From a91a8610cd119dd998c87106c6b0286051bf97eb Mon Sep 17 00:00:00 2001 From: Mark Kurtz Date: Thu, 29 Jul 2021 10:38:31 -0400 Subject: [PATCH] YOLOv5 Sparse Transfer Learning Command Fixes --- .../tutorials/yolov5_sparse_transfer_learning.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md b/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md index dae5d79508a..0d1502a292e 100644 --- a/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md +++ b/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md @@ -117,11 +117,11 @@ The recipes are specific to the sparsification type, so the training command wil 1) Select the proper command to run based on the model and the sparsification type of the model you chose earlier. - YOLOv5s Pruned transfer learning: ```bash - python train.py --data voc.yaml --cfg ../models/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned-aggressive_96 --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5s.transfer_learn_pruned.md + python train.py --data voc.yaml --cfg ../models/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned-aggressive_96?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned.md ``` - YOLOv5s Pruned-Quantized transfer learning: ```bash - python train.py --data voc.yaml --cfg ../models/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94 --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5s.transfer_learn_pruned_quantized.md + python train.py --data voc.yaml --cfg ../models/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md ``` - YOLOv5s Baseline transfer learning: ```bash @@ -129,11 +129,11 @@ The recipes are specific to the sparsification type, so the training command wil ``` - YOLOv5l Pruned transfer learning: ```bash - python train.py --data voc.yaml --cfg ../models/yolov5l.yaml --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned-aggressive_98 --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5l.transfer_learn_pruned.md + python train.py --data voc.yaml --cfg ../models/yolov5l.yaml --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned-aggressive_98?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned.md ``` - YOLOv5l Pruned-Quantized transfer learning: ```bash - python train.py --data voc.yaml --cfg ../models/yolov5l.yaml --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95 --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5l.transfer_learn_pruned_quantized.md + python train.py --data voc.yaml --cfg ../models/yolov5l.yaml --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md ``` - YOLOv5l Baseline transfer learning: ```bash