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  1. detect.py +236 -0
  2. runs/train/exp17/F1_curve.png +0 -0
  3. runs/train/exp17/PR_curve.png +0 -0
  4. runs/train/exp17/P_curve.png +0 -0
  5. runs/train/exp17/R_curve.png +0 -0
  6. runs/train/exp17/confusion_matrix.png +0 -0
  7. runs/train/exp17/events.out.tfevents.1715928118.abggdna-DGX-Station-A100.430083.0 +3 -0
  8. runs/train/exp17/hyp.yaml +28 -0
  9. runs/train/exp17/labels.jpg +0 -0
  10. runs/train/exp17/labels_correlogram.jpg +0 -0
  11. runs/train/exp17/opt.yaml +72 -0
  12. runs/train/exp17/results.csv +201 -0
  13. runs/train/exp17/results.png +0 -0
  14. runs/train/exp17/weights/best.pt +3 -0
  15. runs/train/exp17/weights/best_striped.pt +3 -0
  16. runs/train/exp17/weights/last.pt +3 -0
  17. runs/train/exp17/weights/last_striped.pt +3 -0
detect.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import os
3
+ import platform
4
+ import sys
5
+ from pathlib import Path
6
+ import gradio as gr
7
+ import torch
8
+ from PIL import Image
9
+
10
+ FILE = Path(__file__).resolve()
11
+ ROOT = FILE.parents[0] # YOLO root directory
12
+ if str(ROOT) not in sys.path:
13
+ sys.path.append(str(ROOT)) # add ROOT to PATH
14
+ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
15
+
16
+ from models.common import DetectMultiBackend
17
+ from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
18
+ from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
19
+ increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
20
+ from utils.plots import Annotator, colors, save_one_box
21
+ from utils.torch_utils import select_device, smart_inference_mode
22
+
23
+
24
+ @smart_inference_mode()
25
+ def run(
26
+ weights=ROOT / 'yolo.pt', # model path or triton URL
27
+ source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
28
+ data=ROOT / 'data/coco.yaml', # dataset.yaml path
29
+ imgsz=(640, 640), # inference size (height, width)
30
+ conf_thres=0.25, # confidence threshold
31
+ iou_thres=0.45, # NMS IOU threshold
32
+ max_det=1000, # maximum detections per image
33
+ device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
34
+ view_img=False, # show results
35
+ save_txt=False, # save results to *.txt
36
+ save_conf=False, # save confidences in --save-txt labels
37
+ save_crop=False, # save cropped prediction boxes
38
+ nosave=False, # do not save images/videos
39
+ classes=None, # filter by class: --class 0, or --class 0 2 3
40
+ agnostic_nms=False, # class-agnostic NMS
41
+ augment=False, # augmented inference
42
+ visualize=False, # visualize features
43
+ update=False, # update all models
44
+ project=ROOT / 'runs/detect', # save results to project/name
45
+ name='exp', # save results to project/name
46
+ exist_ok=False, # existing project/name ok, do not increment
47
+ line_thickness=3, # bounding box thickness (pixels)
48
+ hide_labels=False, # hide labels
49
+ hide_conf=False, # hide confidences
50
+ half=False, # use FP16 half-precision inference
51
+ dnn=False, # use OpenCV DNN for ONNX inference
52
+ vid_stride=1, # video frame-rate stride
53
+ ):
54
+ source = str(source)
55
+ save_img = not nosave and not source.endswith('.txt') # save inference images
56
+ is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
57
+ is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
58
+ webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
59
+ screenshot = source.lower().startswith('screen')
60
+ if is_url and is_file:
61
+ source = check_file(source) # download
62
+
63
+ # Directories
64
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
65
+ (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
66
+
67
+ # Load model
68
+ device = select_device(device)
69
+ model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
70
+ stride, names, pt = model.stride, model.names, model.pt
71
+ imgsz = check_img_size(imgsz, s=stride) # check image size
72
+
73
+ # Dataloader
74
+ bs = 1 # batch_size
75
+ if webcam:
76
+ view_img = check_imshow(warn=True)
77
+ dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
78
+ bs = len(dataset)
79
+ elif screenshot:
80
+ dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
81
+ else:
82
+ dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
83
+ vid_path, vid_writer = [None] * bs, [None] * bs
84
+
85
+ # Run inference
86
+ model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
87
+ seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
88
+ for path, im, im0s, vid_cap, s in dataset:
89
+ with dt[0]:
90
+ im = torch.from_numpy(im).to(model.device)
91
+ im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
92
+ im /= 255 # 0 - 255 to 0.0 - 1.0
93
+ if len(im.shape) == 3:
94
+ im = im[None] # expand for batch dim
95
+
96
+ # Inference
97
+ with dt[1]:
98
+ visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
99
+ pred = model(im, augment=augment, visualize=visualize)
100
+
101
+ # NMS
102
+ with dt[2]:
103
+ pred = pred[0][1] if isinstance(pred[0], list) else pred[0]
104
+ pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
105
+
106
+ # Second-stage classifier (optional)
107
+ # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
108
+
109
+ # Process predictions
110
+ for i, det in enumerate(pred): # per image
111
+ seen += 1
112
+ if webcam: # batch_size >= 1
113
+ p, im0, frame = path[i], im0s[i].copy(), dataset.count
114
+ s += f'{i}: '
115
+ else:
116
+ p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
117
+
118
+ p = Path(p) # to Path
119
+ save_path = str(save_dir / p.name) # im.jpg
120
+ txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
121
+ s += '%gx%g ' % im.shape[2:] # print string
122
+ gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
123
+ imc = im0.copy() if save_crop else im0 # for save_crop
124
+ annotator = Annotator(im0, line_width=line_thickness, example=str(names))
125
+ if len(det):
126
+ # Rescale boxes from img_size to im0 size
127
+ det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
128
+
129
+ # Print results
130
+ for c in det[:, 5].unique():
131
+ n = (det[:, 5] == c).sum() # detections per class
132
+ s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
133
+
134
+ # Write results
135
+ for *xyxy, conf, cls in reversed(det):
136
+ if save_txt: # Write to file
137
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
138
+ line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
139
+ with open(f'{txt_path}.txt', 'a') as f:
140
+ f.write(('%g ' * len(line)).rstrip() % line + '\n')
141
+
142
+ if save_img or save_crop or view_img: # Add bbox to image
143
+ c = int(cls) # integer class
144
+ label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
145
+ annotator.box_label(xyxy, label, color=colors(c, True))
146
+ if save_crop:
147
+ save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
148
+
149
+ # Stream results
150
+ im0 = annotator.result()
151
+ if view_img:
152
+ if platform.system() == 'Linux' and p not in windows:
153
+ windows.append(p)
154
+ cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
155
+ cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
156
+ cv2.imshow(str(p), im0)
157
+ cv2.waitKey(1) # 1 millisecond
158
+
159
+ # Save results (image with detections)
160
+ if save_img:
161
+ if dataset.mode == 'image':
162
+ cv2.imwrite(save_path, im0)
163
+ else: # 'video' or 'stream'
164
+ if vid_path[i] != save_path: # new video
165
+ vid_path[i] = save_path
166
+ if isinstance(vid_writer[i], cv2.VideoWriter):
167
+ vid_writer[i].release() # release previous video writer
168
+ if vid_cap: # video
169
+ fps = vid_cap.get(cv2.CAP_PROP_FPS)
170
+ w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
171
+ h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
172
+ else: # stream
173
+ fps, w, h = 30, im0.shape[1], im0.shape[0]
174
+ save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
175
+ vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
176
+ vid_writer[i].write(im0)
177
+
178
+ # Print time (inference-only)
179
+ LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
180
+
181
+ # Print results
182
+ t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
183
+ LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
184
+ if save_txt or save_img:
185
+ s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
186
+ LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
187
+ if update:
188
+ strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
189
+
190
+
191
+ def parse_opt():
192
+ parser = argparse.ArgumentParser()
193
+ parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL')
194
+ parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
195
+ parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
196
+ parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
197
+ parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
198
+ parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
199
+ parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
200
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
201
+ parser.add_argument('--view-img', action='store_true', help='show results')
202
+ parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
203
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
204
+ parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
205
+ parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
206
+ parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
207
+ parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
208
+ parser.add_argument('--augment', action='store_true', help='augmented inference')
209
+ parser.add_argument('--visualize', action='store_true', help='visualize features')
210
+ parser.add_argument('--update', action='store_true', help='update all models')
211
+ parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
212
+ parser.add_argument('--name', default='exp', help='save results to project/name')
213
+ parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
214
+ parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
215
+ parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
216
+ parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
217
+ parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
218
+ parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
219
+ parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
220
+ opt = parser.parse_args()
221
+ opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
222
+ print_args(vars(opt))
223
+ return opt
224
+
225
+
226
+ def main(opt):
227
+ check_requirements(exclude=('tensorboard', 'thop'))
228
+ run(**vars(opt))
229
+
230
+
231
+ # if __name__ == "__main__":
232
+ # opt = parse_opt()
233
+ # main(opt)
234
+
235
+
236
+
runs/train/exp17/F1_curve.png ADDED
runs/train/exp17/PR_curve.png ADDED
runs/train/exp17/P_curve.png ADDED
runs/train/exp17/R_curve.png ADDED
runs/train/exp17/confusion_matrix.png ADDED
runs/train/exp17/events.out.tfevents.1715928118.abggdna-DGX-Station-A100.430083.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e47d5e6569fb0cf38b99e6a102eeab71dd130cb478d5dd288158b4fae849033c
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+ size 1685033
runs/train/exp17/hyp.yaml ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ lr0: 0.01
2
+ lrf: 0.01
3
+ momentum: 0.937
4
+ weight_decay: 0.0005
5
+ warmup_epochs: 3.0
6
+ warmup_momentum: 0.8
7
+ warmup_bias_lr: 0.1
8
+ box: 7.5
9
+ cls: 0.5
10
+ cls_pw: 1.0
11
+ dfl: 1.5
12
+ obj_pw: 1.0
13
+ iou_t: 0.2
14
+ anchor_t: 5.0
15
+ fl_gamma: 0.0
16
+ hsv_h: 0.015
17
+ hsv_s: 0.7
18
+ hsv_v: 0.4
19
+ degrees: 0.0
20
+ translate: 0.1
21
+ scale: 0.9
22
+ shear: 0.0
23
+ perspective: 0.0
24
+ flipud: 0.0
25
+ fliplr: 0.5
26
+ mosaic: 1.0
27
+ mixup: 0.15
28
+ copy_paste: 0.3
runs/train/exp17/labels.jpg ADDED
runs/train/exp17/labels_correlogram.jpg ADDED
runs/train/exp17/opt.yaml ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ weights: /raid/users/mohammadibrahim-st/TSAI/Assignment15/yolov9/weights/gelan-c.pt
2
+ cfg: models/detect/gelan-c.yaml
3
+ data: /raid/users/mohammadibrahim-st/TSAI/Assignment15/yolov9/jetdata/data.yaml
4
+ hyp:
5
+ lr0: 0.01
6
+ lrf: 0.01
7
+ momentum: 0.937
8
+ weight_decay: 0.0005
9
+ warmup_epochs: 3.0
10
+ warmup_momentum: 0.8
11
+ warmup_bias_lr: 0.1
12
+ box: 7.5
13
+ cls: 0.5
14
+ cls_pw: 1.0
15
+ dfl: 1.5
16
+ obj_pw: 1.0
17
+ iou_t: 0.2
18
+ anchor_t: 5.0
19
+ fl_gamma: 0.0
20
+ hsv_h: 0.015
21
+ hsv_s: 0.7
22
+ hsv_v: 0.4
23
+ degrees: 0.0
24
+ translate: 0.1
25
+ scale: 0.9
26
+ shear: 0.0
27
+ perspective: 0.0
28
+ flipud: 0.0
29
+ fliplr: 0.5
30
+ mosaic: 1.0
31
+ mixup: 0.15
32
+ copy_paste: 0.3
33
+ epochs: 200
34
+ batch_size: 512
35
+ imgsz: 640
36
+ rect: false
37
+ resume: false
38
+ nosave: false
39
+ noval: false
40
+ noautoanchor: false
41
+ noplots: false
42
+ evolve: null
43
+ bucket: ''
44
+ cache: null
45
+ image_weights: false
46
+ device: '0'
47
+ multi_scale: false
48
+ single_cls: false
49
+ optimizer: SGD
50
+ sync_bn: false
51
+ workers: 8
52
+ project: runs/train
53
+ name: exp
54
+ exist_ok: false
55
+ quad: false
56
+ cos_lr: false
57
+ flat_cos_lr: false
58
+ fixed_lr: false
59
+ label_smoothing: 0.0
60
+ patience: 100
61
+ freeze:
62
+ - 0
63
+ save_period: -1
64
+ seed: 0
65
+ local_rank: -1
66
+ min_items: 0
67
+ close_mosaic: 15
68
+ entity: null
69
+ upload_dataset: false
70
+ bbox_interval: -1
71
+ artifact_alias: latest
72
+ save_dir: runs/train/exp17
runs/train/exp17/results.csv ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/cls_loss, val/dfl_loss, x/lr0, x/lr1, x/lr2
2
+ 0, 1.2863, 3.1061, 1.4301, 0.72322, 0.12445, 0.396, 0.30629, 0.90551, 2.8054, 1.2081, 0.1, 0, 0
3
+ 1, 1.1504, 3.1989, 1.3783, 0.51939, 0.14881, 0.22899, 0.1664, 0.91291, 2.864, 1.2205, 0.0991, 9.9505e-05, 9.9505e-05
4
+ 2, 1.169, 3.1728, 1.3538, 0.49981, 0.14275, 0.21889, 0.15949, 0.91305, 2.87, 1.2206, 0.098198, 0.00019802, 0.00019802
5
+ 3, 1.2377, 3.1007, 1.3988, 0.47155, 0.1369, 0.21304, 0.15543, 0.91474, 2.8775, 1.2237, 0.097296, 0.00029554, 0.00029554
6
+ 4, 1.1112, 3.1902, 1.3435, 0.4541, 0.13866, 0.20779, 0.15046, 0.9172, 2.8884, 1.2244, 0.096392, 0.00039208, 0.00039208
7
+ 5, 1.2161, 3.1322, 1.452, 0.45701, 0.1369, 0.21009, 0.15362, 0.92028, 2.8956, 1.2258, 0.095488, 0.00048762, 0.00048762
8
+ 6, 1.1506, 3.0552, 1.3543, 0.43391, 0.13095, 0.20509, 0.15253, 0.92115, 2.8968, 1.2274, 0.094582, 0.00058218, 0.00058218
9
+ 7, 1.1882, 3.1866, 1.3566, 0.44917, 0.13105, 0.1992, 0.14545, 0.91943, 2.8983, 1.2288, 0.093676, 0.00067574, 0.00067574
10
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159
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160
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161
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163
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164
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165
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166
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167
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174
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