
         "id": "1NcFxRcFdJ_O",
-        "outputId": "a98e611d-979b-4e8c-d61c-8e219958ed33",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "outputId": "138f2d1d-364c-405a-cf13-ea91a2aff915"
       },
       "source": [
         "# Train YOLOv5s on COCO128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
       ],
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
             "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
             "\n",
-            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=10, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
-            "Start Tensorboard with \"tensorboard --logdir runs/\", view at http://localhost:6006/\n",
-            "2020-11-05 16:39:40.555423: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
-            "Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)\n",
+            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
+            "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
+            "2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
             "Hyperparameters {'lr0': 0.01, 'lrf': 0.2, '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}\n",
+            "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...\n",
+            "100% 14.5M/14.5M [00:01<00:00, 14.8MB/s]\n",
+            "\n",
             "\n",
             "                 from  n    params  module                                  arguments                     \n",
             "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
@@ -955,15 +957,15 @@
             " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
             " 23                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          \n",
             " 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
-            "Model Summary: 191 layers, 7.46816e+06 parameters, 7.46816e+06 gradients\n",
+            "Model Summary: 283 layers, 7468157 parameters, 7468157 gradients\n",
             "\n",
             "Transferred 370/370 items from yolov5s.pt\n",
             "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
-            "Scanning images: 100% 128/128 [00:00<00:00, 5375.86it/s]\n",
-            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 15114.61it/s]\n",
-            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 141.99it/s]\n",
-            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 12572.50it/s]\n",
-            "Caching images (0.1GB): 100% 128/128 [00:01<00:00, 79.68it/s] \n",
+            "Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n",
+            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n",
+            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n",
+            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n",
+            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n",
             "NumExpr defaulting to 2 threads.\n",
             "\n",
             "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
@@ -973,22 +975,21 @@
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       0/2     3.22G   0.04202   0.06746   0.01503    0.1245       194       640: 100% 8/8 [00:03<00:00,  2.51it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:03<00:00,  2.42it/s]\n",
-            "                 all         128         929       0.405       0.762       0.701       0.449\n",
+            "       0/2     5.24G   0.04202   0.06745   0.01503    0.1245       194       640: 100% 8/8 [00:03<00:00,  2.01it/s]\n",
+            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:03<00:00,  2.40it/s]\n",
+            "                 all         128         929       0.404       0.758       0.701        0.45\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       1/2     3.17G   0.04461   0.05873   0.01689    0.1202       142       640: 100% 8/8 [00:01<00:00,  4.14it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:01<00:00,  6.49it/s]\n",
-            "                 all         128         929       0.402       0.772       0.703       0.452\n",
+            "       1/2     5.12G   0.04461   0.05874    0.0169    0.1202       142       640: 100% 8/8 [00:01<00:00,  4.14it/s]\n",
+            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:01<00:00,  5.75it/s]\n",
+            "                 all         128         929       0.403       0.772       0.703       0.453\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       2/2     3.17G   0.04445   0.06545   0.01666    0.1266       149       640: 100% 8/8 [00:01<00:00,  4.33it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:02<00:00,  2.78it/s]\n",
-            "                 all         128         929       0.395       0.766       0.701       0.455\n",
+            "       2/2     5.12G   0.04445   0.06545   0.01667    0.1266       149       640: 100% 8/8 [00:01<00:00,  4.15it/s]\n",
+            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:06<00:00,  1.18it/s]\n",
+            "                 all         128         929       0.395       0.767       0.702       0.452\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n",
-            "Optimizer stripped from runs/train/exp/weights/best.pt, 15.2MB\n",
-            "3 epochs completed in 0.005 hours.\n",
+            "3 epochs completed in 0.006 hours.\n",
             "\n"
           ],
           "name": "stdout"
@@ -1140,10 +1141,9 @@
         "id": "mcKoSIK2WSzj"
       },
       "source": [
-        "# Test all models\n",
+        "# Test all\n",
         "%%shell\n",
-        "for x in s m l x\n",
-        "do\n",
+        "for x in s m l x; do\n",
         "  python test.py --weights yolov5$x.pt --data coco.yaml --img 640\n",
         "done"
       ],
@@ -1156,26 +1156,22 @@
         "id": "FGH0ZjkGjejy"
       },
       "source": [
-        "# Run unit tests\n",
+        "# Unit tests\n",
         "%%shell\n",
-        "# git clone https://github.com/ultralytics/yolov5\n",
-        "# cd yolov5\n",
-        "pip install -qr requirements.txt onnx\n",
+        "export PYTHONPATH=\"$PWD\"  # to run *.py. files in subdirectories\n",
         "\n",
-        "export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n",
-        "for x in yolov5s #yolov5m yolov5l yolov5x # models\n",
-        "do\n",
-        "  rm -rf runs\n",
-        "  python train.py --weights $x.pt --cfg $x.yaml --epochs 3 --img 320 --device 0  # train\n",
-        "  for di in 0 cpu # inference devices\n",
-        "  do\n",
-        "    python detect.py --weights $x.pt --device $di  # detect official\n",
-        "    python detect.py --weights runs/train/exp/weights/last.pt --device $di  # detect custom\n",
-        "    python test.py --weights $x.pt --device $di # test official\n",
-        "    python test.py --weights runs/train/exp/weights/last.pt --device $di # test custom\n",
+        "for m in yolov5s; do  # models\n",
+        "  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained\n",
+        "  python train.py --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch\n",
+        "  for d in 0 cpu; do  # devices\n",
+        "    python detect.py --weights $m.pt --device $d  # detect official\n",
+        "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
+        "    python test.py --weights $m.pt --device $d # test official\n",
+        "    python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n",
         "  done\n",
-        "  python models/yolo.py --cfg $x.yaml # inspect\n",
-        "  python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",
+        "  python hubconf.py  # hub\n",
+        "  python models/yolo.py --cfg $m.yaml  # inspect\n",
+        "  python models/export.py --weights $m.pt --img 640 --batch 1  # export\n",
         "done"
       ],
       "execution_count": null,