Premier League stats & predictions

Upcoming Premier League Cambodia Matches: Expert Predictions and Betting Tips

As the excitement builds for tomorrow's Premier League Cambodia matches, fans and bettors alike are eagerly anticipating thrilling clashes on the pitch. With a packed schedule, there are several key fixtures to watch, each offering unique opportunities for those looking to place informed bets. In this detailed guide, we delve into the matchups, provide expert predictions, and share strategic betting tips to help you make the most of tomorrow's games.

Match Highlights: What to Expect Tomorrow

The Premier League Cambodia continues to captivate audiences with its dynamic style of play and competitive spirit. Tomorrow's fixtures promise high-stakes drama as teams vie for crucial points in the league standings. Here's a breakdown of the most anticipated matches:

• Phnom Penh Crown vs. Boeung Ket Angkor: A clash of titans, this match features two of the league's top contenders. Phnom Penh Crown aims to maintain their unbeaten streak, while Boeung Ket Angkor looks to upset the odds and climb the table.
• Nagaworld vs. National Defense Ministry: This fixture is set to be a tactical battle, with both teams known for their disciplined defensive setups. Expect a tightly contested game with few goals.
• Khemara Keila vs. Preah Khan Reach: With both teams struggling in recent weeks, this match could be a turning point. A win here could provide much-needed momentum for either side.

Expert Predictions: Analyzing Tomorrow's Fixtures

Our expert analysts have scoured the latest stats and team news to provide you with insightful predictions for tomorrow's matches. Here’s what they have to say:

Betting Tips: How to Maximize Your Wagers

Betting on football can be an exciting way to engage with the sport, but it requires careful strategy and informed decisions. Here are some tips to help you place smart bets on tomorrow's Premier League Cambodia matches:

• Consider Over/Under Goals: Given the defensive nature of some teams, betting on under 2.5 goals could be a safe bet for matches like Nagaworld vs. National Defense Ministry.
• Bet on Both Teams to Score: In games where both teams have shown attacking prowess, such as Phnom Penh Crown vs. Boeung Ket Angkor, betting on both teams to score might offer value.
• Explore Handicap Bets: For matches with clear favorites, handicap betting can level the playing field and increase your chances of winning.
• Stay Updated on Team News: Injuries or suspensions can significantly impact a team's performance. Keep an eye on pre-match reports for any last-minute changes.

Detailed Match Analysis: Tactical Insights

To help you understand the intricacies of tomorrow's fixtures, we provide a detailed tactical analysis of each key match:

Fan Reactions and Social Media Buzz

The Premier League Cambodia community is buzzing with anticipation as fans share their thoughts and predictions online. Here’s what’s trending on social media platforms:

• "Can't wait for #PhnomPenhCrownvsBKAngkor! This could be a classic!" - @FootballFan123
• "Nagaworld’s defense is rock solid this season! #NagaworldNDM" - @DefenderKing
• "Khemara Keila needs a win here! Come on guys!" - @KhmerSupporter99

Injury Updates and Player Form

Injuries can drastically alter the dynamics of a match. Here are the latest updates on key players who might impact tomorrow's fixtures:

• Phnom Penh Crown: Their star striker is fit after recovering from a minor injury scare during training.
• Boeung Ket Angkor: A defensive midfielder remains doubtful due to muscle strain but may still feature in some capacity.
• Nagaworld: No new injury concerns; all players are expected to be available.
• National Defense Ministry: A forward is questionable after picking up an ankle knock in training.
• Khemara Keila: Their captain is back from suspension and ready to lead from the front.
• Preah Khan Reach: Concerns over their goalkeeper’s fitness remain; he is listed as doubtful after missing practice sessions due to illness.rahulyhg/Cloud-Detection-in-Satellite-images<|file_sep|>/README.md # Cloud Detection in Satellite images This repository contains code used in paper ["Cloud Detection Using Deep Learning"](https://arxiv.org/abs/2106.09464) submitted at IEEE GRSS Data Fusion Contest 2021. ## Installation The package can be installed by cloning this repository. git clone https://github.com/rahulyhg/Cloud-Detection-in-Satellite-images.git The package uses tensorflow==2.2 which should work fine with GPU. ## Usage ### Training The training script takes following arguments: python train.py --data_path PATH_TO_TRAINING_DATA --output_dir OUTPUT_DIR --batch_size BATCH_SIZE --epochs EPOCHS ### Evaluation The evaluation script takes following arguments: python eval.py --model_path MODEL_PATH --data_path PATH_TO_TEST_DATA --output_dir OUTPUT_DIR ## Dataset The dataset can be downloaded [here](https://drive.google.com/drive/folders/1sWdM7JgYQjQ8EaH5cVwvZrKlSgW0R6DI?usp=sharing). After unzipping it will create folder `Data` which contains two folders `Train` & `Test`. The `Train` folder contains subfolders `Images` & `Labels` where each image in `Images` folder has corresponding label in `Labels` folder (same filename). The `Test` folder contains subfolder `Images` where each image will be evaluated by our model. ## Results | Model | F1 Score | | --- | --- | | Unet | 0.9048 | | DeepLabV3+ | 0.9246 | | DeepLabV3+ + Unet | **0.9348** | ## License [MIT](LICENSE) <|repo_name|>rahulyhg/Cloud-Detection-in-Satellite-images<|file_sep|>/train.py import os import time import argparse import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras.optimizers import Adam from dataset import get_dataset_from_directory from model import UnetModel def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, help='Path containing training data.') parser.add_argument('--output_dir', type=str, help='Path where outputs will be saved.') parser.add_argument('--batch_size', type=int, default=8, help='Batch size.') parser.add_argument('--epochs', type=int, default=50, help='Number of epochs.') return parser.parse_args() def main(): args = parse_args() print(args) data_dir = args.data_path output_dir = args.output_dir if not os.path.exists(output_dir): os.mkdir(output_dir) model = UnetModel() model.summary() train_dataset = get_dataset_from_directory(data_dir + '/Train/Images', data_dir + '/Train/Labels', batch_size=args.batch_size) test_dataset = get_dataset_from_directory(data_dir + '/Test/Images', data_dir + '/Test/Labels', batch_size=args.batch_size) opt = Adam(lr=1e-4) model.compile(optimizer=opt, loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy', tf.keras.metrics.AUC(name='auc')]) checkpoint_path = output_dir + "/cp-{epoch:04d}.ckpt" cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1) history = model.fit(train_dataset, epochs=args.epochs, validation_data=test_dataset, callbacks=[cp_callback], verbose=1) history_dict = history.history acc = history_dict['auc'] val_acc = history_dict['val_auc'] loss = history_dict['loss'] val_loss = history_dict['val_loss'] plt.figure(figsize=(10, 5)) plt.subplot(121) plt.plot(acc) plt.plot(val_acc) plt.xlabel('Epoch') plt.ylabel('AUC') plt.legend(['Train', 'Test'], loc='upper left') plt.subplot(122) plt.plot(loss) plt.plot(val_loss) plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend(['Train', 'Test'], loc='upper left') plt.savefig(output_dir + '/training_curve.png') plt.show() if __name__ == '__main__': main()<|file_sep|># import tensorflow_addons as tfa import tensorflow as tf def dice_coef(y_true, y_pred): y_true_f = tf.reshape(y_true, [-1]) y_pred_f = tf.reshape(y_pred > 0., [-1]) intersection = tf.reduce_sum(y_true_f * y_pred_f) return (2.*intersection + 1.) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + 1.) # def dice_loss(y_true,y_pred): # return 1-dice_coef(y_true,y_pred) def binary_crossentropy(y_true,y_pred): return tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true,y_pred=y_pred) def combined_loss(weight): def loss(y_true,y_pred): return (weight * binary_crossentropy(y_true,y_pred)) - ((1-weight) * dice_coef(y_true,y_pred)) return loss def unet_model(input_shape=(256,None,None), n_filters=32): inputs = tf.keras.layers.Input(shape=input_shape) conv_1_1 = tf.keras.layers.Conv2D(n_filters,kernel_size=(3,3),padding='same')(inputs) bn_1_1 = tf.keras.layers.BatchNormalization()(conv_1_1) act_1_1 = tf.keras.layers.ReLU()(bn_1_1) conv_1_2 = tf.keras.layers.Conv2D(n_filters,kernel_size=(3,3),padding='same')(act_1_1) bn_1_2 = tf.keras.layers.BatchNormalization()(conv_1_2) act_1_2 = tf.keras.layers.ReLU()(bn_1_2) pool_1 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(act_1_2) conv_2_1 = tf.keras.layers.Conv2D(n_filters*2,kernel_size=(3,3),padding='same')(pool_1) bn_2_1 = tf.keras.layers.BatchNormalization()(conv_2_1) act_2_1 = tf.keras.layers.ReLU()(bn_2_1) conv_2_2 = tf.keras.layers.Conv2D(n_filters*2,kernel_size=(3,3),padding='same')(act_2_1) bn_2_2 = tf.keras.layers.BatchNormalization()(conv_2_2) act_2_2 = tf.keras.layers.ReLU()(bn_2_2) pool_2 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(act_2_2) conv_3_1 = tf.keras.layers.Conv2D(n_filters*4,kernel_size=(3,3),padding='same')(pool_2) bn_3_1 = tf.keras.layers.BatchNormalization()(conv_3_1) act_3_1 = tf.keras.layers.ReLU()(bn_3_1) conv_3_p_lateral = conv_to_upsample(conv_out=act_out=act_in=act_out=act_in) upsample_conv_p_lateral_upsampled_and_merged_with_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral_and_conv_downsampled_to_same_resolution_as_act_p_lateral_and_concatenated_with_act_p_lateral upsample_conv_merge_with_conv_downsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_upsampled_to_same_resolution_as_pooling_layer_before_last_pooling_layer_in_encoder_block_of_unet_model_and_concatenated_with_the_output_of_the_corresponding_encoder_block_in_unet_model_before_the_last_encoding_block upconv_merge_with_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_upsampeld_to_same_dimensionality_as_previous_encoding_block_output_and_concatenated_with_the_output_of_the_corresponding_encoding_block_in_unet_model outputs_concatted_final_decoder_blocks_concatenated_with_the_corresponding_encoding_blocks_outputs_for_all_decoder_blocks_after_final_decoding_blocks_are_done_but_before_final_activation_function_is_applied_is_done_by_applying_a_convolutional_filter_that_reduces_the_number_of_channels_from_double_the_number_of_channels_that_were_present_at_the_start_of_the_unet_network_into_a_single