Tomorrow's EuroCup Group A Showdown: Unpacking the Action
The EuroCup Group A stage is set for an electrifying day of basketball as teams across Europe prepare to battle it out on the court. With fans eagerly anticipating the matches scheduled for tomorrow, we delve into the action-packed lineup, offering expert betting predictions and insights to enhance your viewing experience. Whether you're a die-hard fan or a casual observer, this guide will provide you with all the essential details to make the most of tomorrow's EuroCup encounters.
As the tournament progresses, Group A has showcased some of the most thrilling matchups in recent memory. With teams vying for a top spot and a chance to advance to the next round, each game promises to be a strategic battle filled with skill, determination, and high-stakes drama. In this section, we'll break down the key matchups, analyze team performances, and offer betting tips to help you navigate the excitement of tomorrow's games.
Match Highlights: Key Games to Watch
Tomorrow's schedule features several pivotal clashes that could determine the fate of Group A contenders. Here are the standout matches you won't want to miss:
- Team X vs. Team Y: This clash is set to be one of the day's most anticipated games. With both teams boasting strong defensive records and dynamic offensive plays, expect a tightly contested match.
- Team Z vs. Team W: Known for their fast-paced style and impressive three-point shooting, this matchup promises to be an offensive spectacle. Keep an eye on key players who could tip the scales in favor of their team.
- Team A vs. Team B: A battle of consistency versus flair, this game pits Team A's reliable performance against Team B's unpredictable brilliance. It's a classic encounter that could go either way.
Expert Betting Predictions
As we approach tomorrow's games, expert analysts have weighed in with their betting predictions. While no outcome is guaranteed in sports, these insights can provide valuable guidance for those looking to place informed bets.
Team X vs. Team Y
Analysts predict a narrow victory for Team X, citing their superior home-court advantage and recent form. However, Team Y's resilience and strategic play should not be underestimated.
- Pick: Team X -1.5 points
- Total Points Over/Under: Over 160 points
Team Z vs. Team W
Expect a high-scoring affair as both teams have demonstrated exceptional shooting capabilities. The prediction leans towards Team Z due to their consistent performance under pressure.
- Pick: Team Z outright win
- Total Points Over/Under: Over 175 points
Team A vs. Team B
This match is deemed highly unpredictable, with both teams having the potential to secure a win. Analysts suggest looking for value in the spread rather than outright winners.
- Pick: Spread: Team A +4 points
- Total Points Over/Under: Under 150 points
In-Depth Team Analysis
Team X: The Defensive Powerhouse
Known for their impenetrable defense, Team X has consistently kept opponents' scoring in check throughout the tournament. Their ability to disrupt offensive plays and force turnovers has been a key factor in their success.
- Key Player: John Doe - A defensive stalwart known for his shot-blocking and steals.
- Strengths: Strong rebounding, disciplined rotations.
- Weaknesses: Occasional lapses in perimeter defense.
Team Y: The Resilient Contenders
Despite facing tough competition, Team Y has shown remarkable resilience and adaptability. Their ability to execute under pressure makes them formidable opponents.
- Key Player: Jane Smith - An all-around player with exceptional leadership qualities.
- Strengths: Versatile offense, clutch shooting.
- Weaknesses: Inconsistent defense.
Team Z: The Offensive Firepower
With a reputation for explosive offensive plays, Team Z has been a constant threat to opponents with their quick transitions and sharpshooting abilities.
- Key Player: Mike Johnson - Renowned for his three-point shooting accuracy.
- Strengths: Fast breaks, perimeter shooting.
- Weaknesses: Turnover-prone at times.
Team W: The Strategic Playmakers
Known for their strategic gameplay and meticulous planning, Team W excels in controlling the pace of the game and exploiting opponents' weaknesses.
- Key Player: Sarah Lee - A playmaker with exceptional court vision.
- Strengths: Ball movement, defensive adjustments.
- Weaknesses: Limited depth on bench.
Tactical Breakdown: What to Watch For
<|repo_name|>eliotahmad/keras-retinanet<|file_sep|>/examples/convert_coco_to_csv.py
"""
Convert COCO dataset to csv format
"""
import argparse
import os
import json
import numpy as np
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert COCO dataset annotations from json format '
'to csv format.'
'csv files are generated as follows:'
'img1.jpg,xmin,ymin,xmax,ymax,classn'
'img1.jpg,xmin,ymin,xmax,ymax,classn'
'img1.jpg,xmin,ymin,xmax,ymax,classn'
'...'
'imgN.jpg,xmin,ymin,xmax,ymax,class')
parser.add_argument('--annotation_path', required=True,
help='path to annotations file (json)')
# parser.add_argument('--classes_path', required=True,
# help='path to classes file (json)')
# parser.add_argument('--images_dir', required=True,
# help='path where images are stored')
# parser.add_argument('--output_path', required=True,
# help='path where csv file will be written')
# parser.add_argument('--convert_to_csv', action='store_true',
# help='whether or not convert annotation '
# 'to csv file')
# return parser.parse_args()
def convert(args):
# classes = json.load(open(args.classes_path))
# if not args.convert_to_csv:
# return
# classes_dict = {}
# class_ids = []
# class_names = []
# for cls in classes['categories']:
# class_ids.append(cls['id'])
# class_names.append(cls['name'])
# classes_dict[cls['id']] = cls['name']
# print('Number of classes found: {}'.format(len(classes['categories'])))
if __name__ == '__main__':
# args = parse_args()
# convert(args)
<|file_sep|># Keras RetinaNet
## Installation
This package requires Python >= `3.6` (not tested on Python `2`), Keras >= `2` (not tested on Keras `1`) and Tensorflow >= `1` (not tested on Tensorflow `0`). Install dependencies by running:
pip install -r requirements.txt
## Examples
### Training
To train RetinaNet from scratch on your own dataset using tensorflow backend run:
python keras_retinanet/bin/train.py csv data/.csv data/_anchors.csv --model resnet50 --batch_size=8 --epochs=10
To train on MS COCO run:
python keras_retinanet/bin/train.py coco
To train on Pascal VOC run:
python keras_retinanet/bin/train.py voc
### Inference
To run inference on images using pretrained models from [the model zoo](https://github.com/fizyr/keras-retinanet/releases) run:
python keras_retinanet/bin/infer.py models/.h5 path/to/images --augment
The `--augment` flag enables test-time augmentation by averaging predictions over original image and horizontally flipped image.
For other ways of using this library refer to [the documentation](https://github.com/fizyr/keras-retinanet/blob/master/docs/source/user_guide.md).
## Model Zoo
[](https://github.com/fizyr/keras-retinanet/releases)
Pretrained models are available at [the model zoo](https://github.com/fizyr/keras-retinanet/releases).
## License
This project is licensed under [MIT License](LICENSE).
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sat Apr 21 15:27:36 2018
@author: eliat
"""
from keras import layers
from keras import backend as K
def spatial_pyramid_pooling(input_tensor,
bin_sizes=(1,2),
mode='max'):
"""
Spatial pyramid pooling layer used in https://arxiv.org/pdf/1612.01105.pdf
:param input_tensor:
input tensor shape must be known (i.e., all dimensions except
the batch dimension should be specified)
:param bin_sizes:
list of bin sizes used by spatial pyramid pooling layer
(e.g., [1,2] will apply max pooling operation over spatial bins
of size [HxW], [H/2 x W/2])
:param mode:
pooling mode used in spatial pyramid pooling layer
(e.g., max or avg)
"""
# get feature map size
input_shape = K.shape(input_tensor)
h = input_shape[1]
w = input_shape[2]
num_channels = input_shape[3]
pooled_outputs = []
# apply different bin sizes
for bin_size in bin_sizes:
# compute size of bins
bin_h = int(h/bin_size)
bin_w = int(w/bin_size)
# perform pooling operation
if mode == 'max':
x = layers.MaxPool2D(pool_size=(bin_h,
bin_w),
strides=(bin_h,
bin_w),
padding='same')(input_tensor)
x = layers.Reshape((bin_size*bin_size*num_channels,),
name='pool_spp_{}_{}'.format(mode,
bin_size))(x)
pooled_outputs.append(x)
elif mode == 'avg':
x = layers.AveragePooling2D(pool_size=(bin_h,
bin_w),
strides=(bin_h,
bin_w),
padding='same')(input_tensor)
x = layers.Reshape((bin_size*bin_size*num_channels,),
name='pool_spp_{}_{}'.format(mode,
bin_size))(x)
pooled_outputs.append(x)
# concatenate all pooled outputs
concat_features = layers.Concatenate(axis=1,name='concat_spp_features')(pooled_outputs)
return concat_features<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Mon Feb 26 18:57:32 2018
@author: eliat
"""
from .resnet import resnet50
from .resnet import resnet101<|repo_name|>eliotahmad/keras-retinanet<|file_sep|>/requirements.txt
cython>=0.25
Keras==2.1.6
matplotlib==2.0.0
numpy==1.13.3
opencv-python==3.4.0
Pillow==5.0.0
scipy==1.0
tensorflow-gpu==1.4<|repo_name|>eliotahmad/keras-retinanet<|file_sep|>/keras_retinanet/models/resnet.py
"""ResNet backbone.
Adapted from https://github.com/fchollet/deep-learning-models/
"""
import keras.backend as K
from keras.layers import Input
from keras.layers import Add
from keras.layers import Activation
from keras.layers import ZeroPadding2D
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import AveragePooling2D
from keras.layers import BatchNormalization
from keras.models import Model
def identity_block(input_tensor,
kernel_size,
filters,
stage,
block):
"""The identity_block is the block that has no conv layer at shortcut
Arguments:
input_tensor: input tensor
kernel_size: default 3x3; size of middle conv layer at main path
filters: list of integers specifying filters in main path
e.g., filters=[64,64,256] means three conv layers with
filters of size 64,64 and 256 respectively
stage: integer; current stage label; used for generating layer names
block: 'a','b'..., current block label; used for generating layer names
Returns:
Output tensor for the block; tensor shape should be same as input tensor shape
"""
filters1,filters2,filters3 = filters
conv_name_base ='res'+str(stage)+block+'_branch'
bn_name_base ='bn'+str(stage)+block+'_branch'
# first component of main path
x = Convolution2D(filters1,(1,1),
name=conv_name_base+'2a',
kernel_initializer='he_normal',
padding='valid',
use_bias=False)(input_tensor)
x = BatchNormalization(name=bn_name_base+'2a')(x)
x = Activation('relu')(x)
# second component of main path
x = Convolution2D(filters2,kernel_size,padding='same',
name=conv_name_base+'2b',
kernel_initializer='he_normal',
use_bias=False)(x)
x = BatchNormalization(name=bn_name_base+'2b')(x)
x = Activation('relu')(x)
# third component of main path
x = Convolution2D(filters3,(1,1),
name=conv_name_base+'2c',
kernel_initializer='he_normal',
padding='valid',
use_bias=False)(x)
x = BatchNormalization(name=bn_name_base+'2c')(x)
# shortcut path
x=Add()([x,input_tensor])
# final step
x=Activation('relu')(x)
return x
def conv_block(input_tensor,
kernel_size,
filters,
stage,
block,
strides=(2,2)):
"""A block that has a conv layer at shortcut.
Arguments:
input_tensor: input tensor
kernel_size: default 3x3; size of middle conv layer at main path
filters: list of integers specifying filters in main path
e.g., filters=[64,64,256] means three conv layers with
filters of size 64,64,and 256 respectively
stage: integer; current stage label; used for generating layer names
block: 'a','b'..., current block label; used for generating layer names
strides: Strides for the first convolutional layer in the block
Returns:
Output tensor for the block; tensor shape should be same as input tensor shape
"""
filters1,filters2,filters3=filters
conv_name_base ='res'+str(stage)+block+'_branch'
bn_name_base ='bn'+str(stage)+block+'_branch'
# first component of main path
x=Convolution2D(filters1,(1,1),strides=strides,name=conv_name_base+'2a',padding='valid',use_bias=False,kernel_initializer='he_normal')(input_tensor)
x=BatchNormalization(name=bn_name_base+'2a')(x)
x=Activation('relu')(x)
if K.image_data_format() == 'channels_last':
axis=-1
else:
axis=1
def resnet50(input_shape=None,
include_top=True):
"""Instantiates the ResNet50 architecture.
ResNet50 is available for both TensorFlow and Theano backends.
(For TensorFlow it uses data format convention "channels_last" by default,
while Theano uses "channels_first". To change data format convection from
Theano's default "channels_first" to "channels_last" simply set
Keras backend.tensorflow_backend.set_image_data_format("channels_last")
before importing ResNet50.)
Note that the default input image size for this model is 224x224.
Arguments:
include_top (boolean): whether to include the fully-connected layer at top of network
Returns:
model (Model): Keras model instance
Reference:
- [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) (CVPR 201