-
Notifications
You must be signed in to change notification settings - Fork 167
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
a847009
commit e67e4ac
Showing
3 changed files
with
343 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| clients: | ||
| # Type | ||
| type: mistnet | ||
|
|
||
| # The total number of clients | ||
| total_clients: 1 | ||
|
|
||
| # The number of clients selected in each round | ||
| per_round: 1 | ||
|
|
||
| # Should the clients compute test accuracy locally? | ||
| do_test: false | ||
|
|
||
| server: | ||
| type: mistnet | ||
|
|
||
| address: 127.0.0.1 | ||
| port: 7363 | ||
|
|
||
| data: | ||
| # The training and testing dataset | ||
| datasource: YOLO | ||
| data_params: packages/yolov5/yolov5/data/coco128.yaml | ||
|
|
||
| # Where the dataset is located | ||
| data_path: ./data/COCO | ||
| train_path: ./data/COCO/coco128/images/train2017/ | ||
| test_path: ./data/COCO/coco128/images/train2017/ | ||
|
|
||
| # download command/URL (optional) | ||
| download_urls: | ||
| [ | ||
| "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip", | ||
| ] | ||
|
|
||
| # number of training examples | ||
| num_train_examples: 128 | ||
|
|
||
| # number of testing examples | ||
| num_test_examples: 128 | ||
|
|
||
| # number of classes | ||
| num_classes: 80 | ||
|
|
||
| # image size | ||
| image_size: 640 | ||
|
|
||
| # class names | ||
| classes: | ||
| [ | ||
| "person", | ||
| "bicycle", | ||
| "car", | ||
| "motorcycle", | ||
| "airplane", | ||
| "bus", | ||
| "train", | ||
| "truck", | ||
| "boat", | ||
| "traffic light", | ||
| "fire hydrant", | ||
| "stop sign", | ||
| "parking meter", | ||
| "bench", | ||
| "bird", | ||
| "cat", | ||
| "dog", | ||
| "horse", | ||
| "sheep", | ||
| "cow", | ||
| "elephant", | ||
| "bear", | ||
| "zebra", | ||
| "giraffe", | ||
| "backpack", | ||
| "umbrella", | ||
| "handbag", | ||
| "tie", | ||
| "suitcase", | ||
| "frisbee", | ||
| "skis", | ||
| "snowboard", | ||
| "sports ball", | ||
| "kite", | ||
| "baseball bat", | ||
| "baseball glove", | ||
| "skateboard", | ||
| "surfboard", | ||
| "tennis racket", | ||
| "bottle", | ||
| "wine glass", | ||
| "cup", | ||
| "fork", | ||
| "knife", | ||
| "spoon", | ||
| "bowl", | ||
| "banana", | ||
| "apple", | ||
| "sandwich", | ||
| "orange", | ||
| "broccoli", | ||
| "carrot", | ||
| "hot dog", | ||
| "pizza", | ||
| "donut", | ||
| "cake", | ||
| "chair", | ||
| "couch", | ||
| "potted plant", | ||
| "bed", | ||
| "dining table", | ||
| "toilet", | ||
| "tv", | ||
| "laptop", | ||
| "mouse", | ||
| "remote", | ||
| "keyboard", | ||
| "cell phone", | ||
| "microwave", | ||
| "oven", | ||
| "toaster", | ||
| "sink", | ||
| "refrigerator", | ||
| "book", | ||
| "clock", | ||
| "vase", | ||
| "scissors", | ||
| "teddy bear", | ||
| "hair drier", | ||
| "toothbrush", | ||
| ] | ||
|
|
||
| # Number of samples in each partition | ||
| partition_size: 128 | ||
|
|
||
| # IID or non-IID? | ||
| sampler: iid | ||
|
|
||
| trainer: | ||
| # The type of the trainer | ||
| type: yolov5 | ||
|
|
||
| # The maximum number of training rounds | ||
| rounds: 1 | ||
|
|
||
| # Whether the training should use multiple GPUs if available | ||
| parallelized: false | ||
|
|
||
| # The maximum number of clients running concurrently | ||
| max_concurrency: 3 | ||
|
|
||
| # The target accuracy | ||
| target_accuracy: 0.99 | ||
|
|
||
| # Number of epoches for local training in each communication round | ||
| epochs: 500 | ||
| batch_size: 16 | ||
| optimizer: SGD | ||
| linear_lr: false | ||
|
|
||
| # The machine learning model | ||
| model_name: yolov5 | ||
| model_config: packages/yolov5/yolov5/models/yolov5s.yaml | ||
| train_params: packages/yolov5/yolov5/data/hyp.scratch.yaml | ||
|
|
||
| algorithm: | ||
| # Aggregation algorithm | ||
| type: mistnet | ||
|
|
||
| cut_layer: 4 | ||
| epsilon: 100 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| clients: | ||
| # Type | ||
| type: mistnet | ||
|
|
||
| # The total number of clients | ||
| total_clients: 0 | ||
|
|
||
| # The number of clients selected in each round | ||
| per_round: 1 | ||
|
|
||
| # Should the clients compute test accuracy locally? | ||
| do_test: false | ||
|
|
||
| server: | ||
| type: mistnet | ||
|
|
||
| address: 127.0.0.1 | ||
| port: 7363 | ||
|
|
||
| data: | ||
| # The training and testing dataset | ||
| datasource: YOLO | ||
| data_params: packages/yolov5/yolov5/data/coco128.yaml | ||
|
|
||
| # Where the dataset is located | ||
| data_path: ./data/COCO | ||
| train_path: ./data/COCO/coco128/images/train2017/ | ||
| test_path: ./data/COCO/coco128/images/train2017/ | ||
|
|
||
| # download command/URL (optional) | ||
| download_urls: | ||
| [ | ||
| "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip", | ||
| ] | ||
|
|
||
| # number of training examples | ||
| num_train_examples: 128 | ||
|
|
||
| # number of testing examples | ||
| num_test_examples: 128 | ||
|
|
||
| # number of classes | ||
| num_classes: 80 | ||
|
|
||
| # image size | ||
| image_size: 640 | ||
|
|
||
| # class names | ||
| classes: | ||
| [ | ||
| "person", | ||
| "bicycle", | ||
| "car", | ||
| "motorcycle", | ||
| "airplane", | ||
| "bus", | ||
| "train", | ||
| "truck", | ||
| "boat", | ||
| "traffic light", | ||
| "fire hydrant", | ||
| "stop sign", | ||
| "parking meter", | ||
| "bench", | ||
| "bird", | ||
| "cat", | ||
| "dog", | ||
| "horse", | ||
| "sheep", | ||
| "cow", | ||
| "elephant", | ||
| "bear", | ||
| "zebra", | ||
| "giraffe", | ||
| "backpack", | ||
| "umbrella", | ||
| "handbag", | ||
| "tie", | ||
| "suitcase", | ||
| "frisbee", | ||
| "skis", | ||
| "snowboard", | ||
| "sports ball", | ||
| "kite", | ||
| "baseball bat", | ||
| "baseball glove", | ||
| "skateboard", | ||
| "surfboard", | ||
| "tennis racket", | ||
| "bottle", | ||
| "wine glass", | ||
| "cup", | ||
| "fork", | ||
| "knife", | ||
| "spoon", | ||
| "bowl", | ||
| "banana", | ||
| "apple", | ||
| "sandwich", | ||
| "orange", | ||
| "broccoli", | ||
| "carrot", | ||
| "hot dog", | ||
| "pizza", | ||
| "donut", | ||
| "cake", | ||
| "chair", | ||
| "couch", | ||
| "potted plant", | ||
| "bed", | ||
| "dining table", | ||
| "toilet", | ||
| "tv", | ||
| "laptop", | ||
| "mouse", | ||
| "remote", | ||
| "keyboard", | ||
| "cell phone", | ||
| "microwave", | ||
| "oven", | ||
| "toaster", | ||
| "sink", | ||
| "refrigerator", | ||
| "book", | ||
| "clock", | ||
| "vase", | ||
| "scissors", | ||
| "teddy bear", | ||
| "hair drier", | ||
| "toothbrush", | ||
| ] | ||
|
|
||
| # Number of samples in each partition | ||
| partition_size: 128 | ||
|
|
||
| # IID or non-IID? | ||
| sampler: iid | ||
|
|
||
| trainer: | ||
| # The type of the trainer | ||
| type: yolov5 | ||
|
|
||
| # The maximum number of training rounds | ||
| rounds: 1 | ||
|
|
||
| # Whether the training should use multiple GPUs if available | ||
| parallelized: false | ||
|
|
||
| # The maximum number of clients running concurrently | ||
| max_concurrency: 3 | ||
|
|
||
| # The target accuracy | ||
| target_accuracy: 0.99 | ||
|
|
||
| # Number of epoches for local training in each communication round | ||
| epochs: 500 | ||
| batch_size: 16 | ||
| optimizer: SGD | ||
| linear_lr: false | ||
|
|
||
| # The machine learning model | ||
| model_name: yolov5 | ||
| model_config: packages/yolov5/yolov5/models/yolov5s.yaml | ||
| train_params: packages/yolov5/yolov5/data/hyp.scratch.yaml | ||
|
|
||
| algorithm: | ||
| # Aggregation algorithm | ||
| type: mistnet | ||
|
|
||
| cut_layer: 4 | ||
| epsilon: 100 |
Submodule plato
updated
from 997835 to 677b53