diff --git a/.gitignore b/.gitignore index 85207dada..43ea9c32e 100644 --- a/.gitignore +++ b/.gitignore @@ -6,7 +6,8 @@ __pycache__/ ml-model/formula_images/ ml-model/output/ ml-model/crop_formula_images/ - +ml-model/yolov5/config.py +.vscode/ output.zip *.zip @@ -19,6 +20,7 @@ data/ simese_data/ venv/ ml-model/model.pt +ml-model/yolov5/preprocess_data/ training_data/ *.png im2latex/ @@ -29,3 +31,4 @@ venv/ ml-model/paths_output.csv ml-model/web/__pycache__/ +datasets/ diff --git a/ml-model/yolov5/detect_copy1.py b/ml-model/yolov5/detect_copy1.py new file mode 100644 index 000000000..5e83ac43f --- /dev/null +++ b/ml-model/yolov5/detect_copy1.py @@ -0,0 +1,259 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) +from utils.plots import Annotator, colors, save_one_box +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s.pt', # model path or triton URL + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/detect', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred = model(im, augment=augment, visualize=visualize) + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Write results + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}__.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.50, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/ml-model/yolov5/input_info/names.txt b/ml-model/yolov5/input_info/names.txt deleted file mode 100644 index bc337535d..000000000 --- a/ml-model/yolov5/input_info/names.txt +++ /dev/null @@ -1,3 +0,0 @@ -mathsearch-intermediary -012330fd-7c87-4236-8f4c-b39f3ea72968 -0c923fac-14a1-4f5f-ad1f-88c4e168693b \ No newline at end of file diff --git a/ml-model/yolov5/main.py b/ml-model/yolov5/main.py deleted file mode 100644 index 00387c43a..000000000 --- a/ml-model/yolov5/main.py +++ /dev/null @@ -1,156 +0,0 @@ -import subprocess -from subprocess import call, run -import json -import pandas as pd -import os -import shutil -import boto3 -import numpy as np -import cv2 -import time -import sys -import PyPDF2 -import pdf2image -from PIL import Image -import csv -import requests - -PREPROCESS_FOLDER = "/home/ubuntu/MathSearch/ml-model/yolov5/preprocess_data/" -DATA_FOLDER = "/home/ubuntu/MathSearch/ml-model/yolov5/input_data/" - -""" -pdf files location hard coded to DATA_FOLDER -Args: pdf_filename, target_filename - -pdf_filename is inputs/[pdf_filename] of user's pdf query in S3's 'mathsearch-intermediary' bucket -target_filename is inputs/[target_file] of user's pdf query -""" - -def main(pdf_image_prefix,local_target): - """ - target_file_name: str of name of file we are looking for. - Assumption: input_images/ has been updated with the latest images. - """ - print("running yolov5/main.py...") - - os.chdir("/home/ubuntu/MathSearch/ml-model/yolov5") - target_file_name = local_target - - # Dataset contains output of YOLO model - # Clear folder to reset working directory - dataset_path = "ranking/dataset" - if(os.path.isdir(dataset_path)): - shutil.rmtree(dataset_path) - - # Call YOLO model. - # Uses best.torchscript weights - # Input data: input_data/ - # Writing output to ranking/dataset - run('conda run -n pytorch python detect.py --weights best.torchscript --source input_data/{} --save-txt --save-crop --project ranking/dataset/'.format(sys.argv[1]), shell=True) - - # Get list of files written to YOLO output, except for target_file_name - dir_list = os.listdir(os.path.join(dataset_path,"exp" ,"crops", "equation")) - dir_list = [x for x in dir_list if x != target_file_name] - - # Construct tbl of generated crops for similarity detection model - img_database = pd.DataFrame(columns = ['image_name', 'image_source', 'coo_1', 'coo_2', 'coo_3', 'coo_4']) - for f in dir_list: - img_source, rem = f.split("__") - df = pd.read_csv(os.path.join(dataset_path,"exp" ,"labels/") + img_source + ".txt", delim_whitespace=True, header=None) - new_row = {'image_name': f, 'image_source': img_source, 'coo_1':df.iloc[0, 1], - 'coo_2':df.iloc[0, 2], 'coo_3': df.iloc[0, 3], - 'coo_4': df.iloc[0, 4]} - img_database = img_database.append(new_row, ignore_index = True) - img_database.to_csv("ranking/img_database.csv") - - # Call similarity detection model - # Writes final output to top5.csv - run('conda run -n pytorch python ./ranking/ImageMatching.py',shell=True ) - - -# Json example -# { -# "file":"ex1.pdf", -# "coords":"0 0.3392857142857143 0.17142857142857146 0.30952380952380953 0.12698412698412698 1 0.32242063492063494 0.4380952380952381 0.26785714285714285 0.08888888888888889" -# } -def send_result_to_frontend(pdf_name): - result_coords = "" - result_csv = "/home/ubuntu/MathSearch/ml-model/yolov5/ranking/top5.csv" - with open(result_csv, 'r') as f: - reader = csv.reader(f, delimiter=',') - for row in reader: - # adding page number and coords for each re-rank - # # result_coords += str(int(row[0])+1) + " " - result_coords += row[0] + " " - result_coords += row[3] + " " - result_coords += row[4] + " " - result_coords += row[5] + " " - result_coords += row[6] + " " - frontend_url = "http://3.94.25.91/api/result" - json = { - "file":pdf_name, - "coords":result_coords - } - print(pdf_name) - print(result_coords) - res = requests.get(frontend_url, json=json) - res = print(res) # OK = 200 - - -def remove_files(): - global DATA_FOLDER - for f in os.listdir(DATA_FOLDER): - try: - os.remove(os.path.join(DATA_FOLDER, f)) - except: - shutil.rmtree(os.path.join(DATA_FOLDER, f)) - -def download_files(pdf_name, target_name): - global DATA_FOLDER - global PREPROCESS_FOLDER - s3 = boto3.client("s3") - MATHSEARCH_BUCKET='mathsearch-intermediary' - local_pdf = PREPROCESS_FOLDER + pdf_name - local_target = DATA_FOLDER + target_name[:-5] + "target.png" - print("local_pdf",local_pdf) - print("pdf_name",pdf_name) - - # download and preprocess pdf to png - s3.download_file( - Bucket=MATHSEARCH_BUCKET, Key="inputs/"+pdf_name, Filename=local_pdf - ) - images = pdf2image.convert_from_path(local_pdf) - print(local_pdf) - os.mkdir(DATA_FOLDER + pdf_name) - for i in range(len(images)): - pdf_image = DATA_FOLDER + pdf_name + "/"+ str(i) + ".png" - print(pdf_image) - images[i].save(pdf_image) - - # download target png - s3.download_file( - Bucket=MATHSEARCH_BUCKET, Key="inputs/"+target_name, Filename=local_target - ) - -if __name__ == "__main__": - - pdf_name = sys.argv[1] - target_name = sys.argv[2] - - print(pdf_name) - print(target_name) - - remove_files() - time.sleep(10) - download_files(pdf_name,target_name) - - # prefix example: - # /home/ubuntu/MathSearch/ml-model/yolov5/input_data/012330fd-7c87-4236-8f4c-b39f3ea72968_pdf - # actual path: - # /home/ubuntu/MathSearch/ml-model/yolov5/input_data/012330fd-7c87-4236-8f4c-b39f3ea72968_pdf0.png - pdf_image_prefix = DATA_FOLDER + pdf_name - local_target = DATA_FOLDER + target_name[:-5] + "target.png" - - main(pdf_image_prefix,local_target) - print("finished running yolo! sending results to frontend...") - send_result_to_frontend(pdf_name) \ No newline at end of file diff --git a/ml-model/yolov5/ranking/dataset/exp2/labels/ex1_0.txt b/ml-model/yolov5/ranking/dcopy/exp/labels/ex1_0.txt similarity index 100% rename from ml-model/yolov5/ranking/dataset/exp2/labels/ex1_0.txt rename to ml-model/yolov5/ranking/dcopy/exp/labels/ex1_0.txt diff --git a/ml-model/yolov5/ranking/dcopy/exp/labels/ex1_1.txt b/ml-model/yolov5/ranking/dcopy/exp/labels/ex1_1.txt new file mode 100644 index 000000000..84dd4b75b --- /dev/null +++ b/ml-model/yolov5/ranking/dcopy/exp/labels/ex1_1.txt @@ -0,0 +1 @@ +0 0.330834 0.436236 0.277694 0.108673 diff --git a/ml-model/yolov5/ranking/img_database.csv b/ml-model/yolov5/ranking/img_database.csv index f0bc8409e..de518aa49 100644 --- a/ml-model/yolov5/ranking/img_database.csv +++ b/ml-model/yolov5/ranking/img_database.csv @@ -1,18 +1,4 @@ ,image_name,image_source,coo_1,coo_2,coo_3,coo_4 -0,Page2__.jpg,Page2,0.494163,0.301358,0.291868,0.0619835 -1,DeSa__.jpg,DeSa,0.439157,0.741329,0.415663,0.0526012 -2,DeSa__4.jpg,DeSa,0.45241,0.543353,0.339759,0.0416185 -3,Page3__.jpg,Page3,0.321057,0.389454,0.129704,0.0571792 -4,Page4__5.jpg,Page4,0.507806,0.791269,0.345476,0.156889 -5,DeSa__2.jpg,DeSa,0.315663,0.647688,0.314458,0.050289 -6,Page4__3.jpg,Page4,0.738391,0.306958,0.106085,0.0900409 -7,DeSa2__.jpg,DeSa2,0.439157,0.741329,0.415663,0.0526012 -8,DeSa2__2.jpg,DeSa2,0.315663,0.647688,0.314458,0.050289 -9,Page4__2.jpg,Page4,0.377902,0.305935,0.0832666,0.085266 -10,Page5__.jpg,Page5,0.485121,0.951722,0.342208,0.0955115 -11,DeSa2__3.jpg,DeSa2,0.421084,0.451445,0.453012,0.0693642 -12,Page4__4.jpg,Page4,0.500801,0.305935,0.0776621,0.0511596 -13,DeSa__3.jpg,DeSa,0.421084,0.451445,0.453012,0.0693642 -14,DeSa2__4.jpg,DeSa2,0.45241,0.543353,0.339759,0.0416185 -15,Page4__.jpg,Page4,0.293435,0.187926,0.0760608,0.0648022 -16,Page1__.jpg,Page1,0.487605,0.903516,0.395378,0.169531 +0,1__.jpg,1,0.492647,0.564091,0.33,0.0518182 +1,2__.jpg,2,0.498235,0.655455,0.289412,0.0309091 +2,3__.jpg,3,0.504706,0.387273,0.295294,0.0545455 diff --git a/ml-model/yolov5/ranking/img_database_copy.csv b/ml-model/yolov5/ranking/img_database_copy.csv new file mode 100644 index 000000000..f0bc8409e --- /dev/null +++ b/ml-model/yolov5/ranking/img_database_copy.csv @@ -0,0 +1,18 @@ +,image_name,image_source,coo_1,coo_2,coo_3,coo_4 +0,Page2__.jpg,Page2,0.494163,0.301358,0.291868,0.0619835 +1,DeSa__.jpg,DeSa,0.439157,0.741329,0.415663,0.0526012 +2,DeSa__4.jpg,DeSa,0.45241,0.543353,0.339759,0.0416185 +3,Page3__.jpg,Page3,0.321057,0.389454,0.129704,0.0571792 +4,Page4__5.jpg,Page4,0.507806,0.791269,0.345476,0.156889 +5,DeSa__2.jpg,DeSa,0.315663,0.647688,0.314458,0.050289 +6,Page4__3.jpg,Page4,0.738391,0.306958,0.106085,0.0900409 +7,DeSa2__.jpg,DeSa2,0.439157,0.741329,0.415663,0.0526012 +8,DeSa2__2.jpg,DeSa2,0.315663,0.647688,0.314458,0.050289 +9,Page4__2.jpg,Page4,0.377902,0.305935,0.0832666,0.085266 +10,Page5__.jpg,Page5,0.485121,0.951722,0.342208,0.0955115 +11,DeSa2__3.jpg,DeSa2,0.421084,0.451445,0.453012,0.0693642 +12,Page4__4.jpg,Page4,0.500801,0.305935,0.0776621,0.0511596 +13,DeSa__3.jpg,DeSa,0.421084,0.451445,0.453012,0.0693642 +14,DeSa2__4.jpg,DeSa2,0.45241,0.543353,0.339759,0.0416185 +15,Page4__.jpg,Page4,0.293435,0.187926,0.0760608,0.0648022 +16,Page1__.jpg,Page1,0.487605,0.903516,0.395378,0.169531 diff --git a/ml-model/yolov5/ranking/top5.csv b/ml-model/yolov5/ranking/top5.csv index 33b74c425..255a7ae6e 100644 --- a/ml-model/yolov5/ranking/top5.csv +++ b/ml-model/yolov5/ranking/top5.csv @@ -1,6 +1,3 @@ -Unnamed: 0,image_name,image_source,coo_1,coo_2,coo_3,coo_4 -16,Page1__.jpg,Page1,0.487605,0.903516,0.395378,0.169531 -4,Page4__5.jpg,Page4,0.507806,0.791269,0.345476,0.156889 -10,Page5__.jpg,Page5,0.485121,0.951722,0.342208,0.0955115 -11,DeSa2__3.jpg,DeSa2,0.421084,0.451445,0.453012,0.0693642 -0,Page2__.jpg,Page2,0.494163,0.301358,0.291868,0.0619835 \ No newline at end of file +0,1__.jpg,1,0.492647,0.564091,0.33,0.0518182 +2,3__.jpg,3,0.504706,0.387273,0.295294,0.0545455 +1,2__.jpg,2,0.498235,0.655455,0.289412,0.0309091