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World Cup Qualification CAF Group I stats & predictions

Overview of CAF Group I World Cup Qualifiers

The CAF Group I qualifiers are an exciting phase in the journey to the FIFA World Cup, featuring intense competition among Africa's top football teams. As we approach tomorrow's matches, fans and analysts alike are eagerly anticipating the outcomes that could reshape the standings in this crucial group. With several teams vying for a spot in the next round, each match carries significant weight, promising thrilling performances and strategic masterclasses on the field.

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Matchday Schedule and Key Fixtures

Tomorrow's lineup includes several key fixtures that could determine the fate of teams in Group I. Here's a detailed look at the schedule:

  • Team A vs. Team B: This match is expected to be a high-stakes encounter with both teams needing points to maintain their positions. Team A, currently sitting at the top of the group, will look to consolidate their lead, while Team B aims to disrupt their momentum.
  • Team C vs. Team D: With both teams closely matched in points, this fixture is poised to be a battle for survival. Team C's home advantage might play a crucial role, but Team D's recent form suggests they could pull off an upset.
  • Team E vs. Team F: A clash of styles as Team E's attacking prowess meets Team F's defensive solidity. Fans can expect a tactical battle with both teams looking to exploit any weaknesses.

Expert Betting Predictions

Betting enthusiasts have been closely analyzing the odds and statistics leading up to these matches. Here are some expert predictions for tomorrow's fixtures:

  • Team A vs. Team B: Analysts predict a narrow victory for Team A, with odds favoring them at 1.75. The key player to watch is their star forward, who has been in exceptional form.
  • Team C vs. Team D: A draw is considered likely, with odds at 3.10. Both teams have shown resilience in recent games, making this a tightly contested match.
  • Team E vs. Team F: A high-scoring game is anticipated, with over 2.5 goals priced at 2.20. Team E's attacking flair could see them emerge victorious, but Team F's defense will not make it easy.

Tactical Analysis

The tactical setups for tomorrow's matches are crucial in determining the outcomes. Here’s a breakdown of what to expect:

  • Team A: Likely to employ a 4-3-3 formation, focusing on quick transitions and utilizing the wings to stretch Team B's defense.
  • Team B: Expected to set up in a 5-4-1 formation, aiming to absorb pressure and counter-attack swiftly through their pacey wingers.
  • Team C: May opt for a 3-5-2 setup, providing defensive stability while allowing wing-backs to support attacks down the flanks.
  • Team D: Predicted to use a 4-2-3-1 formation, with an emphasis on midfield control and exploiting spaces behind the opposition's defense.
  • Team E: Likely to go with an attacking 4-2-3-1, focusing on creating overloads in the final third and utilizing their creative midfielders.
  • Team F: Expected to deploy a solid 4-4-2 formation, aiming to nullify Team E's attacking threats through disciplined defending and quick counter-attacks.

Potential Game-Changers

In football, certain players have the ability to turn the tide of a match with their individual brilliance. Here are some potential game-changers for tomorrow's fixtures:

  • Player X (Team A): Known for his incredible dribbling skills and ability to unlock defenses, Player X could be pivotal in breaking down Team B's defensive setup.
  • Player Y (Team B): A dynamic midfielder with exceptional vision and passing range, Player Y can dictate the tempo of the game and create scoring opportunities out of nothing.
  • Player Z (Team C): With his aerial prowess and knack for scoring headers, Player Z could be crucial in set-piece situations against Team D.
  • Player W (Team D): A tenacious defender known for his tackling and intercepting abilities, Player W will be key in disrupting Team C's attacking plays.
  • Player V (Team E): An agile forward with quick feet and sharp shooting skills, Player V is expected to test Team F's defense repeatedly.
  • Player U (Team F): A reliable goalkeeper with excellent reflexes and shot-stopping ability, Player U will be vital in keeping his team in contention against Team E's attacks.

Injury Concerns and Squad News

Injuries can significantly impact team performance, especially in high-stakes qualifiers like these. Here’s the latest on squad news:

  • Team A: Their key defender is doubtful due to a hamstring injury but may recover in time for tomorrow’s match.
  • Team B: Missing their captain due to suspension; this absence could affect their leadership on the pitch.
  • Team C: Their star striker is back from injury and expected to start against Team D.
  • Team D: Concerns over their goalkeeper’s fitness after a knock in training; his availability remains uncertain.
  • Team E: Full squad available; no injury concerns reported ahead of the match against Team F.
  • Team F: Midfielder sidelined with a knee injury; they will miss out on tomorrow’s game.

Historical Context and Rivalries

The history between these teams adds an extra layer of intrigue to tomorrow’s fixtures. Here are some notable rivalries and past encounters:

  • Team A vs. Team B: This rivalry dates back decades, with both teams having memorable clashes at major tournaments. Their last encounter ended in a dramatic draw after extra time.
  • Team C vs. Team D: Known for their fierce battles on the pitch, these teams have often exchanged victories in recent years. Their rivalry is marked by intense physicality and competitive spirit.
  • Team E vs. Team F: While not as historically significant as other matchups, this fixture has seen some closely contested games recently, adding excitement for fans.

Potential Impact on Group Standings

karthikvijay/2018-MICCAI-CVPR-Human-Segmentation<|file_sep|>/README.md # CVPR18 MICCAI Challenge: Human Segmentation The [CVPR18 MICCAI Challenge](https://sites.google.com/view/cvpr18michaelchallenge) on human segmentation aims at segmenting human body parts from images using only semantic labels. This repository contains code for my entry. ### Acknowledgement My submission was inspired by [Zhou et al., 2017](https://arxiv.org/abs/1611.07709) and [Zhao et al., 2017](https://arxiv.org/abs/1710.07451). The code here is adapted from [their implementations](https://github.com/mks0601/dual_parsing_network), which are based on [DeepLab-LargeFOV](https://github.com/tensorflow/models/tree/master/research/deeplab). ### Results Here are some results from my submission. * Final result was achieved using `freeze_at=4` during multi-scale testing. ### Pre-trained models You can download pre-trained models from Google Drive: [Link](https://drive.google.com/drive/folders/1w6N0oGmRyN8ZDcGxJzGG0QxWqZK9R5xW?usp=sharing) ### Citation If you use our code or pre-trained models please cite our paper: @inproceedings{ghosh2018human, title={Human Segmentation from Semantic Labels}, author={Ghosh, Sumanjyoti and Talukdar, Partha Pratim}, booktitle={CVPR}, year={2018} } ### License This project is licensed under BSD license. <|repo_name|>karthikvijay/2018-MICCAI-CVPR-Human-Segmentation<|file_sep|>/utils/image_reader.py import os import cv2 import numpy as np from tqdm import tqdm def read_images(folder): imgs = [] for f in tqdm(os.listdir(folder)): img = cv2.imread(os.path.join(folder,f)) if img is not None: img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) imgs.append(img) return imgs def get_mean_std(imgs): h,w,c = imgs[0].shape dtype = imgs[0].dtype print('Calculating mean std') mean = np.zeros((c),dtype=dtype) std = np.zeros((c),dtype=dtype) for img in tqdm(imgs): mean += img.sum(axis=(0,1)) std += ((img**2).sum(axis=(0,1)) - (img.sum(axis=(0,1)))**2/h/w) mean /= len(imgs)*h*w std = np.sqrt(std/(len(imgs)*h*w) - mean**2) return mean,std<|file_sep|># -*- coding: utf-8 -*- """ Created on Mon Jan 22 17:05:59 2018 @author: Sumanjyoti Ghosh """ import os import numpy as np from PIL import Image from utils import dataset_utils def decode_labels(mask): mask = mask.astype(np.uint8) n_classes = dataset_utils.NUM_CLASSES['human'] color_map = dataset_utils.COLOR_MAPS['human'] c_map = color_map[mask] return c_map def save_images(images_folder,label_path,img_path): for i,img_name in enumerate(os.listdir(images_folder)): img_name += '.jpg' print('Saving image {} out of {}'.format(i+1,len(os.listdir(images_folder)))) img = Image.open(os.path.join(images_folder,img_name)) mask = Image.open(label_path[i]) img.putalpha(mask) img.save(os.path.join(img_path,img_name)) if __name__ == '__main__': os.environ["CUDA_VISIBLE_DEVICES"] = '0' import tensorflow as tf from utils import dataset_utils,model_utils,sess_config,sess_config_gpu,dataloader_tfrecord,layers_tfrecord,model_deeplab_largefov,model_pspnet_largefov,model_dpn_largefov,misc_utils,misc_utils_gpu from utils.dataset_utils import get_dataset_from_tfrecord from utils.model_deeplab_largefov import Model_deeplab_largefov from utils.model_pspnet_largefov import Model_pspnet_largefov from utils.model_dpn_largefov import Model_dpn_largefov batch_size=10 dataset='crowdhuman' dataset_type='val' if dataset == 'crowdhuman': num_classes=dataset_utils.NUM_CLASSES['human'] if dataset_type == 'train': is_training=True elif dataset_type == 'val': is_training=False if dataset=='crowdhuman': val_data_list_file='/home/sghosh/datasets/crowdhuman/crowdhuman_val_data_list.txt' tfrecord_folder='/home/sghosh/datasets/crowdhuman/crowdhuman_tfrecords' tfrecord_path=os.path.join(tfrecord_folder,'crowdhuman_val.tfrecords') model='dpn-largefov' if model == 'deeplab-largefov': model_obj=Model_deeplab_largefov() elif model == 'pspnet-largefov': model_obj=Model_pspnet_largefov() elif model == 'dpn-largefov': model_obj=Model_dpn_largefov() prediction_dir=os.path.join('/home/sghosh/datasets/predictions',model,'crowdhuman_'+dataset_type) os.makedirs(prediction_dir) restore_from='/home/sghosh/models/'+model+'_'+dataset+'_'+dataset_type+'_final/model_final_20000' image_reader=dataloader_tfrecord.DataLoader_tfrecord( tfrecord_path,batch_size,num_classes,is_training, dataset_utils.get_augmentations(is_training), dataset_utils.get_data_augmentations(dataset,is_training), dataset_utils.get_crop_sizes(dataset,is_training), dataset_utils.get_output_sizes(dataset,is_training), dataset_utils.get_min_resize_value(dataset,is_training), dataset_utils.get_max_resize_value(dataset,is_training), dataset_utils.get_resize_factor(dataset,is_training), dataset_utils.get_input_sizes(dataset,is_training)) with tf.Graph().as_default(): with tf.Session(config=sess_config_gpu.get_config()) as sess: tf_global_step=tf.train.get_or_create_global_step() input_image_batch,input_label_batch=input_reader.read(image_reader,num_epochs=None) output_batch=model_obj.inference(input_image_batch) variable_averages=tf.train.ExponentialMovingAverage(0) variable_to_restore=variable_averages.variables_to_restore() saver=tf.train.Saver(variable_to_restore) sess.run(tf.global_variables_initializer()) saver.restore(sess,restore_from) prediction_list=[] for i,(input_image,input_label) in enumerate(image_reader): feed_dict={input_image_batch:input_image,input_label_batch:input_label} output=sess.run(output_batch,{input_image_batch:input_image}) output=output.argmax(axis=-1).squeeze() prediction_list.append(output) prediction_list=np.array(prediction_list).reshape(-1,num_classes,h,w).transpose(1,0,2).astype(np.uint8) with open(val_data_list_file,'r') as f: val_data_list=f.readlines() val_data_list=[line.strip() for line in val_data_list] for i,prediction_mask in enumerate(prediction_list): filename=val_data_list[i] filename=filename.split('/')[-1] prediction_mask=prediction_mask.transpose(1,2,0) Image.fromarray(prediction_mask.astype(np.uint8)).save(os.path.join(prediction_dir,filename)) save_images('/home/sghosh/datasets/crowdhuman/crowdhuman_val','/home/sghosh/datasets/crowdhuman/crowdhuman_val_gt','/home/sghosh/datasets/predictions/'+model+'/crowdhuman_val')<|repo_name|>karthikvijay/2018-MICCAI-CVPR-Human-Segmentation<|file_sep|>/submit.sh #!/bin/bash # Usage: # submit.sh DATASET MODEL MODEL_NAME DATASET_TYPE TRAINING OR VALIDATION # DATASET -> crowdhuman or chicagobooth # MODEL -> deeplab-largefov or pspnet-largefov or dpn-largefov or my-model (my own model without PSPNet architecture) # MODEL_NAME -> deeplab or pspnet or dpn or my-model (my own model without PSPNet architecture) # DATASET_TYPE -> train or val # TRAINING
CrowdHuman val set mIoU(%)
0 - 20k iterations
20k - 60k iterations
60k - 100k iterations
100k - final

Final
Baseline (DeepLab-LargeFOV) 24
26
27
28

25
Baseline + Batch Normalization + Dropout + LARS + Label Smoothing

(My model)		 28
29
30
31

30
Baseline + Batch Normalization + Dropout + LARS + Label Smoothing + Multi-scale Training & Testing

(My model)		 31
32
33
34

33
Baseline + Batch Normalization + Dropout + LARS + Label Smoothing + Multi-scale Training & Testing + PSPNet encoder-decoder architecture (two stage)

(My model)		 34
35
36
37

36
Baseline + Batch Normalization + Dropout + LARS + Label Smoothing + Multi-scale Training & Testing + PSPNet encoder-decoder architecture (one stage)

(My model)		 36
37
38
39

38*