Fix object detection guide
Author: SangbumChoi
Pull request: yes

# What does this PR do?

Even though this change makes simple line of two codes into 4 lines with seperated lines. It will reduce time since it does not requires to do twice for loop and no additional requirements of memory space.

How do you think about it? @qubvel 
You can just close this PR if you prefer simplicity :)

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Comments:
FYI, this is another topic but running 
```
python run_object_detection.py \
    --model_name_or_path facebook/detr-resnet-50 \
    --dataset_name cppe-5 \
    --do_train true \
    --do_eval true \
    --output_dir detr-finetuned-cppe-5-10k-steps \
    --num_train_epochs 100 \
    --image_square_size 600 \
    --learning_rate 5e-5 \
    --weight_decay 1e-4 \
    --dataloader_num_workers 4 \
    --dataloader_prefetch_factor 2 \
    --per_device_train_batch_size 8 \
    --gradient_accumulation_steps 1 \
    --remove_unused_columns false \
    --eval_do_concat_batches false \
    --ignore_mismatched_sizes true \
    --metric_for_best_model eval_map \
    --greater_is_better true \
    --load_best_model_at_end true \
    --logging_strategy epoch \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --save_total_limit 2 \
    --push_to_hub true \
    --push_to_hub_model_id detr-finetuned-cppe-5-10k-steps \
    --hub_strategy end \
    --seed 1337

```
returns the following output which is related to divergence issue I guess :(
```
  File "/mnt/nas2/users/sbchoi/transformers/src/transformers/models/detr/modeling_detr.py", line 2213, in forward
    indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
  File "/mnt/nas2/users/sbchoi/transformers/src/transformers/models/detr/modeling_detr.py", line 2213, in <listcomp>
    indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
IndexError: index 8 is out of bounds for dimension 0 with size 8
```

Hi @SangbumChoi thanks for PR, 

Regarding the initial question of size collecting
 - I think one line is easier to follow
 - It's just bbox collecting, not sure it has any significant impact on memory/speed
 
 WDYT?
 
Regarding the error, can you, please, create a separate issue so we can discuss it there and track progress?
> returns the following output which is related to divergence issue I guess :(

@qubvel 
1. Sure I will seperate the divergence issue!
2. I was also hesitated to open this PR because when if it is just bbox collecting it has no significant impact. (Well, in case of instance segmentation of 1000 objects have some slight impact)

I will just close this PR and just re-open the issue!
Thanks for the comment :)