diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..0646e9b0ff7829ed72c855781ac06930b6ef2744
--- /dev/null
+++ b/app.py
@@ -0,0 +1,61 @@
+import gradio as gr
+import os
+import subprocess
+import cv2
+import numpy as np
+
+# Define the detect function
+def detect_and_crop(input_image):
+ # Define paths and parameters
+ weights_path = 'yolo/yolov7-main/runs/train/best.pt'
+ img_size = 640
+ conf = 0.20
+ source = 'dataset/images/train/' # Folder for input images
+
+ # Ensure the input image folder exists
+ os.makedirs(source, exist_ok=True)
+
+ # Save the input image to the source directory
+ input_image.save(os.path.join(source, 'input_image.jpg'))
+
+ # Run the detection command
+ command = [
+ 'python', 'yolo/yolov7-main/detect.py',
+ '--weights', weights_path,
+ '--conf-thres', str(conf),
+ '--img-size', str(img_size),
+ '--source', os.path.join(source, 'input_image.jpg'),
+ '--project', 'out/', # Output directory
+ '--name', 'fixed_folder', # Folder name for results
+ '--exist-ok' # Don't increment folder name
+ ]
+
+ # Execute the command
+ subprocess.run(command)
+
+ # Load the result image
+ output_image_path = 'out/fixed_folder/input_image_upscaled.jpg'
+
+ # Check if the image exists
+ if not os.path.exists(output_image_path):
+ return "No output image found."
+
+ # Read the output image
+ output_image = cv2.imread(output_image_path)
+
+ # Convert BGR (OpenCV format) to RGB (Gradio format)
+ output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
+
+ return output_image
+
+# Set up the Gradio interface
+iface = gr.Interface(
+ fn=detect_and_crop,
+ inputs=gr.Image(type="pil"), # Input type
+ outputs=gr.Image(type="numpy"), # Output type
+ title="YOLOv7 Object Detection",
+ description="Upload an image for object detection and cropping."
+)
+
+# Launch the app
+iface.launch()
diff --git a/dataset/images/train/input_image.jpg b/dataset/images/train/input_image.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a0195e26289b6cd8a19c19a679917d6d300f4dd3
Binary files /dev/null and b/dataset/images/train/input_image.jpg differ
diff --git a/detect.py b/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2d6c901f1fd4f1b1bbf4a391a01105a72285f8e
--- /dev/null
+++ b/detect.py
@@ -0,0 +1,194 @@
+import argparse
+import time
+from pathlib import Path
+from PIL import Image
+import numpy as np
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
+from super_image import EdsrModel, ImageLoader
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+ scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+from utils.plots import plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
+
+def detect(save_img=False):
+ source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
+ save_img = not opt.nosave and not source.endswith('.txt') # save inference images
+ webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+ ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+ # Directories
+ save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
+ (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
+
+ # Initialize
+ set_logging()
+ device = select_device(opt.device)
+ half = device.type != 'cpu' # half precision only supported on CUDA
+
+ # Load YOLOv7 model
+ model = attempt_load(weights, map_location=device) # load FP32 model
+ stride = int(model.stride.max()) # model stride
+ imgsz = check_img_size(imgsz, s=stride) # check img_size
+
+ if trace:
+ model = TracedModel(model, device, opt.img_size)
+
+ if half:
+ model.half() # to FP16
+
+ # Set Dataloader
+ vid_path, vid_writer = None, None
+ if webcam:
+ view_img = check_imshow()
+ cudnn.benchmark = True # set True to speed up constant image size inference
+ dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+ else:
+ dataset = LoadImages(source, img_size=imgsz, stride=stride)
+
+ # Get names and colors
+ names = model.module.names if hasattr(model, 'module') else model.names
+ colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
+
+ # Run inference
+ if device.type != 'cpu':
+ model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
+ old_img_w = old_img_h = imgsz
+ old_img_b = 1
+
+ t0 = time.time()
+ for path, img, im0s, vid_cap in dataset:
+ img = torch.from_numpy(img).to(device)
+ img = img.half() if half else img.float() # uint8 to fp16/32
+ img /= 255.0 # 0 - 255 to 0.0 - 1.0
+ if img.ndimension() == 3:
+ img = img.unsqueeze(0)
+
+ # Warmup
+ if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
+ old_img_b = img.shape[0]
+ old_img_h = img.shape[2]
+ old_img_w = img.shape[3]
+ for i in range(3):
+ model(img, augment=opt.augment)[0]
+
+ # Inference
+ t1 = time_synchronized()
+ with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
+ pred = model(img, augment=opt.augment)[0]
+ t2 = time_synchronized()
+
+ # Apply NMS
+ pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+ t3 = time_synchronized()
+
+ # Process detections
+ for i, det in enumerate(pred): # detections per image
+ if webcam: # batch_size >= 1
+ p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
+ else:
+ p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
+
+ p = Path(p) # to Path
+ save_path = str(save_dir / p.name) # img.jpg
+ txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
+ gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
+
+ if len(det):
+ # Rescale boxes from img_size to im0 size
+ det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+ # Find box with maximum confidence score
+ max_conf_idx = det[:, 4].argmax().item()
+ xyxy_max_conf = det[max_conf_idx][:4] # coordinates of max confidence bbox
+
+ # Crop the image using max confidence bbox
+ x1, y1, x2, y2 = map(int, xyxy_max_conf)
+ cropped_img = im0[y1:y2, x1:x2]
+
+ # Convert the cropped image from BGR to RGB format (OpenCV uses BGR by default)
+ cropped_img_rgb = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)
+
+ # Convert the NumPy array (H, W, C) to a PyTorch tensor (C, H, W) and normalize the pixel values
+ cropped_img_tensor = torch.from_numpy(cropped_img_rgb).float().permute(2, 0, 1) / 255.0
+
+ # Add batch dimension since the model expects batches of images
+ inputs = cropped_img_tensor.unsqueeze(0)
+
+ # Load EDSR model with scale 2
+ edsr_model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=4)
+
+ # Perform super-resolution on the cropped image
+ preds = edsr_model(inputs)
+
+ # Convert the result back to a NumPy array and save it
+ upscaled_img = preds.squeeze(0).cpu().detach().numpy().transpose(1, 2, 0) # (C, H, W) -> (H, W, C)
+
+ # Since the output of the model is normalized, we rescale the values back to 0-255
+ upscaled_img = np.clip(upscaled_img * 255.0, 0, 255).astype(np.uint8)
+
+ # Convert the image back to BGR for saving (since OpenCV saves in BGR format)
+ upscaled_img_bgr = cv2.cvtColor(upscaled_img, cv2.COLOR_RGB2BGR)
+
+ # Save the final upscaled image
+ # Save the upscaled image
+ upscaled_img_save_path = save_dir / f"{p.stem}_upscaled.jpg"
+ cv2.imwrite(str(upscaled_img_save_path), upscaled_img_bgr)
+
+
+ # Save cropped image#
+
+ cropped_img_save_path = save_dir / f"{p.stem}_cropped.jpg"
+ cv2.imwrite(str(cropped_img_save_path), cropped_img)
+
+ # Save upscaled image
+ # upscaled_img_save_path = save_dir / f"{p.stem}_upscaled.jpg"
+ # cv2.imwrite(str(upscaled_img_save_path), upscaled_img)
+
+ # Display both the cropped and upscaled images
+ if view_img:
+ cv2.imshow("Cropped Image", cropped_img) # Show cropped image
+ cv2.imshow("Upscaled Image", upscaled_img) # Show upscaled image
+ cv2.waitKey(1)
+
+ # Print time (inference + NMS)
+ print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
+
+ print(f'Done. ({time.time() - t0:.3f}s)')
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
+ parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
+ parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
+ parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
+ parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--view-img', action='store_true', help='display results')
+ parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+ parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+ parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
+ parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+ parser.add_argument('--augment', action='store_true', help='augmented inference')
+ parser.add_argument('--update', action='store_true', help='update all models')
+ parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+ parser.add_argument('--name', default='exp', help='save results to project/name')
+ parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
+ opt = parser.parse_args()
+ print(opt)
+ #check_requirements(exclude=('pycocotools', 'thop'))
+
+ with torch.no_grad():
+ if opt.update: # update all models (to fix SourceChangeWarning)
+ for opt.weights in ['yolov7.pt']:
+ detect()
+ strip_optimizer(opt.weights)
+ else:
+ detect()
\ No newline at end of file
diff --git a/out/fixed_folder/input_image_upscaled.jpg b/out/fixed_folder/input_image_upscaled.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..91d5e6dd8613e21eec99c80fbad6e01a3512b1b2
Binary files /dev/null and b/out/fixed_folder/input_image_upscaled.jpg differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e0595b35b678c9fd373c29ace9a013716d8e184
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,39 @@
+# Usage: pip install -r requirements.txt
+super-image
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5,<1.24.0
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0,!=1.12.0
+torchvision>=0.8.1,!=0.13.0
+tqdm>=4.41.0
+protobuf<4.21.3
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1 # CoreML export
+# onnx>=1.9.0 # ONNX export
+# onnx-simplifier>=0.3.6 # ONNX simplifier
+# scikit-learn==0.19.2 # CoreML quantization
+# tensorflow>=2.4.1 # TFLite export
+# tensorflowjs>=3.9.0 # TF.js export
+# openvino-dev # OpenVINO export
+
+# Extras --------------------------------------
+ipython # interactive notebook
+psutil # system utilization
+thop # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0 # COCO mAP
+# roboflow
diff --git a/traced_model.pt b/traced_model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..b39f6f878859edcee1dbc925e52094efff66b05a
--- /dev/null
+++ b/traced_model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bf9eded7a908a629f7eddf7f64433b03c3595896ef770551837f75df3e0fb04
+size 283660426
diff --git a/yolo/yolov7-main/.gitignore b/yolo/yolov7-main/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..d1bbbbe3982b4485ed9d9ab5bf73971325c8d765
--- /dev/null
+++ b/yolo/yolov7-main/.gitignore
@@ -0,0 +1,263 @@
+# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tif
+*.tiff
+*.heic
+*.JPG
+*.JPEG
+*.PNG
+*.BMP
+*.TIF
+*.TIFF
+*.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
+*.data
+*.json
+*.cfg
+!setup.cfg
+!cfg/yolov3*.cfg
+
+storage.googleapis.com
+runs/*
+data/*
+data/images/*
+!data/*.yaml
+!data/hyps
+!data/scripts
+!data/images
+!data/images/zidane.jpg
+!data/images/bus.jpg
+!data/*.sh
+
+results*.csv
+
+# Datasets -------------------------------------------------------------------------------------------------------------
+coco/
+coco128/
+VOC/
+
+coco2017labels-segments.zip
+test2017.zip
+train2017.zip
+val2017.zip
+
+# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
+*.m~
+*.mat
+!targets*.mat
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+darknet53.conv.74
+yolov3-tiny.conv.15
+*.ptl
+*.trt
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html # Bokeh Plots
+.pg # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
diff --git a/yolo/yolov7-main/LICENSE.md b/yolo/yolov7-main/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/yolo/yolov7-main/LICENSE.md
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+ The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works. By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users. We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors. You can apply it to
+your programs, too.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+ To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights. Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+ For example, if you distribute copies of such a program, whether
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+freedoms that you received. You must make sure that they, too, receive
+or can get the source code. And you must show them these terms so they
+know their rights.
+
+ Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+ For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software. For both users' and
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+
+ Some devices are designed to deny users access to install or run
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+ Finally, every program is threatened constantly by software patents.
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+avoid the special danger that patents applied to a free program could
+make it effectively proprietary. To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+ The precise terms and conditions for copying, distribution and
+modification follow.
+
+ TERMS AND CONDITIONS
+
+ 0. Definitions.
+
+ "This License" refers to version 3 of the GNU General Public License.
+
+ "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+ "The Program" refers to any copyrightable work licensed under this
+License. Each licensee is addressed as "you". "Licensees" and
+"recipients" may be individuals or organizations.
+
+ To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
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+
+ A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+ To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
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diff --git a/yolo/yolov7-main/README.md b/yolo/yolov7-main/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f820acf6e4bd6134c5958284d3b86d42526048d0
--- /dev/null
+++ b/yolo/yolov7-main/README.md
@@ -0,0 +1,310 @@
+# Official YOLOv7
+
+Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
+
+[](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
+[](https://huggingface.co/spaces/akhaliq/yolov7)
+
+[](https://arxiv.org/abs/2207.02696)
+
+
+
+## Web Demo
+
+- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [](https://huggingface.co/spaces/akhaliq/yolov7)
+
+## Performance
+
+MS COCO
+
+| Model | Test Size | APtest | AP50test | AP75test | batch 1 fps | batch 32 average time |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
+| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
+| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
+| | | | | | | |
+| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
+| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
+| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
+| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
+
+## Installation
+
+Docker environment (recommended)
+ Expand
+
+``` shell
+# create the docker container, you can change the share memory size if you have more.
+nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
+
+# apt install required packages
+apt update
+apt install -y zip htop screen libgl1-mesa-glx
+
+# pip install required packages
+pip install seaborn thop
+
+# go to code folder
+cd /yolov7
+```
+
+ Expand
+
+
+```shell
+wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
+python export.py --weights yolov7-tiny.pt --grid --include-nms
+git clone https://github.com/Linaom1214/tensorrt-python.git
+python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
+
+# Or use trtexec to convert ONNX to TensorRT engine
+/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
+```
+
+ Expand
+
+* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
+* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
+* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
+* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
+* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
+* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
+* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
+* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
+* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
+* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
+
+
+
+
+
+
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+
+
+
+ 
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+