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National 2 Group. C stats & predictions

Exploring the Thrills of Football National 2 Group C France

Football National 2 Group C in France is a dynamic and exciting league that showcases emerging talent and fierce competition. This league serves as a crucial platform for clubs aiming to climb the ranks and achieve promotion to higher divisions. With daily updates on fresh matches and expert betting predictions, enthusiasts can stay ahead of the game, making informed decisions and enjoying every moment of the action-packed season.

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Understanding the Structure of Football National 2 Group C

The Football National 2, also known as Championnat National 2, is the fourth tier in the French football league system. Group C specifically focuses on teams from the south of France, offering a unique blend of regional rivalry and high-stakes football. Each team competes with the goal of securing promotion to the Championnat National, the third tier, making every match crucial.

Key Features of the League

  • Diverse Teams: The league comprises a mix of professional clubs, semi-professional outfits, and ambitious amateur teams, each bringing their unique style and strategy to the pitch.
  • Competitive Matches: Every match is a battle for points, with teams vying for top positions to gain promotion or avoid relegation.
  • Dynamic Gameplay: The league is known for its fast-paced and unpredictable matches, providing fans with thrilling entertainment.

Staying Updated with Daily Match Results

For fans eager to keep up with the latest developments, daily updates on match results are available. These updates provide insights into team performances, standout players, and pivotal moments that define each game. By staying informed, fans can engage more deeply with the league and follow their favorite teams closely.

Expert Betting Predictions: A Strategic Edge

Betting on football adds an extra layer of excitement to following the league. Expert betting predictions offer valuable insights that can enhance your betting strategy. These predictions are based on comprehensive analyses of team form, head-to-head records, player injuries, and other critical factors.

How to Utilize Expert Predictions

  1. Analyze Team Form: Review recent performances to gauge a team's current strength and momentum.
  2. Consider Head-to-Head Stats: Historical matchups can provide clues about how teams might perform against each other.
  3. Monitor Player Availability: Injuries or suspensions can significantly impact a team's chances in a match.
  4. Follow Expert Analysis: Rely on seasoned analysts who provide in-depth evaluations and predictions.

In-Depth Team Profiles

To enhance your understanding of Football National 2 Group C, it's essential to delve into individual team profiles. Each team has its unique history, playing style, and objectives for the season. Here are some highlights:

Sporting Toulon Var Provence (STVP)

  • Home Ground: Stade Mayol
  • Captain: Known for leadership on and off the field
  • Squad Strengths: Strong defensive lineup with promising young talent
  • Season Goals: Aim for top-tier promotion by securing consistent victories

AJ Auxerre II

  • Home Ground: Stade de l'Abbé Deschamps
  • Captain: A seasoned player guiding younger teammates
  • Squad Strengths: Balanced attack and defense with experienced players
  • Season Goals: Maintain competitive edge and aim for top positions

Olympique Alès en Cévennes (OAC)

  • Home Ground: Stade Marcel-Picot
  • Captain: Inspirational leader with a strategic mindset
  • Squad Strengths: Agile midfielders and swift forwards
  • Season Goals: Focus on development while competing for promotion spots

The Role of Fans in Football National 2 Group C

Fans play a pivotal role in the success and vibrancy of Football National 2 Group C. Their unwavering support fuels teams' motivation and creates an electrifying atmosphere during matches. Engaging with fan communities through social media platforms allows supporters to share their passion and insights, fostering a strong sense of camaraderie.

Fan Engagement Strategies

  1. Social Media Interaction: Follow official club pages for real-time updates and fan interactions.
  2. Fan Forums: Participate in discussions to exchange views and predictions with fellow enthusiasts.
  3. Venue Attendance: Attend matches in person to experience the thrill of live football.
  4. Tributes to Players: Show appreciation for players' efforts through chants and cheers.

The Future of Football National 2 Group C

The future of Football National 2 Group C looks promising as clubs continue to invest in youth development and infrastructure. The league's ability to nurture talent ensures a steady supply of skilled players who may eventually shine on larger stages. As technology advances, fans can expect enhanced viewing experiences through innovative platforms and interactive features.

Innovations Enhancing the League Experience

  • Digital Platforms: Streaming services provide access to live matches from anywhere in the world.
  • Data Analytics: Advanced analytics offer deeper insights into player performance and match strategies.
  • Fan Engagement Tools: Interactive apps allow fans to participate in polls, quizzes, and fantasy leagues.
  • Sustainability Initiatives: Clubs are adopting eco-friendly practices to reduce their environmental impact.

Betting Tips for Aspiring Bettors

Betting on Football National 2 Group C can be both exciting and rewarding if approached strategically. Here are some tips to help you make informed bets:

  1. Diversify Your Bets: Spread your bets across different matches to minimize risk.ruizhaoyang/CVPR2018<|file_sep|>/FasterRCNN/README.md ## Requirements + Python==2.7 + tensorflow==1.1 + opencv-python==3.2 + h5py==2.7 + numpy==1.13 ## Train 1. Download pre-trained model bash # I used this one: wget http://www.cs.cmu.edu/~yilun/models/resnet101_faster_rcnn_iter_1600000.h5 # or you can use this one: wget http://www.cs.cmu.edu/~yilun/models/resnet50_faster_rcnn_iter_1200000.h5 2. Train model bash # For resnet101: python train_faster_rcnn.py --model_dir /path/to/save/model --dataset_dir /path/to/coco/train2017/ --val_dataset_dir /path/to/coco/val2017/ --model_path /path/to/pretrained/model/resnet101_faster_rcnn_iter_1600000.h5 --dataset_type coco # For resnet50: python train_faster_rcnn.py --model_dir /path/to/save/model --dataset_dir /path/to/coco/train2017/ --val_dataset_dir /path/to/coco/val2017/ --model_path /path/to/pretrained/model/resnet50_faster_rcnn_iter_1200000.h5 --dataset_type coco Note: I used resnet101_faster_rcnn_iter_1600000.h5 for training. ## Test 1. Prepare dataset Prepare coco val2017 dataset as [here](http://cocodataset.org/#download) bash # unzip images unzip val2017.zip -d val2017/ We use pascal voc format annotation file (labelme annotation), you can generate it by labelme then convert it by using following script: python import json import os from pycocotools.coco import COCO labelme_json_path = 'val2017.json' coco_json_path = 'instances_val2017.json' coco_image_folder = 'val2017' if not os.path.exists(coco_json_path): with open(labelme_json_path) as f: labelme_json = json.load(f) coco = COCO() cats = [] for shape in labelme_json['shapes']: if shape['label'] not in cats: cats.append(shape['label']) for i,cate in enumerate(cats): cat = {} cat['supercategory'] = cate cat['id'] = i +1 cat['name'] = cate coco.dataset['categories'].append(cat) imgs = [] img_id = -1 for shape in labelme_json['shapes']: image_id = shape['imagePath'].split('.')[0] if image_id != img_id: img_id = image_id img = {} img['license'] = None img['file_name'] = image_id + '.jpg' img['height'] = labelme_json['imageHeight'] img['width'] = labelme_json['imageWidth'] img['date_captured'] = None img['flickr_url'] = None img['coco_url'] = None img['id'] = int(image_id) imgs.append(img) coco.dataset['images'].append(img) ann = {} if len(shape["points"]) ==1: x,y,w,h=shape["points"][0][0],shape["points"][0][1],shape["points"][0][0]+shape["points"][1][0],shape["points"][0][1]+shape["points"][1][1] x,y,w,h=int(x),int(y),int(w-x),int(h-y) elif len(shape["points"]) ==4: x,y,w,h=shape["points"][0][0],shape["points"][0][1],shape["points"][2][0],shape["points"][2][1] x,y,w,h=int(x),int(y),int(w-x),int(h-y) else: raise NotImplementedError('invalid points') cat_id = [i +1 for i,cate in enumerate(cats) if cate == shape['label']][0] area=w*h iscrowd=0 bbox=[x,y,w,h] obj={'segmentation':[],'area':area,'iscrowd':iscrowd,'image_id':img_id,'bbox':bbox,'category_id':cat_id,'id':None} coco.dataset['annotations'].append(obj) with open(coco_json_path,'w') as f: json.dump(coco.dataset,f) os.system('cp -r {} {}'.format(os.path.join(labelme_json_path[:-5],'images'),coco_image_folder)) coco.createIndex() ## Evaluate Use following script: python from pycocotools.cocoeval import COCOeval def eval_coco(gt_file,pred_file): gt_coco=COCO(gt_file) pred=COCO.loadRes(pred_file) pred_ids=[pred.getAnnIds(imgIds=gt_coco.getImgIds())[0]] evaluator=COCOeval(gt_coco,pred,'bbox') evaluator.params.imgIds=gt_coco.getImgIds() evaluator.params.catIds=gt_coco.getCatIds() evaluator.params.maxDets=[100] evaluator.params.iouThrs=[x * .05 for x in range(10)] evaluator.evaluate() evaluator.accumulate() evaluator.summarize() if __name__=='__main__': eval_coco('instances_val2017.json','pred.json') The output should be: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = [ recall=(0->100)%] { 'all': xx.xx} Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = xx.xx Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = xx.xx Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = xx.xx Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = xx.xx Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = xx.xx Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = xx.xx Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = xx.xx Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = xx.xx Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = xx.xx Precision x Recall ~~~~~~~~~~ AP AR [...] ## Test your model Use following script: python import cv2 as cv import numpy as np import tensorflow as tf from faster_rcnn import FasterRCNN class_names=['__background__', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train','tvmonitor'] def test(model_dir): model=FasterRCNN(model_dir,model_path=None,n_classes=len(class_names)) image=cv.imread('/path/to/test/image.jpg') image=np.expand_dims(image,axis=-1) image=np.tile(image,(1,1,1,3)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) sess.run(tf.tables_initializer()) model.restore(sess) bboxes,scores,class_ids=model.predict(sess,image) return bboxes,scores,class_ids if __name__=='__main__': bboxes,scores,class_ids=test('/path/to/saved/model') The output is bboxes,scores,class_ids, where bboxes.shape==(n_boxes_by_image,n_boxes_by_image,[x_min,y_min,x_max,y_max]), scores.shape==(n_boxes_by_image,)and class_ids.shape==(n_boxes_by_image,). If you want draw bounding box on image: python for bbox,score,class_id in zip(bboxes[0],scores[0],class_ids[0]): if score > threshold: cv.rectangle(image,(bbox[1],bbox[0]),(bbox[3],bbox[2]),(255,255,255)) cv.putText(image,class_names[class_id]+str(score),(bbox[1],bbox[0]),cv.FONT_HERSHEY_SIMPLEX,.5,(255)) cv.imshow('image',image) cv.waitKey() <|repo_name|>ruizhaoyang/CVPR2018<|file_sep|>/README.md # CVPR2018 Codes for paper ["Weakly-supervised Dense Object Detection"](http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Weakly-Supervised_Dense_Object_CVPR_2018_paper.html) ## Abstract Deep learning based object detection methods have achieved impressive results on many benchmarks but require dense pixel-wise annotations which are labor intensive to obtain. This paper proposes a weakly supervised object detection method that only requires image-level labels. Our method first trains an objectness classifier using only image-level labels. Then it uses objectness classifier outputs as supervision signals to localize objects via dense classification. Finally we refine object proposals generated from localization step using CNN features learned from original images. We evaluate our method on two benchmarks PASCAL VOC2007 detection task and MS COCO detection task. Our method achieves state-of-the-art results among weakly supervised methods. ## Results ### PASCAL VOC2007 ### MS COCO ## Requirements + Python==2.7 + tensorflow==1.x + h5py==2.x + numpy==1.x ## Install Faster RCNN Please refer [here](https://github.com/yizhouk/CVPR2018/blob/master/FasterRCNN/README.md). ## Train Weakly Supervised Object Detector For training Weakly Supervised Object Detector please refer [here](https://github.com/yizhouk/CVPR2018/blob/master/WSDetector/README.md). ## Test Weakly Supervised Object Detector For testing Weakly Supervised Object Detector please refer [here](https://github.com/yizhouk/CVPR2018/blob/master/WSDetector