Introduction to Volleyball Extraliga Hungary
The Volleyball Extraliga in Hungary stands as a premier competition, showcasing the nation's finest volleyball talent. With teams competing fiercely, each match is a spectacle of skill and strategy. This guide provides expert insights into the latest matches, updated daily, along with expert betting predictions to enhance your viewing experience.
Understanding the Extraliga Structure
The Extraliga is structured into two main phases: the regular season and the playoffs. The regular season features all teams competing in a round-robin format, ensuring each team faces off multiple times. The top teams then progress to the playoffs, where intense battles determine the national champion.
Key Teams and Players to Watch
Several teams consistently perform well in the Extraliga, thanks to their skilled rosters and strategic play. Key players often make headlines with their exceptional performances. Here are some notable teams and players:
- Team A: Known for their powerful serve and strong defense.
- Team B: Famous for their agile players and strategic gameplay.
- Player X: A top scorer with remarkable agility and precision.
- Player Y: Renowned for their leadership and tactical acumen.
Daily Match Updates and Highlights
Stay updated with daily match reports that provide comprehensive coverage of each game. Highlights include key moments, standout performances, and critical turning points that influenced the match outcome.
Betting Predictions by Experts
Expert betting predictions offer valuable insights for enthusiasts looking to engage in sports betting. These predictions are based on thorough analysis of team form, player performance, and historical data.
- Prediction Analysis: Detailed breakdown of factors influencing each match's outcome.
- Odds Comparison: Comparative analysis of odds from various bookmakers.
- Betting Strategies: Tips on how to approach betting with confidence.
Tactical Insights and Strategies
Understanding the tactics employed by top teams can enhance your appreciation of the game. Here are some strategic elements that define successful teams:
- Serving Techniques: How different serves can disrupt opponents' formations.
- Defensive Formations: The importance of positioning and movement in defense.
- Offensive Plays: Strategies to break through even the toughest defenses.
Historical Context and Records
The Extraliga has a rich history, with numerous memorable matches and records set over the years. Understanding this history provides context for current performances and rivalries.
- All-Time Top Scorers: A list of players who have left an indelible mark on the league.
- Memorable Matches: Key games that have defined the league's history.
- Club Rivalries: Long-standing rivalries that add excitement to each encounter.
The Role of Analytics in Volleyball
In modern sports, analytics play a crucial role in shaping team strategies and player development. Here's how analytics are applied in volleyball:
- Data Collection: Gathering data on player performance and team dynamics.
- Data Analysis: Using statistical tools to uncover patterns and insights.
- Actionable Insights: Translating data into strategies that improve performance.
Fan Engagement and Community Building
Fans are integral to the vibrancy of the Extraliga. Engaging with fans through social media, forums, and live events helps build a strong community around the sport.
- Social Media Campaigns: Initiatives to connect with fans online.
- Fan Events: Organizing meet-and-greets, autograph sessions, and more.
- User-Generated Content: Encouraging fans to share their own experiences and insights.
The Future of Volleyball Extraliga Hungary
The future looks promising for the Extraliga, with plans for expansion, increased investment in youth development, and international collaborations. These initiatives aim to elevate the league's profile globally.
- Youth Development Programs: Investing in young talent to ensure a strong future for Hungarian volleyball.
- International Collaborations: Partnering with foreign leagues to exchange knowledge and expertise.
- Tech Integration: Leveraging technology to enhance training, fan engagement, and matchday experiences.
Frequently Asked Questions (FAQs)
<|repo_name|>jaredkhan/landmark<|file_sep|>/src/landmark/dataset.py
from pathlib import Path
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import transforms
from landmark.network import (
ResnetEncoder,
VggEncoder,
)
class LandmarkDataset(Dataset):
def __init__(self,
root_dir: Path,
n_landmarks: int = None,
max_length: int = None,
image_size: int = None,
pretraining_type: str = None):
self.root_dir = root_dir
self.n_landmarks = n_landmarks
self.max_length = max_length
self.image_size = image_size
self.pretraining_type = pretraining_type
self.landmark_path = root_dir / 'landmarks'
self.image_path = root_dir / 'images'
if self.pretraining_type is not None:
assert image_size is not None
assert pretraining_type in ['vgg', 'resnet']
if pretraining_type == 'vgg':
self.encoder = VggEncoder(image_size=image_size)
else:
self.encoder = ResnetEncoder(image_size=image_size)
# Load landmark files
landmark_files = sorted(list(self.landmark_path.glob('*.npy')))
assert len(landmark_files) > max_length
if max_length is not None:
landmark_files = landmark_files[:max_length]
self.landmark_files = landmark_files
# Load image files
image_files = sorted(list(self.image_path.glob('*.jpg')))
assert len(image_files) > max_length
if max_length is not None:
image_files = image_files[:max_length]
self.image_files = image_files
# Construct landmarks tensor.
# We will need to pad these tensors before using them as input.
# We do this now so that we only do it once.
landmarks_list = []
for landmark_file in self.landmark_files:
landmarks_array = np.load(landmark_file)
if n_landmarks is not None:
landmarks_array = landmarks_array[:n_landmarks]
landmarks_list.append(torch.from_numpy(landmarks_array))
landmarks_tensor_list = [F.pad(lm, (0, n_landmarks - lm.shape[0],
0, n_landmarks - lm.shape[0]))
for lm in landmarks_list]
self.landmarks_tensor_list = torch.stack(landmarks_tensor_list)
# Construct transforms
normalize_transforms_dict = {
'vgg': transforms.Normalize(mean=[0.485,0.456,0.406],
std=[0.229,0.224,0.225]),
'resnet': transforms.Normalize(mean=[0.5]*3,
std=[0.5]*3)
}
resize_transforms_dict = {
'vgg': transforms.Resize((224,224)),
'resnet': transforms.Resize((224,224))
}
assert pretraining_type in resize_transforms_dict.keys()
transform_dict_list = [{
'pretrain': transforms.Compose([
resize_transforms_dict[pretraining_type],
transforms.ToTensor(),
normalize_transforms_dict[pretraining_type]
]),
'not_pretrain': transforms.Compose([
resize_transforms_dict[pretraining_type],
transforms.ToTensor(),
normalize_transforms_dict[pretraining_type],
transforms.Lambda(lambda x: x.repeat(n_landmarks-1,-1,-1))
])
}]
def __len__(self):
return len(self.landmark_files)
def __getitem__(self,index):
image_file=self.image_files[index]
landmark_file=self.landmark_files[index]
img=Image.open(image_file).convert('RGB')
if self.pretraining_type is not None:
img=self.encoder.preprocess(img)
img=self.encoder(img)
else:
img=self.encoder.preprocess(img)
img=self.encoder(img)
img=img.repeat(self.n_landmarks-1,-1,-1,-1)
return img,self.landmarks_tensor_list[index],image_file.name
class LandmarkDatasetTest(Dataset):
<|file_sepachtig|># Landmark
Landmark is a PyTorch implementation of [A Multimodal Sequence-to-Sequence Model for Facial Landmark Localization](https://arxiv.org/abs/1907.05518) by Jun Zhu et al.
## Requirements
To run this code you will need Python3 (version >=3.6), PyTorch (version >=1.0), Numpy (version >=1.13), OpenCV (version >=4), Pandas (version >=0.23) and Pillow (version >=5).
## Dataset
This implementation was tested on [300W-LP](http://ibug.doc.ic.ac.uk/resources/300-W/) dataset.
## Training
To train your own model from scratch run:
python train.py --epochs=200 --lr=1e-4 --lr-decay=50 --batch-size=16 --checkpoint-path=./checkpoints --data-path=./dataset/300W_LP --pretrain-type=resnet
To fine-tune a pretrained model run:
python train.py --epochs=200 --lr=1e-4 --lr-decay=50 --batch-size=16 --checkpoint-path=./checkpoints/fine-tune-checkpoint.pt --data-path=./dataset/300W_LP --pretrain-type=resnet
To visualize training loss during training run:
tensorboard --logdir="./logs"
## Testing
To test your model run:
python test.py --checkpoint-path=./checkpoints/fine-tune-checkpoint.pt --data-path=./dataset/300W_LP-test-set --image-size=224
Note that `--image-size` should be set according to what you used during training.
## Acknowledgements
This implementation was inspired by [PyTorch Transformer Tutorial](https://pytorch.org/tutorials/beginner/chatbot_tutorial.html).
<|repo_name|>jaredkhan/landmark<|file_sep tartu.mr.cs.landmark.utils
import numpy as np
import cv2
def draw_keypoints(image_path: str,
keypoints_path: str,
output_path: str):
im=cv2.imread(image_path)
kp=np.load(keypoints_path)
im=cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
kp=np.array(kp,dtype='float32')
for i in range(kp.shape[0]):
cv2.circle(im,(int(kp[i][0]),int(kp[i][1])),5,(255*i/(kp.shape[0]),255*i/(kp.shape[0]),255*i/(kp.shape[0])),-1)
cv2.imwrite(output_path+'.png',im)
def draw_bbox(image_path: str,
keypoints_path: str,
output_path: str):
im=cv2.imread(image_path)
kp=np.load(keypoints_path)
im=cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
left_x=min(kp[:,0])
right_x=max(kp[:,0])
top_y=min(kp[:,1])
bottom_y=max(kp[:,1])
cv2.rectangle(im,(int(left_x),int(top_y)),(int(right_x),int(bottom_y)),(255)*np.ones(3),3)
cv2.imwrite(output_path+'.png',im)<|repo_name|>jaredkhan/landmark<|file_sep]#!/usr/bin/env python3
import argparse
def parse_args():
parser=argparse.ArgumentParser(description='Landmark Implementation')
parser.add_argument('--epochs',
type=int,
default=100,
help='Number of epochs')
parser.add_argument('--lr',
type=float,
default=1e-4,
help='Learning rate')
parser.add_argument('--lr-decay',
type=int,
default=50,
help='Learning rate decay interval')
parser.add_argument('--batch-size',
type=int,
default=16,
help='Batch size')
parser.add_argument('--checkpoint-path',
type=str,
default=None,
help='Path for saving checkpoints')
parser.add_argument('--data-path',
type=str,
default=None,
help='Path to dataset')
parser.add_argument('--image-size',
type=int,
default=None,
help='Image size')
parser.add_argument('--n-landmarks',
type=int,
default=None,
help='Number of landmarks')
parser.add_argument('--max-length',
type=int,
default=None,
help='Maximum number of images per dataset split')
parser.add_argument('--pretrain-type',
type=str,
default=None,
help="Pretrained encoder ('vgg' or 'resnet')")
if __name__=='__main__':
args=parse_args()
<|file_sep "# Landmark"
Landmark is a PyTorch implementation of [A Multimodal Sequence-to-Sequence Model for Facial Landmark Localization](https://arxiv.org/abs/1907.05518) by Jun Zhu et al.
## Requirements
To run this code you will need Python3 (version >=3.6), PyTorch (version >=1.0), Numpy (version >=1.13), OpenCV (version >=4), Pandas (version >=0.23) and Pillow (version >=5).
## Dataset
This implementation was tested on [300W-LP](http://ibug.doc.ic.ac.uk/resources/300-W/) dataset.
## Training
To train your own model from scratch run:
bash
python train.py --epochs=200 --lr=1e-4 --lr-decay=50 --batch-size=16 --checkpoint-path=./checkpoints --data-path=./dataset/300W_LP --pretrain-type=resnet
To fine-tune a pretrained model run:
bash
python train.py --epochs=200 --lr=1e-4 --lr-decay=50 --batch-size=16 --checkpoint-path=./checkpoints/fine-tune-checkpoint.pt --data-path=./dataset/300W_LP --pretrain-type=resnet
To visualize training loss during training run:
bash
tensorboard --logdir="./logs"
## Testing
To test your model run:
bash
python test.py --checkpoint-path=./checkpoints/fine-tune-checkpoint.pt --data-path=./dataset/300W_LP-test-set --image-size=224
Note that `--image-size` should be set according to what you used during training.
## Acknowledgements
This implementation was inspired by [PyTorch Transformer Tutorial](https://pytorch.org/tutorials/beginner/chatbot_tutorial.html).
<|repo_name|>jaredkhan/landmark<|file_sep**Multimodal Sequence-to-sequence Model for Facial Landmark Localization**
This repo contains an implementation of our paper ["A Multimodal Sequence-to-sequence Model for Facial Landmark Localization"](https://arxiv.org/pdf/1907.05518.pdf) published at ICCV2019.
## Requirements
* Python >=3.x.x
* PyTorch >=1.x.x
## Datasets
We use [300W-LP](http://ibug.doc.ic.ac.uk/resources/300-W/) dataset as our main dataset which contains ~250K images with bounding box annotations along with landmark locations.
For finetuning we also use WFLW dataset which contains ~41K images with high-quality bounding boxes along with landmark locations.
## Installation
First install all requirements:
bash
pip install -r requirements.txt
Then download our pre-trained models from Google Drive ([model.pt](https://drive.google.com/file/d/14QZQnGtCwN8aRzH9RfVHtSloYdUdZDjJ/view?usp=sharing)) or Dropbox ([model.zip](https://www.dropbox.com/s/nz80gckdf6z9b9v/model.zip?dl=0)).
Afterwards extract downloaded model file into `./checkpoints` directory.
For testing purposes you can also download our prediction results from Google Drive ([results.zip](https://drive.google.com/file/d/15XNQ5YXqUJxRqyIqVeq4QcQo8mzioBJb/view?usp=sharing)) or Dropbox ([results
