General Expert Overview
The upcoming match between Holland Park U23 and Capalaba U23 is anticipated to be a thrilling encounter, with a high likelihood of goals based on the provided data. Both teams have a history of conceding goals, as reflected in the average goals conceded (4.02) and scored (1.32), suggesting an open game. The average total goals of 4.24 indicates that this match could be a high-scoring affair. This expectation is further supported by the relatively low odds for “Over 1.5 Goals” at 95.50 and “Over 2.5 Goals” at 65.00, hinting at an aggressive approach from both teams.
Betting List: First Half
When considering the betting list, it is evident that there is a high probability of no goals being scored in the first half, given the odds favor this outcome with the combined odds for ‘Both Teams to Score Under’ at 97.40% and ‘No Goals’ in the second half at 97.6%. This suggests that there is a strong expectation of minimal activity in the second half of the game, with only about 98.60% certainty in one list. It’s important to consider that this reflects a very strong prediction for no goals in the second half.
Expert Predictions
This section offers insights into the expected outcomes based on current trends and statistical analysis.
Betting List: Over/Under Goals
- “Over 1.5 Goals”: With a probability of 95%, it is likely that more than one goal will be scored in this match, as indicated by odds at 95.50, this suggests that even though there may be some hesitation from one or both teams to score before halftime, there might be many opportunities for goal.
General Overview
The match between Holland Park U23 and Capalaba U23 presents an intriguing scenario for bettors and enthusiasts alike, with predictions pointing towards a high-scoring affair. Given the average total goals of 4.24 and average conceded goals of 4.02, there is significant potential for over 1.5 and even over 2.5 goals during the game.
Betting List: Both Teams To Score
The likelihood of both teams scoring in this match stands at 65.20%, suggesting an evenly matched encounter where defensive lapses could lead to opportunities for both sides.
- “Both Teams To Score”: The chances are slightly over half at 65.20%, indicating that while there is uncertainty, both teams have shown capabilities to break through defenses.
Betting List: Over/Under Specific Goals
- “Over 2.5 Goals”: At 65%, this bet suggests that the game could surpass two and a half goals, reflecting an expectation of an engaging and dynamic match.
- “Over 3.5 Goals”: The odds stand at 61.50%, indicating that while less likely than over 2.5 goals, surpassing three and a half goals is still within reach given the attacking nature suggested by previous stats.
Betting List: Team Outcomes
The predictions point towards a closely contested match with no clear favorite emerging as dominant based on betting odds alone.
- “Away Team To Win”: With odds at 54.50%, there’s nearly an even chance for Capalaba U23 to emerge victorious, reflecting competitive parity between both teams.
Betting List: Both Teams Not To Score
- “Both Teams Not To Score In First Half”: The extremely high probability (98.40%) implies that either team will likely break the deadlock early in the game.
- “Both Teams Not To Score In Second Half”: The odds favor no goals being scored after halftime at 97.60%, suggesting possible strategic adjustments or fatigue impacting performance later in the game.
Betting List: Total Goals & BTTS
- “Over 2.5 BTTS (Both Teams To Score)”: At 52.90%, this indicates that while not highly probable, there is still a significant chance for both teams to score multiple times throughout the match.
The general trend indicates a high-scoring match with potentially many opportunities for scoring but also some risk involved due to low accuracy in defense from both teams.
Expert Betting Predictions
The first betting list focuses on whether or not Holland Park should have an outdoor event planned.
Betting Insights
In our expert opinion, considering Holland’s recent form against their lower league opponents, we predict that while they will lose their upcoming friendly against Barcelona B team on August Bank Holiday Tuesday.
Prediction: Over/Under Goals:
This game has all signs of becoming an aggressive contest with numerous bets available on it.
The average number of goals per game is currently at four with both teams having conceded only three times each during their last six matches.
First Betting Tip:
Pick: ‘Over’ – Total Goals Over – At Odds: @1/8 (8/1)Pick: ‘Holland Park U23’ – Correct Score – At Odds: @9/1 (9/1)Pick: ‘Draw No Bet’ – Draw No Bet – At Odds: @7/4 (7/4)Pick: ‘BTTS – Yes’ – Both Teams To Score – At Odds: @11/10 (11/10)Pick: ‘Draw No Bet’ – Draw No Bet – At Odds: @7/4 (7/4)Pick: ‘Capalaba U23’ – Correct Score – At Odds: @6/1 (6/1)Pick: ‘BTTS – Yes’ – Both Teams To Score – At Odds: @11/10 (11/10)Pick: ‘Under’ – Total Goals Under – At Odds: @8/11 (8/11)Pick: ‘Holland Park U23’ – Correct Score – At Odds: @9/1 (9/1)
Second Betting Tip:
Pick: ‘Under’ – Total Goals Under – At Odds: @8/11 (8/11)Pick: ‘Holland Park U23’ – Correct Score – At Odds: @9/1 (9/1)Pick: ‘Draw No Bet’ – Draw No Bet – At Odds: @7/4 (7/4)Pick: ‘BTTS – Yes’ – Both Teams To Score – At Odds: @11/10 (11/10)Pick: ‘Draw No Bet’ – Draw No Bet – At Odds: @7/4 (7/4)Pick: ‘Capalaba U23’ – Correct Score – At Odds: @6/1 (6/1)</>a-d-o-l-f/jetson-cv/jetson-cv/src/cv_utils.py
import cv2
import numpy as np
from typing import Optional
def load_image(img_path):
img = cv2.imread(img_path)
return img
def get_img_size(img):
h = img.shape[0]
w = img.shape[1]
return h,w
def get_subwindow_avg(im,
pos,
model_sz,
original_sz,
avg_chans):
if not isinstance(pos, tuple):
pos = [pos_x,pos_y]
if not isinstance(pos, tuple):
# assert imutils.is_cv_bgr_image(img)
# if not isinstance(pos, tuple):
# raise TypeError(“pos must be a tuple”)
def get_subwindow_avg(im,
pos,
model_sz,
original_sz,
avg_chans=None):
# if isinstance(pos) != tuple:
# raise TypeError(“pos must be a tuple”)
# if len(model_sz) == None:
# sz = original_sz
# else:
# sz = model_sz
# elif isinstance(model_sz) == int or float:
def crop_center(img,cropx,cropy):
y,x=img.shape[0],img.shape[1]
if len(model_sz) == None:
sz = original_sz
else:
sz = model_sz
if type(sz) == tuple:
else:
sz = x,y
def crop_center(img,cropx,cropy):
if len(sz)==None:
def crop_center(img,half_size=None):
# sz = model_sz / original_size
azamabbas/zoo/src/jetson-inference-tensorrt.py
import cv2
import jetson.utils
import time
class JetsonInference(object):
def __init__(self):
# Jetson Inference
This repository contains some useful functions I wrote to play around with different jetson models.
## Jetson Models
This repository contains implementations of:
– YOLOv5 Nano Darknet53 MobileNetV3 SSD with CSPDarknet53 backbone trained on [COCO dataset](https://cocodataset.org/#home).
– YOLOv5 Nano Darknet53 MobileNetV3 SSD with CSPDarknet53 backbone trained on [Jetson Zoo](https://github.com/dusty-nv/jetson-inference/tree/master/docs/datasets).
– YOLOv5 Nano Darknet53 MobileNetV3 SSD with CSPDarknet53 backbone trained on [Custom Dataset](https://github.com/a-d-o-l-f/jetson-cv/blob/main/dataset/data/custom_data.yml).
– Tiny Yolo V3 MobileNetV3 SSD with CSPDarknet53 backbone trained on [COCO dataset](https://cocodataset.org/#home).
– Tiny Yolo V3 MobileNetV3 SSD with CSPDarknet53 backbone trained on [Jetson Zoo](https://github.com/dusty-nv/jetson-inference/tree/master/docs/datasets).
– Tiny Yolo V3 MobileNetV3 SSD with CSPDarknet53 backbone trained on [Custom Dataset](https://github.com/a-d-o-l-f/jetson-cv/blob/main/dataset/data/custom_data.yml).
– Faster RCNN MobileNetV3 SSD with ResNet50 backbone trained on [COCO dataset](https://cocodataset.org/#home).
– Faster RCNN MobileNetV3 SSD with ResNet50 backbone trained on [Jetson Zoo](https://github.com/dusty-nv/jetson-inference/tree/master/docs/datasets).
– Faster RCNN MobileNetV3 SSD with ResNet50 backbone trained on [Custom Dataset](https://github.com/a-d-o-l-f/jetson-cv/blob/main/dataset/data/custom_data.yml).
All models were trained using YOLOv5 source code and converted using **torchvision** library.
## Jetson Zoo Model Conversion
To convert Jetson Zoo models into ONNX format run:
bash
python src/conversion.py –convert-to-onnx –path-to-zoo-models /home/ubuntu/zoo_models –model-name {jetson_inference_model_name}
**Note:** You can find all Jetson Inference models names in `models.txt` file located inside `/home/ubuntu/zoo_models` directory.
To convert Jetson Zoo models into TRT format run:
bash
python src/conversion.py –convert-to-trt –path-to-zoo-models /home/ubuntu/zoo_models –model-name {jetson_inference_model_name}
## Custom Dataset Model Conversion
To convert Custom Dataset models into ONNX format run:
bash
python src/conversion.py –convert-to-onnx –path-to-custom-models /home/ubuntu/custom_models –model-name {custom_model_name}
**Note:** You can find all Custom Models names inside `data` directory.
To convert Custom Models using your own dataset, you must change `data` file inside `/custom_dataset` folder in order to contain your class names instead of coco dataset class names.
To do so you can use:
bash
python src/utils/data_aug.py
or
bash
python src/utils/data_augmentation.py
and then copy your dataset path
For example:
python train.py –data coco.yaml –batch-size=32 –weights yolov5s.pt –epochs=2000 –device cpu –name yolov5_tiny_mobilenet_voc_2000epoch.pt
## Downloading Model Weights
### On Colab GPU
bash
!apt-get update && apt install libsm6 libxext6 python-opencv
!pip install tensorboard torch torchvision numpy opencv-python jetson-utils==0.0.18 PyYAML==0.12 pillow==8.* matplotlib==3.*
## License
MIT License
Copyright (c) [Aliakbar Abbaszade-Haghi](https://github.com/alishahab)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files(the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software.
# Copyright © Ali Okandan.
#
# Licensed under MIT license.
# https://www.github.com/AIModels/aerial-image-segmentation/
from typing import AnyStr
from pathlib import Path
import os.path as osp
ROOT_DIR = osp.dirname(osp.dirname(__file__))
def get_root():
return Path(__file__).parent.parent
DATA_DIR = osp.join(ROOT_DIR,”data”, “nuscenes”)
MODELS_DIR = “/media/nvidia/models/”
MODEL_PATHS_DIR = “/media/nvidia/model_zoo”
MODEL_PATHS_DIR = os.path.join(MODEL_PATHS_DIR,”onnx”)
NUSCENES_DATASET_ROOT = ‘/media/nvidia/Datasets/NuScenes’
NUSCENES_DATASET_ROOT_TRAIN = os.path.join(NUSCENES_DATASETS_ROOT,”train”)
NUSCENES_DATASETS_ROOT = “/media/nvidia/NVIDIA-Datasets/Nuscenes”
DATA_DIR = ‘/media/nvidia’
class Map():
train_txt = “train.txt”
val_txt=”val.txt”
train_image_folder_path=”train_images/”
train_image_folder_path=’train_images/’
valid_txt=’val.txt’
def __init__(self):
self.train_dir=os.path.join(self.DATA_DIR,self.train_txt)
self.val_dir=os.path.join(self.DATA_DIR,self.val_txt)
self.image_folder_path=os.path.join(self.DATA_DIR,self.train_image_folder_path)
def get_train_dir(self):
return self.train_dir
def get_val_dir(self):
return self.val_dir
def get_image_folder_path(self):
return self.image_folder_path
PATH_TO_COCO_CLASSES_FILE=”/media/nvidia/NVIDIA-Dataset/yolo/coco.names”
class_names = [“person”,”car”,”bus”]
class_names=[“car”,”bus”,”truck”,”trailer”,”person”,”motorcycle”,”traffic_cone”]
PATH_TO_CUSTOM_CLASSES_FILE=”/media/nvidia/NVIDIA-Dataset/yolo/custom.names”
PATH_TO_CUSTOM_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/yolo/yolov5s.pt”
PATH_TO_CUSTOM_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/yolo/cfg/yolov5s.cfg”
PATH_TO_CUSTOM_MODEL=”/media/nvidia/NVIDIA-Dataset/yolo/models/”
PATH_TO_CUSTOM_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/yolo/yolov5m.pt”
PATH_TO_CUSTOM_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/yolo/cfg/yolov5m.cfg”
PATH_TO_CUSTOM_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/yolo/yolov5l.pt”
PATH_TO_CUSTOM_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/yolo/cfg/yolov5l.cfg”
PATH_TO_CUSTOM_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/yolo/yolov5x.pt”
PATH_TO_CUSTOM_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/yolo/cfg/yolov5x.cfg”
PATH_TO_PRETRAINED_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/yolo/tinyyolov4.pt”
PATH_TO_PRETRAINED_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/tinyyolov4/cfg/tinyyolov4.cfg”
PATH_TO_PRETRAINED_MODEL_WEIGHTS=”/media/nvidia/NVIDIA-Dataset/tinyyolov4/models/tinyyolov4.weights”
PATH_TO_PRETRAINED_MODEL_CONFIG=”/media/nvidia/NVIDIA-Dataset/tinyyolov4/cfg/tinyyolov4.cfg”
PATH_TO_PRETRAINED_MODEL_WEIGHTS=”/home/ubuntu/workdir/models/ssd_mobilenet_v3_large_coco_2020_01_14/model.onnx”
PATH_TO_PRETRAINED_MODEL_CONFIG=”ssd_mobilenet_v
