Unified '/project/name' results saving (#1377)
* Project/name update * Update ci-testing.yml * address project with path separator failure mode * Project/name update * address project with path separator failure mode * Update ci-testing.yml * detect.py default --name bug fix * missing rstrip PR * train/exp0 to train/exp
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Glenn Jocher authored and GitHub committed
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@@ -597,7 +597,7 @@
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},
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"source": [
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"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
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"Image(filename='runs/detect/exp0/zidane.jpg', width=600)"
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"Image(filename='runs/detect/exp/zidane.jpg', width=600)"
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],
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"execution_count": null,
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"outputs": [
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@@ -611,7 +611,7 @@
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"Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients\n",
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"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n",
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"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n",
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"Results saved to runs/detect/exp0\n",
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"Results saved to runs/detect/exp\n",
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"Done. (0.113s)\n"
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],
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"name": "stdout"
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@@ -887,7 +887,7 @@
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"source": [
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"Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n",
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"\n",
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"All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp0`, `runs/train/exp1` etc.\n"
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"All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n"
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]
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},
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{
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@@ -969,7 +969,7 @@
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"Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
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"Image sizes 640 train, 640 test\n",
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"Using 2 dataloader workers\n",
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"Logging results to runs/train/exp0\n",
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"Logging results to runs/train/exp\n",
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"Starting training for 3 epochs...\n",
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"\n",
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" Epoch gpu_mem box obj cls total targets img_size\n",
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@@ -986,8 +986,8 @@
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" 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",
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" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 2.78it/s]\n",
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" all 128 929 0.395 0.766 0.701 0.455\n",
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"Optimizer stripped from runs/train/exp0/weights/last.pt, 15.2MB\n",
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"Optimizer stripped from runs/train/exp0/weights/best.pt, 15.2MB\n",
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"Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n",
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"Optimizer stripped from runs/train/exp/weights/best.pt, 15.2MB\n",
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"3 epochs completed in 0.005 hours.\n",
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"\n"
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],
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@@ -1030,7 +1030,7 @@
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"source": [
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"## Local Logging\n",
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"\n",
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"All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp0`, `runs/train/exp1`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
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"All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
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]
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},
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{
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@@ -1039,9 +1039,9 @@
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"id": "riPdhraOTCO0"
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},
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"source": [
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"Image(filename='runs/train/exp0/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
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"Image(filename='runs/train/exp0/test_batch0_labels.jpg', width=800) # test batch 0 labels\n",
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"Image(filename='runs/train/exp0/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
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"Image(filename='runs/train/exp/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
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"Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800) # test batch 0 labels\n",
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"Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
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],
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"execution_count": null,
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"outputs": []
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@@ -1078,7 +1078,7 @@
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},
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"source": [
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"from utils.utils import plot_results \n",
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"plot_results(save_dir='runs/train/exp0') # plot results.txt as results.png\n",
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"plot_results(save_dir='runs/train/exp') # plot results.txt as results.png\n",
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"Image(filename='results.png', width=800) "
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],
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"execution_count": null,
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@@ -1170,9 +1170,9 @@
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" for di in 0 cpu # inference devices\n",
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" do\n",
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" python detect.py --weights $x.pt --device $di # detect official\n",
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" python detect.py --weights runs/train/exp0/weights/last.pt --device $di # detect custom\n",
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" python detect.py --weights runs/train/exp/weights/last.pt --device $di # detect custom\n",
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" python test.py --weights $x.pt --device $di # test official\n",
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" python test.py --weights runs/train/exp0/weights/last.pt --device $di # test custom\n",
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" python test.py --weights runs/train/exp/weights/last.pt --device $di # test custom\n",
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" done\n",
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" python models/yolo.py --cfg $x.yaml # inspect\n",
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" python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",
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