Fix getting started yolov5 sparse transfer command

Author: markurtz

src/content/get-started/transfer-a-sparsified-model/cv-object-detection.mdx

@@ -30,21 +30,21 @@ After the command completes, the trained model will reamin sparse, achieve an mA
 
 ```bash
 $ sparseml.yolov5.train \
-    --project yolov5l \
-    --name sparsified \
-    --data VOC.yaml \
+    --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95?recipe_type=transfer_learn \
+    --recipe zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95?recipe_type=transfer_learn \
     --cfg models_v5.0/yolov5l.yaml \
-    --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95?recipe_type=transfer \
     --hyp data/hyps/hyp.finetune.yaml \
-    --recipe recipes/yolov5.transfer_learn_pruned_quantized.md
+    --data VOC.yaml \
+    --project yolov5l \
+    --name sparsified
 ```
 
 The most important arguments are `--data`, `--weights`, and `--recipe`:
 - `--data` specifies the dataset onto which the model will be fine-tuned
 - `--weights` specifies the base model used to start the transfer learning process (can be a SparseZoo stub or local custom model path)
 - `--recipe` specifies the hyperparameters of the fine-tuning process (can be a SparseZoo stub or a local custom recipe)
 
-To utilize your own dataset, set up the appropriate image dataset structure and pass the path as the `--data` argument. 
+To utilize your own dataset, set up the appropriate image dataset structure and pass the path as the `--data` argument.
 An example for VOC is on [GitHub](https://github.com/neuralmagic/sparseml/blob/main/src/sparseml/yolov5/data/VOC.yaml).
 
 The `--cfg` and `--hyp` are configuration files. You can checkout the examples on [GitHub](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/yolov5).