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UltralyticsAssistant
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README.md
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README.md
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@ -107,15 +107,15 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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```bash
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python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/LNwODJXcvt4' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/LNwODJXcvt4' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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```
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</details>
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@ -127,10 +127,10 @@ The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5
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```bash
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python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
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yolov5s 64
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yolov5m 40
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yolov5l 24
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yolov5x 16
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yolov5s 64
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yolov5m 40
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yolov5l 24
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yolov5x 16
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```
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<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
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@ -105,15 +105,15 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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```bash
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python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/LNwODJXcvt4' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/LNwODJXcvt4' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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```
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</details>
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@ -126,10 +126,10 @@ python detect.py --weights yolov5s.pt --source 0 #
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```bash
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python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
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yolov5s 64
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yolov5m 40
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yolov5l 24
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yolov5x 16
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yolov5s 64
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yolov5m 40
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yolov5l 24
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yolov5x 16
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```
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<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
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@ -45,7 +45,7 @@ That's it! You're done 😎
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To enable ClearML experiment tracking, simply install the ClearML pip package.
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```bash
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pip install clearml>=1.2.0
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pip install clearml > =1.2.0
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```
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This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
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@ -144,7 +144,7 @@ clearml-data close
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Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
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```bash
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python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
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python train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache < your_dataset_id > --weights
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```
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## 👀 Hyperparameter Optimization
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@ -177,7 +177,7 @@ In short: every experiment tracked by the experiment manager contains enough inf
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You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
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```bash
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clearml-agent daemon --queue <queues_to_listen_to> [--docker]
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clearml-agent daemon --queue < queues_to_listen_to > [--docker]
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```
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### Cloning, Editing And Enqueuing
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@ -102,12 +102,12 @@ Logging Models to Comet is disabled by default. To enable it, pass the `save-per
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```shell
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python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--save-period 1
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--save-period 1
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```
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## Logging Model Predictions
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@ -122,12 +122,12 @@ Here is an [example project using the Panel](https://www.comet.com/examples/come
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```shell
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python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--bbox_interval 2
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--bbox_interval 2
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```
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### Controlling the number of Prediction Images logged to Comet
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@ -136,12 +136,12 @@ When logging predictions from YOLOv5, Comet will log the images associated with
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```shell
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env COMET_MAX_IMAGE_UPLOADS=200 python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--bbox_interval 1
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--bbox_interval 1
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```
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### Logging Class Level Metrics
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@ -150,11 +150,11 @@ Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision
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```shell
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env COMET_LOG_PER_CLASS_METRICS=true python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt
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```
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## Uploading a Dataset to Comet Artifacts
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@ -165,12 +165,12 @@ The dataset be organized in the way described in the [YOLOv5 documentation](http
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```shell
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python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--upload_dataset
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data coco128.yaml \
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--weights yolov5s.pt \
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--upload_dataset
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```
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You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
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@ -192,11 +192,11 @@ Then pass this file to your training script in the following way
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```shell
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python train.py \
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data artifact.yaml \
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--weights yolov5s.pt
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--img 640 \
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--batch 16 \
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--epochs 5 \
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--data artifact.yaml \
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--weights yolov5s.pt
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```
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Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. <img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
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@ -211,7 +211,7 @@ This will restore the run to its state before the interruption, which includes r
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```shell
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python train.py \
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--resume "comet://<your run path>"
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--resume "comet://<your run path>"
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```
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## Hyperparameter Search with the Comet Optimizer
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