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update weights size and correct the references
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mihir135 committed Sep 20, 2021
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Expand Up @@ -150,12 +150,12 @@ The table below compares these tradeoffs and shows how to run them on the COCO d

| Recipe Name | Description | Train Command | COCO mAP@0.5 | Size on Disk | DeepSparse Performance** |
|----------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|--------------|--------------------------|
| YOLOv5s Baseline | The baseline, small YOLOv5 model used as the starting point for sparsification. | ``` python train.py --cfg ../models/yolov5s.yaml --weights "" --data coco.yaml --hyp data/hyp.scratch.yaml ``` | 0.556 | 154 MB | 78.2 img/sec |
| [YOLOv5s Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5s.pruned.md) | Creates a highly sparse, FP32 YOLOv5s model that recovers close to the baseline model. | ``` python train.py --cfg ../models/yolov5s.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5s.pruned.md ``` | 0.534 | 32.8 MB | 100.5 img/sec |
| [YOLOv5s Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5s.pruned_quantized.md) | Creates a highly sparse, INT8 YOLOv5s model that recovers reasonably close to the baseline model. | ``` python train.py --cfg ../models/yolov5s.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5s.pruned_quantized.md ``` | 0.525 | 12.7 MB | 198.2 img/sec |
| YOLOv5l Baseline | The baseline, large YOLOv5 model used as the starting point for sparsification. | ``` python train.py --cfg ../models/yolov5l.yaml --weights "" --data coco.yaml --hyp data/hyp.scratch.yaml ``` | 0.654 | 24.8 MB | 22.7 img/sec |
| [YOLOv5l Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5l.pruned.md) | Creates a highly sparse, FP32 YOLOv5l model that recovers close to the baseline model. | ``` python train.py --cfg ../models/yolov5l.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5l.pruned.md ``` | 0.643 | 8.4 MB | 40.1 img/sec |
| [YOLOv5l Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5l.pruned_quantized.md) | Creates a highly sparse, INT8 YOLOv5l model that recovers reasonably close to the baseline model. | ``` python train.py --cfg ../models/yolov5l.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5l.pruned_quantized.md ``` | 0.623 | 3.3 MB | 98.6 img/sec |
| YOLOv5s Baseline | The baseline, small YOLOv5 model used as the starting point for sparsification. | ``` python train.py --cfg ../models/yolov5s.yaml --weights "" --data coco.yaml --hyp data/hyp.scratch.yaml ``` | 0.556 | 24.8 MB | 78.2 img/sec |
| [YOLOv5s Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5s.pruned.md) | Creates a highly sparse, FP32 YOLOv5s model that recovers close to the baseline model. | ``` python train.py --cfg ../models/yolov5s.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5s.pruned.md ``` | 0.534 | 8.4 MB | 100.5 img/sec |
| [YOLOv5s Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5s.pruned_quantized.md) | Creates a highly sparse, INT8 YOLOv5s model that recovers reasonably close to the baseline model. | ``` python train.py --cfg ../models/yolov5s.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5s.pruned_quantized.md ``` | 0.525 | 3.3 MB | 198.2 img/sec |
| YOLOv5l Baseline | The baseline, large YOLOv5 model used as the starting point for sparsification. | ``` python train.py --cfg ../models/yolov5l.yaml --weights "" --data coco.yaml --hyp data/hyp.scratch.yaml ``` | 0.654 | 154 MB | 22.7 img/sec |
| [YOLOv5l Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5l.pruned.md) | Creates a highly sparse, FP32 YOLOv5l model that recovers close to the baseline model. | ``` python train.py --cfg ../models/yolov5l.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5l.pruned.md ``` | 0.643 | 32.8 MB | 40.1 img/sec |
| [YOLOv5l Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/recipes/yolov5l.pruned_quantized.md) | Creates a highly sparse, INT8 YOLOv5l model that recovers reasonably close to the baseline model. | ``` python train.py --cfg ../models/yolov5l.yaml --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data coco.yaml --hyp data/hyp.scratch.yaml --recipe ../recipes/yolov5l.pruned_quantized.md ``` | 0.623 | 12.7 MB | 98.6 img/sec |

** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.6 of the DeepSparse Engine.

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