There's a game called Superfighters Deluxe  and I want to create a object detection with yolov5. For this it needs to have a custom dataset which I already have and it's already pre trained. This one C:\Users\Ninja.000\Downloads\exp3-20240102T094124Z-001\exp3\weights\best.pt

How can I make this work? To my desire? Which is verify if game is open, if it is do the object detection and show it on a new window capturing the localPlayer and enemy in real time 

Here are the logs for training data

50 epochs completed in 1.037 hours.
Optimizer stripped from ../drive/MyDrive/Things/yolov5-master/runs/train/exp3/weights/last.pt, 14.4MB
Optimizer stripped from ../drive/MyDrive/Things/yolov5-master/runs/train/exp3/weights/best.pt, 14.4MB

2024-01-02 04:06:00.937930: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-02 04:06:00.938025: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-02 04:06:00.939835: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
train: weights=../drive/MyDrive/Things/yolov5-master/yolov5s.pt, cfg=, data=/content/drive/MyDrive/Things/yolov5-master/yolov5_config.yaml, hyp=../drive/MyDrive/Things/yolov5-master/data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=../drive/MyDrive/Things/yolov5-master/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 🚀 2024-1-1 Python-3.10.12 torch-2.1.2+cu121 CPU

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir ../drive/MyDrive/Things/yolov5-master/runs/train', view at http://localhost:6006/
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 16.1MB/s]
Overriding model.yaml nc=80 with nc=2

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1     18879  models.yolo.Detect                      [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7025023 parameters, 7025023 gradients, 16.0 GFLOPs

Transferred 343/349 items from ../drive/MyDrive/Things/yolov5-master/yolov5s.pt
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/drive/MyDrive/test2... 35 images, 0 backgrounds, 0 corrupt: 100% 35/35 [00:19<00:00,  1.81it/s]
train: New cache created: /content/drive/MyDrive/test2.cache
val: Scanning /content/drive/MyDrive/test2.cache... 35 images, 0 backgrounds, 0 corrupt: 100% 35/35 [00:00<?, ?it/s]

AutoAnchor: 6.46 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to ../drive/MyDrive/Things/yolov5-master/runs/train/exp3/labels.jpg... 
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to ../drive/MyDrive/Things/yolov5-master/runs/train/exp3
Starting training for 50 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       0/49         0G     0.1246    0.03552    0.03061          5        640: 100% 3/3 [01:25<00:00, 28.62s/it]
                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/2 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 2.100s exceeded
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:13<00:00,  6.92s/it]
                   all         35         63          0          0          0          0

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       1/49         0G     0.1223     0.0367    0.03103         12        640: 100% 3/3 [01:01<00:00, 20.64s/it]
                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/2 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 2.100s exceeded
                 Class     Images  Instances          P          R      mAP50   mAP50-95:  50% 1/2 [00:11<00:11, 11.69s/it]WARNING ⚠️ NMS time limit 0.650s exceeded
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:13<00:00,  6.80s/it]
                   all         35         63   0.000276     0.0179   0.000145   4.34e-05

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       2/49         0G      0.115    0.03567    0.02994          4        640: 100% 3/3 [01:03<00:00, 21.26s/it]
                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/2 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 2.100s exceeded
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:14<00:00,  7.38s/it]
                   all         35         63   0.000447     0.0321   0.000319    5.7e-05

Validating ../drive/MyDrive/Things/yolov5-master/runs/train/exp3/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:11<00:00,  5.97s/it]
                   all         35         63      0.681      0.884      0.894      0.425
           localPlayer         35         35      0.824      0.804        0.9      0.422
                 enemy         35         28      0.537      0.964      0.888      0.427
Results saved to ../drive/MyDrive/Things/yolov5-master/runs/train/exp3