├── README.md ├── Yolov8_cs2_csgo_demo.py ├── best_cs2_model.pt ├── configuration_files ├── custom_data.yaml └── yolov7-custom.yaml ├── model_history_curves ├── P_curve.png ├── R_curve.png ├── confusion_matrix.png ├── results.png └── results.txt └── test_images ├── a1.png ├── a3.png ├── a5.png ├── a6.png ├── a7.png ├── a9.png └── test_batch2_pred.jpg /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # About Project 4 | This project aims to detect enemy players inside the game in real-time and point the player’s aim directly to the enemy’s head to fire the weapon. 5 | I collected the dataset inside the game by taking screenshots from time to time while playing. 6 | After collecting images, annotation was done with LabelImg. 7 | You can find the dataset on my Kaggle page, link: https://www.kaggle.com/datasets/merfarukgnaydn/counter-strike-2-body-and-head-classification 8 | 9 |
10 | The dataset is small for now, but the results are really good. Lots of people gave me feedback about the data quality, and they were all positive because the images are from the game, not from real life. Therefore, there aren’t any differences in environments, there aren’t any sharp lighting changes between images, or shape differences in objects. 11 |

12 | Both YOLOv7 and YOLOv8 models are trained, and the best balance between real-time performance and accuracy was achieved by the YOLOv8m model. Depending on your GPU, for better FPS you can also train with the YOLOv8n model. 13 |
14 | 15 | # Demo video 16 | 17 | 18 | 19 | 20 | 21 | https://github.com/siromermer/CS2-Yolov7-Custom-ObjectDetection/assets/113242649/69525835-9c82-40b9-acf8-678272df9490 22 | 23 | 24 |

25 | 26 | # Why not CS2 ? 27 | there is no Raw input and Mouse acceleration options in cs2 , therefore even models can detect players without problem in cs2 there is problem with mouse movements . As soon as they add this setting options to CS2 , this will work without problem for sure 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /Yolov8_cs2_csgo_demo.py: -------------------------------------------------------------------------------- 1 | 2 | from mss import mss 3 | from PIL import Image, ImageGrab 4 | 5 | from ultralytics import YOLO 6 | import time 7 | import cv2 8 | import numpy as np 9 | import time 10 | 11 | import pyautogui 12 | 13 | """ 14 | Raw input : off 15 | Mouse acceleration : off 16 | sensivity : 3.85 17 | screen_size : height=480 , width=640 18 | 19 | """ 20 | 21 | avg_fps=0 22 | 23 | img = None 24 | t0 = time.time() 25 | n_frames = 1 26 | 27 | model = YOLO('yolov8_100epoch.pt') 28 | 29 | label_dict={1:"ct_body",2:"ct_head",3:"t_body",4:"t_head"} 30 | 31 | #pyautogui.FAILSAFE=False 32 | 33 | sct = mss() 34 | while True: 35 | 36 | img = np.array(sct.grab((0,0,640,480))) 37 | 38 | 39 | # img = cv.cvtColor(img, cv.COLOR_RGB2BGR) 40 | #img=img[:,:,:3] 41 | region_of_interest = img[:480, :640 ,:3] 42 | region_of_interest = np.ascontiguousarray(region_of_interest, dtype=np.uint8) # this solved my issue !!!!!!!!!!! 43 | 44 | 45 | # Run inference on the source 46 | results = model(region_of_interest) 47 | 48 | result_list=[] 49 | class_list=[] 50 | conf_list=[] 51 | 52 | k=0 53 | for result in results: 54 | 55 | for class_name in result.boxes.cls: 56 | class_list.append(int(class_name)) 57 | 58 | 59 | for id,box in enumerate(result.boxes.xyxy) : # box with xyxy format, (N, 4) 60 | if k==0: 61 | if label_dict[class_list[id]]=="t_head" or label_dict[class_list[id]]=="ct_head": 62 | x1,y1,x2,y2=int(box[0]),int(box[1]),int(box[2]),int(box[3]) 63 | 64 | x_mid=int((x1+x2)/2) 65 | y_mid=int((y1+y2)/2) 66 | 67 | pyautogui.moveTo(x_mid,y_mid) 68 | pyautogui.click(x1+5,y1) 69 | 70 | cv2.rectangle(region_of_interest,(x1,y1),(x2,y2),(0,0,255),2) 71 | cv2.putText(region_of_interest, str(avg_fps) , (20,50) , cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0), 1, cv2.LINE_AA) 72 | k+=1 73 | 74 | 75 | 76 | 77 | #region_of_interest=cv2.cvtColor(region_of_interest,cv2.COLOR_RGB2BGR) 78 | cv2.imshow("Computer Vision", region_of_interest) 79 | 80 | # Break loop and end test 81 | key = cv2.waitKey(1) 82 | if key == ord('q'): 83 | break 84 | 85 | elapsed_time = time.time() - t0 86 | avg_fps = (n_frames / elapsed_time) 87 | print("Average FPS: " + str(avg_fps)) 88 | #cv2.putText(region_of_interest, str(avg_fps) , (50,50) , cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0), 3, cv2.LINE_AA) 89 | n_frames += 1 90 | 91 | 92 | -------------------------------------------------------------------------------- /best_cs2_model.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/best_cs2_model.pt -------------------------------------------------------------------------------- /configuration_files/custom_data.yaml: -------------------------------------------------------------------------------- 1 | 2 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 3 | train: ./data/train # 118287 images 4 | val: ./data/val # 5000 images 5 | 6 | # number of classes 7 | nc: 5 8 | 9 | # class names 10 | names: [ "none", "ct_body", "ct_head","t_body","t_head"] 11 | -------------------------------------------------------------------------------- /configuration_files/yolov7-custom.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 4 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [12,16, 19,36, 40,28] # P3/8 9 | - [36,75, 76,55, 72,146] # P4/16 10 | - [142,110, 192,243, 459,401] # P5/32 11 | 12 | # yolov7 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | 17 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 18 | [-1, 1, Conv, [64, 3, 1]], 19 | 20 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 21 | [-1, 1, Conv, [64, 1, 1]], 22 | [-2, 1, Conv, [64, 1, 1]], 23 | [-1, 1, Conv, [64, 3, 1]], 24 | [-1, 1, Conv, [64, 3, 1]], 25 | [-1, 1, Conv, [64, 3, 1]], 26 | [-1, 1, Conv, [64, 3, 1]], 27 | [[-1, -3, -5, -6], 1, Concat, [1]], 28 | [-1, 1, Conv, [256, 1, 1]], # 11 29 | 30 | [-1, 1, MP, []], 31 | [-1, 1, Conv, [128, 1, 1]], 32 | [-3, 1, Conv, [128, 1, 1]], 33 | [-1, 1, Conv, [128, 3, 2]], 34 | [[-1, -3], 1, Concat, [1]], # 16-P3/8 35 | [-1, 1, Conv, [128, 1, 1]], 36 | [-2, 1, Conv, [128, 1, 1]], 37 | [-1, 1, Conv, [128, 3, 1]], 38 | [-1, 1, Conv, [128, 3, 1]], 39 | [-1, 1, Conv, [128, 3, 1]], 40 | [-1, 1, Conv, [128, 3, 1]], 41 | [[-1, -3, -5, -6], 1, Concat, [1]], 42 | [-1, 1, Conv, [512, 1, 1]], # 24 43 | 44 | [-1, 1, MP, []], 45 | [-1, 1, Conv, [256, 1, 1]], 46 | [-3, 1, Conv, [256, 1, 1]], 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, -3], 1, Concat, [1]], # 29-P4/16 49 | [-1, 1, Conv, [256, 1, 1]], 50 | [-2, 1, Conv, [256, 1, 1]], 51 | [-1, 1, Conv, [256, 3, 1]], 52 | [-1, 1, Conv, [256, 3, 1]], 53 | [-1, 1, Conv, [256, 3, 1]], 54 | [-1, 1, Conv, [256, 3, 1]], 55 | [[-1, -3, -5, -6], 1, Concat, [1]], 56 | [-1, 1, Conv, [1024, 1, 1]], # 37 57 | 58 | [-1, 1, MP, []], 59 | [-1, 1, Conv, [512, 1, 1]], 60 | [-3, 1, Conv, [512, 1, 1]], 61 | [-1, 1, Conv, [512, 3, 2]], 62 | [[-1, -3], 1, Concat, [1]], # 42-P5/32 63 | [-1, 1, Conv, [256, 1, 1]], 64 | [-2, 1, Conv, [256, 1, 1]], 65 | [-1, 1, Conv, [256, 3, 1]], 66 | [-1, 1, Conv, [256, 3, 1]], 67 | [-1, 1, Conv, [256, 3, 1]], 68 | [-1, 1, Conv, [256, 3, 1]], 69 | [[-1, -3, -5, -6], 1, Concat, [1]], 70 | [-1, 1, Conv, [1024, 1, 1]], # 50 71 | ] 72 | 73 | # yolov7 head 74 | head: 75 | [[-1, 1, SPPCSPC, [512]], # 51 76 | 77 | [-1, 1, Conv, [256, 1, 1]], 78 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 79 | [37, 1, Conv, [256, 1, 1]], # route backbone P4 80 | [[-1, -2], 1, Concat, [1]], 81 | 82 | [-1, 1, Conv, [256, 1, 1]], 83 | [-2, 1, Conv, [256, 1, 1]], 84 | [-1, 1, Conv, [128, 3, 1]], 85 | [-1, 1, Conv, [128, 3, 1]], 86 | [-1, 1, Conv, [128, 3, 1]], 87 | [-1, 1, Conv, [128, 3, 1]], 88 | [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], 89 | [-1, 1, Conv, [256, 1, 1]], # 63 90 | 91 | [-1, 1, Conv, [128, 1, 1]], 92 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 93 | [24, 1, Conv, [128, 1, 1]], # route backbone P3 94 | [[-1, -2], 1, Concat, [1]], 95 | 96 | [-1, 1, Conv, [128, 1, 1]], 97 | [-2, 1, Conv, [128, 1, 1]], 98 | [-1, 1, Conv, [64, 3, 1]], 99 | [-1, 1, Conv, [64, 3, 1]], 100 | [-1, 1, Conv, [64, 3, 1]], 101 | [-1, 1, Conv, [64, 3, 1]], 102 | [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], 103 | [-1, 1, Conv, [128, 1, 1]], # 75 104 | 105 | [-1, 1, MP, []], 106 | [-1, 1, Conv, [128, 1, 1]], 107 | [-3, 1, Conv, [128, 1, 1]], 108 | [-1, 1, Conv, [128, 3, 2]], 109 | [[-1, -3, 63], 1, Concat, [1]], 110 | 111 | [-1, 1, Conv, [256, 1, 1]], 112 | [-2, 1, Conv, [256, 1, 1]], 113 | [-1, 1, Conv, [128, 3, 1]], 114 | [-1, 1, Conv, [128, 3, 1]], 115 | [-1, 1, Conv, [128, 3, 1]], 116 | [-1, 1, Conv, [128, 3, 1]], 117 | [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], 118 | [-1, 1, Conv, [256, 1, 1]], # 88 119 | 120 | [-1, 1, MP, []], 121 | [-1, 1, Conv, [256, 1, 1]], 122 | [-3, 1, Conv, [256, 1, 1]], 123 | [-1, 1, Conv, [256, 3, 2]], 124 | [[-1, -3, 51], 1, Concat, [1]], 125 | 126 | [-1, 1, Conv, [512, 1, 1]], 127 | [-2, 1, Conv, [512, 1, 1]], 128 | [-1, 1, Conv, [256, 3, 1]], 129 | [-1, 1, Conv, [256, 3, 1]], 130 | [-1, 1, Conv, [256, 3, 1]], 131 | [-1, 1, Conv, [256, 3, 1]], 132 | [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], 133 | [-1, 1, Conv, [512, 1, 1]], # 101 134 | 135 | [75, 1, RepConv, [256, 3, 1]], 136 | [88, 1, RepConv, [512, 3, 1]], 137 | [101, 1, RepConv, [1024, 3, 1]], 138 | 139 | [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) 140 | ] 141 | -------------------------------------------------------------------------------- /model_history_curves/P_curve.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/model_history_curves/P_curve.png -------------------------------------------------------------------------------- /model_history_curves/R_curve.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/model_history_curves/R_curve.png -------------------------------------------------------------------------------- /model_history_curves/confusion_matrix.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/model_history_curves/confusion_matrix.png -------------------------------------------------------------------------------- /model_history_curves/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/model_history_curves/results.png -------------------------------------------------------------------------------- /model_history_curves/results.txt: -------------------------------------------------------------------------------- 1 | 0/99 0.992G 0.09146 0.01391 0.02875 0.1341 3 640 0.0001739 0.01111 8.412e-06 1.406e-06 0.1392 0.01658 0.032 2 | 1/99 6.91G 0.06969 0.01127 0.0237 0.1047 0 640 0.005378 0.125 0.003143 0.001013 0.1267 0.01661 0.02844 3 | 2/99 7.33G 0.05768 0.00974 0.01912 0.08655 0 640 0.05462 0.1468 0.0413 0.01473 0.1238 0.01565 0.02514 4 | 3/99 7.33G 0.05891 0.008897 0.01892 0.08672 2 640 0.478 0.2403 0.1861 0.05073 0.1174 0.01526 0.02468 5 | 4/99 7.33G 0.04776 0.007921 0.01524 0.07092 2 640 0.6714 0.2344 0.1858 0.07385 0.1125 0.01475 0.02471 6 | 5/99 7.33G 0.04505 0.007255 0.01312 0.06543 2 640 0.7081 0.2736 0.2741 0.1151 0.1122 0.01435 0.02424 7 | 6/99 7.33G 0.03858 0.007174 0.01189 0.05764 2 640 0.5924 0.4953 0.3376 0.1391 0.1124 0.01311 0.02419 8 | 7/99 7.33G 0.04086 0.006599 0.01177 0.05923 4 640 0.7425 0.3264 0.3394 0.1195 0.1108 0.0131 0.02376 9 | 8/99 7.33G 0.04366 0.006335 0.01298 0.06297 2 640 0.7395 0.2417 0.2392 0.09994 0.1091 0.0142 0.02219 10 | 9/99 7.33G 0.04443 0.006404 0.01292 0.06376 4 640 0.7011 0.2572 0.2339 0.09802 0.1141 0.01241 0.02248 11 | 10/99 7.33G 0.04665 0.006066 0.01354 0.06625 3 640 0.7037 0.2722 0.2735 0.1172 0.1112 0.01315 0.02279 12 | 11/99 7.33G 0.05122 0.005519 0.01502 0.07176 2 640 0.6396 0.2331 0.1854 0.07113 0.1164 0.01243 0.02256 13 | 12/99 7.33G 0.04549 0.005372 0.0134 0.06426 5 640 0.398 0.1972 0.1316 0.03617 0.1188 0.0125 0.02282 14 | 13/99 7.33G 0.04668 0.005715 0.01343 0.06583 3 640 0.6641 0.1861 0.2049 0.07454 0.1167 0.0115 0.02293 15 | 14/99 7.33G 0.04891 0.005735 0.01474 0.06938 5 640 0.4916 0.1071 0.08051 0.03133 0.117 0.0134 0.02372 16 | 15/99 7.33G 0.04343 0.005652 0.01227 0.06135 7 640 0.652 0.2401 0.1951 0.07213 0.1126 0.01326 0.02393 17 | 16/99 7.33G 0.04736 0.005927 0.01351 0.0668 0 640 0.2203 0.1415 0.09527 0.04542 0.1163 0.01315 0.02309 18 | 17/99 7.33G 0.04982 0.005664 0.01481 0.07029 4 640 0.1287 0.1043 0.0197 0.00967 0.138 0.1057 0.05681 19 | 18/99 7.33G 0.05042 0.005552 0.01426 0.07023 7 640 0.3478 0.2539 0.1108 0.03586 0.1233 0.01113 0.03957 20 | 19/99 7.33G 0.04715 0.005365 0.01411 0.06662 2 640 0.1709 0.15 0.09031 0.03494 0.12 0.01291 0.02517 21 | 20/99 7.33G 0.04822 0.005654 0.01368 0.06755 4 640 0.3703 0.1859 0.08659 0.0331 0.1217 0.01208 0.04919 22 | 21/99 7.33G 0.04622 0.005366 0.01268 0.06427 2 640 0.4629 0.3347 0.2576 0.1021 0.1101 0.01306 0.02206 23 | 22/99 7.33G 0.04723 0.005305 0.01265 0.06518 1 640 0.7374 0.05139 0.06029 0.02067 0.1221 0.0122 0.02488 24 | 23/99 7.33G 0.04621 0.005215 0.01292 0.06434 0 640 0.1477 0.1658 0.09897 0.03764 0.1188 0.01198 0.02439 25 | 24/99 7.33G 0.04752 0.005693 0.01351 0.06672 3 640 0.2475 0.2213 0.1422 0.05956 0.1148 0.01289 0.0235 26 | 25/99 7.33G 0.04252 0.005844 0.01245 0.06081 4 640 0.2457 0.302 0.1938 0.08601 0.1128 0.01382 0.04307 27 | 26/99 7.33G 0.04172 0.005359 0.01153 0.05861 2 640 0.6033 0.304 0.301 0.1416 0.1087 0.01303 0.02647 28 | 27/99 7.33G 0.04322 0.005721 0.01144 0.06038 2 640 0.3816 0.3079 0.2723 0.0907 0.1076 0.01343 0.02201 29 | 28/99 7.33G 0.04463 0.005511 0.01191 0.06205 6 640 0.6035 0.3063 0.3094 0.1355 0.1065 0.0133 0.02132 30 | 29/99 7.33G 0.0439 0.005248 0.01069 0.05983 2 640 0.7033 0.3694 0.4221 0.1718 0.1015 0.01377 0.01963 31 | 30/99 7.33G 0.04069 0.005446 0.00956 0.0557 2 640 0.6395 0.4929 0.5156 0.2469 0.09716 0.01402 0.01867 32 | 31/99 7.33G 0.04449 0.005654 0.01126 0.06141 2 640 0.6905 0.3209 0.3544 0.1765 0.1039 0.01365 0.02074 33 | 32/99 7.33G 0.0423 0.005285 0.009734 0.05732 2 640 0.6319 0.4342 0.4221 0.2082 0.1012 0.01385 0.01999 34 | 33/99 7.33G 0.04672 0.005236 0.01059 0.06255 2 640 0.6689 0.3765 0.3954 0.1807 0.1044 0.01387 0.02052 35 | 34/99 7.33G 0.04083 0.005177 0.009208 0.05521 3 640 0.6447 0.4687 0.4922 0.2638 0.101 0.01297 0.02125 36 | 35/99 7.33G 0.04332 0.00511 0.00964 0.05807 4 640 0.6652 0.5025 0.5395 0.2755 0.09768 0.01342 0.01767 37 | 36/99 7.33G 0.04325 0.005262 0.009083 0.05759 8 640 0.6438 0.5343 0.5499 0.275 0.09854 0.01298 0.01786 38 | 37/99 7.33G 0.04095 0.004986 0.00782 0.05376 2 640 0.6838 0.5688 0.5939 0.3024 0.09639 0.01282 0.01694 39 | 38/99 7.33G 0.0406 0.005156 0.00802 0.05378 4 640 0.7161 0.5531 0.6174 0.296 0.09489 0.01301 0.01699 40 | 39/99 7.33G 0.0383 0.005326 0.007088 0.05071 10 640 0.6544 0.6011 0.6041 0.2908 0.09502 0.01321 0.01654 41 | 40/99 7.33G 0.0409 0.005115 0.007467 0.05348 2 640 0.7769 0.5642 0.633 0.3505 0.09633 0.01233 0.01719 42 | 41/99 7.33G 0.03963 0.005121 0.008391 0.05314 2 640 0.6246 0.4138 0.4491 0.2296 0.09969 0.01438 0.01863 43 | 42/99 7.33G 0.03949 0.005019 0.008382 0.05289 4 640 0.7477 0.4333 0.5096 0.2694 0.09644 0.01425 0.01866 44 | 43/99 7.33G 0.0393 0.005314 0.008069 0.05269 4 640 0.7023 0.5426 0.5916 0.3197 0.09472 0.01311 0.01759 45 | 44/99 7.33G 0.04 0.004788 0.007851 0.05264 1 640 0.749 0.5212 0.5996 0.3249 0.09391 0.01327 0.01683 46 | 45/99 7.33G 0.03928 0.005214 0.008666 0.05316 2 640 0.684 0.6588 0.6606 0.3787 0.09149 0.01306 0.01643 47 | 46/99 7.33G 0.03609 0.005116 0.006485 0.04769 0 640 0.7678 0.6095 0.6687 0.3645 0.09141 0.013 0.01677 48 | 47/99 7.33G 0.03729 0.004989 0.006591 0.04887 6 640 0.8309 0.5867 0.6593 0.3325 0.09189 0.01305 0.01673 49 | 48/99 7.33G 0.03834 0.005242 0.006325 0.04991 2 640 0.8609 0.6347 0.6927 0.3749 0.09102 0.0131 0.01649 50 | 49/99 7.33G 0.03673 0.00461 0.007565 0.0489 4 640 0.8037 0.6541 0.7074 0.3911 0.08959 0.01296 0.01645 51 | 50/99 7.33G 0.03974 0.004679 0.007892 0.05231 2 640 0.7824 0.6798 0.7188 0.4193 0.08881 0.01277 0.01612 52 | 51/99 7.33G 0.04222 0.00493 0.007865 0.05502 3 640 0.8223 0.6426 0.7046 0.4083 0.08953 0.01305 0.01627 53 | 52/99 7.33G 0.03601 0.004765 0.006091 0.04687 4 640 0.7664 0.6646 0.6972 0.413 0.08881 0.0132 0.01633 54 | 53/99 7.33G 0.03658 0.004774 0.007057 0.04841 2 640 0.7426 0.6961 0.7115 0.4054 0.08788 0.01357 0.01623 55 | 54/99 7.33G 0.03938 0.004901 0.006135 0.05042 0 640 0.7994 0.6799 0.715 0.413 0.08698 0.01347 0.01572 56 | 55/99 7.33G 0.03701 0.004751 0.006188 0.04795 7 640 0.8403 0.6669 0.7378 0.4244 0.08656 0.01325 0.01564 57 | 56/99 7.33G 0.03862 0.00482 0.006483 0.04992 2 640 0.7547 0.6912 0.7201 0.4186 0.08643 0.01332 0.01588 58 | 57/99 7.33G 0.03864 0.00448 0.006793 0.04992 2 640 0.7056 0.6867 0.6941 0.4118 0.08621 0.01373 0.01574 59 | 58/99 7.33G 0.03755 0.004643 0.006282 0.04848 2 640 0.858 0.658 0.7058 0.4266 0.08697 0.01327 0.01603 60 | 59/99 7.33G 0.03845 0.004883 0.00696 0.05029 4 640 0.8437 0.6275 0.7071 0.4131 0.08676 0.0135 0.01623 61 | 60/99 7.33G 0.03745 0.004792 0.006273 0.04851 2 640 0.8713 0.6491 0.7291 0.4378 0.0854 0.01354 0.01583 62 | 61/99 7.33G 0.03472 0.005129 0.005484 0.04533 2 640 0.8071 0.6572 0.721 0.4237 0.08422 0.01397 0.0158 63 | 62/99 7.33G 0.03647 0.005011 0.005797 0.04728 2 640 0.7347 0.7393 0.7466 0.4491 0.08237 0.01451 0.0156 64 | 63/99 7.33G 0.03535 0.004894 0.004667 0.04491 4 640 0.8506 0.7043 0.7512 0.4453 0.08158 0.01432 0.01545 65 | 64/99 7.33G 0.03548 0.004688 0.005671 0.04584 2 640 0.8333 0.7017 0.7471 0.4527 0.08111 0.01427 0.01544 66 | 65/99 7.33G 0.03649 0.004666 0.004907 0.04606 4 640 0.8005 0.7379 0.7546 0.4665 0.08059 0.01408 0.01524 67 | 66/99 7.33G 0.03541 0.00468 0.005255 0.04535 0 640 0.8103 0.6939 0.7527 0.4417 0.08171 0.01419 0.01534 68 | 67/99 7.33G 0.03544 0.005168 0.004449 0.04506 0 640 0.8154 0.7213 0.7669 0.4529 0.08095 0.0143 0.01496 69 | 68/99 7.33G 0.03296 0.004756 0.004225 0.04194 6 640 0.7982 0.7099 0.7452 0.4597 0.08118 0.01443 0.01493 70 | 69/99 7.33G 0.03416 0.004529 0.004325 0.04301 2 640 0.9023 0.6887 0.7794 0.4738 0.08058 0.01443 0.01527 71 | 70/99 7.33G 0.03535 0.004601 0.004104 0.04405 2 640 0.8947 0.7019 0.7775 0.4776 0.08003 0.0144 0.0154 72 | 71/99 7.33G 0.03205 0.004805 0.003842 0.0407 2 640 0.8703 0.7283 0.7836 0.4678 0.07962 0.01446 0.01533 73 | 72/99 7.33G 0.03436 0.004694 0.004168 0.04322 2 640 0.9261 0.699 0.7809 0.4658 0.07961 0.01441 0.01545 74 | 73/99 7.33G 0.03373 0.004882 0.003985 0.0426 4 640 0.9259 0.708 0.7903 0.4777 0.07896 0.01445 0.01569 75 | 74/99 7.33G 0.03382 0.004731 0.003422 0.04197 2 640 0.8803 0.7388 0.8092 0.4937 0.07864 0.01425 0.01509 76 | 75/99 7.33G 0.03535 0.004595 0.004631 0.04458 2 640 0.8879 0.7448 0.807 0.4919 0.0778 0.0145 0.01499 77 | 76/99 7.33G 0.03118 0.004487 0.003596 0.03927 0 640 0.9013 0.7369 0.8067 0.4935 0.07727 0.01459 0.01525 78 | 77/99 7.33G 0.03138 0.00454 0.003536 0.03946 6 640 0.9352 0.7223 0.8163 0.5116 0.07697 0.01452 0.01516 79 | 78/99 7.33G 0.03211 0.004334 0.003389 0.03983 2 640 0.9568 0.7113 0.8103 0.501 0.07711 0.01452 0.01495 80 | 79/99 7.33G 0.03275 0.004637 0.003833 0.04122 4 640 0.9132 0.7284 0.8125 0.512 0.07692 0.01452 0.01519 81 | 80/99 7.33G 0.03282 0.004319 0.00466 0.0418 2 640 0.903 0.7016 0.7946 0.5005 0.07726 0.01446 0.0152 82 | 81/99 7.33G 0.03046 0.004587 0.003402 0.03845 0 640 0.8819 0.7475 0.8098 0.5078 0.07652 0.01463 0.01516 83 | 82/99 7.33G 0.03152 0.004456 0.003291 0.03927 3 640 0.8874 0.7443 0.8037 0.5281 0.07554 0.01481 0.01515 84 | 83/99 7.33G 0.03125 0.004333 0.00289 0.03847 1 640 0.9066 0.7513 0.8159 0.525 0.07521 0.01474 0.01513 85 | 84/99 7.33G 0.03077 0.004546 0.003809 0.03912 5 640 0.8899 0.7765 0.8254 0.5095 0.07526 0.0147 0.01512 86 | 85/99 7.33G 0.03156 0.004523 0.003203 0.03928 4 640 0.8651 0.7844 0.8345 0.5131 0.07449 0.01489 0.01477 87 | 86/99 7.33G 0.03196 0.004654 0.00396 0.04057 6 640 0.9112 0.7485 0.8255 0.5233 0.07407 0.01506 0.01455 88 | 87/99 7.33G 0.03238 0.004512 0.004077 0.04096 2 640 0.9233 0.758 0.8222 0.5184 0.07403 0.01494 0.01439 89 | 88/99 7.33G 0.03063 0.004467 0.002669 0.03776 5 640 0.9242 0.7657 0.8339 0.526 0.0744 0.01485 0.01457 90 | 89/99 7.33G 0.03071 0.004654 0.003305 0.03867 3 640 0.925 0.7655 0.8317 0.5355 0.07461 0.01466 0.0146 91 | 90/99 7.33G 0.03042 0.00411 0.002398 0.03693 4 640 0.9318 0.7634 0.8355 0.5412 0.07428 0.0148 0.01447 92 | 91/99 7.33G 0.03087 0.004395 0.003196 0.03846 3 640 0.9336 0.758 0.8326 0.545 0.0743 0.01497 0.01449 93 | 92/99 7.33G 0.03004 0.004439 0.002773 0.03725 5 640 0.9311 0.7542 0.8294 0.5356 0.07406 0.01485 0.01445 94 | 93/99 7.33G 0.03051 0.004685 0.002747 0.03794 8 640 0.944 0.7624 0.834 0.54 0.07425 0.01465 0.01446 95 | 94/99 7.33G 0.02956 0.004144 0.003177 0.03688 2 640 0.9104 0.7915 0.8483 0.5459 0.07399 0.01467 0.01438 96 | 95/99 7.33G 0.02939 0.004349 0.002805 0.03654 2 640 0.9089 0.7851 0.8467 0.5396 0.07415 0.01462 0.01436 97 | 96/99 7.33G 0.03211 0.004552 0.003459 0.04012 2 640 0.9111 0.7751 0.8419 0.5448 0.07371 0.01466 0.01439 98 | 97/99 7.33G 0.02985 0.004441 0.003798 0.03809 0 640 0.9101 0.7697 0.8383 0.544 0.07318 0.01472 0.01435 99 | 98/99 7.33G 0.03016 0.004241 0.00384 0.03825 2 640 0.9392 0.7673 0.8409 0.5454 0.07269 0.01481 0.01415 100 | 99/99 7.33G 0.03119 0.004515 0.003677 0.03938 6 640 0.9552 0.7753 0.8483 0.5587 0.07223 0.01486 0.01392 101 | -------------------------------------------------------------------------------- /test_images/a1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a1.png -------------------------------------------------------------------------------- /test_images/a3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a3.png -------------------------------------------------------------------------------- /test_images/a5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a5.png -------------------------------------------------------------------------------- /test_images/a6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a6.png -------------------------------------------------------------------------------- /test_images/a7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a7.png -------------------------------------------------------------------------------- /test_images/a9.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/a9.png -------------------------------------------------------------------------------- /test_images/test_batch2_pred.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/siromermer/CS2-CSGO-Yolov8-Yolov7-ObjectDetection/b025ca1e25e8d9a920b8b2c32ff2622fde68fb17/test_images/test_batch2_pred.jpg --------------------------------------------------------------------------------