yolov5_ZWgra/tutorial.ipynb

This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.

Setup

Clone GitHub repository, install dependencies and check PyTorch and GPU.

In [ ]:

!git clone https://github.com/ultralytics/yolov5  # clone
%cd yolov5
%pip install -qr requirements.txt  # install

import torch
import utils
display = utils.notebook_init()  # checks

YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)

Setup complete ✅ (8 CPUs, 51.0 GB RAM, 37.4/166.8 GB disk)

1. Detect

detect.py runs YOLOv5 inference on a variety of sources, downloading models automatically from the latest YOLOv5 release, and saving results to runs/detect. Example inference sources are:

python detect.py --source 0  # webcam
                          img.jpg  # image 
                          vid.mp4  # video
                          path/  # directory
                          'path/*.jpg'  # glob
                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

In [ ]:

!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images
# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)

detect: weights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False
YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 27.8MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.8ms
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 20.1ms
Speed: 0.6ms pre-process, 17.4ms inference, 21.6ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp

        

2. Validate

Validate a model's accuracy on the COCO dataset's val or test splits. Models are downloaded automatically from the latest YOLOv5 release. To show results by class use the --verbose flag.

In [ ]:

# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)
!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip

  0%|          | 0.00/780M [00:00<?, ?B/s]

In [ ]:

# Validate YOLOv5s on COCO val
!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half

val: data=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 52.7MB/s]
val: Scanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10509.20it/s]
val: New cache created: /content/datasets/coco/val2017.cache
                 Class     Images  Instances          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [00:50<00:00,  3.10it/s]
                   all       5000      36335       0.67      0.521      0.566      0.371
Speed: 0.1ms pre-process, 1.0ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)

Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
Loading and preparing results...
DONE (t=5.62s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=77.03s).
Accumulating evaluation results...
DONE (t=14.63s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.374
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.572
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.516
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.566
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.724
Results saved to runs/val/exp

3. Train

Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package

Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml, starting from pretrained --weights yolov5s.pt, or from randomly initialized --weights '' --cfg yolov5s.yaml.

• Pretrained Models are downloaded automatically from the latest YOLOv5 release
• Datasets available for autodownload include: COCO, COCO128, VOC, Argoverse, VisDrone, GlobalWheat, xView, Objects365, SKU-110K.
• Training Results are saved to runs/train/ with incrementing run directories, i.e. runs/train/exp2, runs/train/exp3 etc.
  
  

A Mosaic Dataloader is used for training which combines 4 images into 1 mosaic.

Train on Custom Data with Roboflow 🌟 NEW

Roboflow enables you to easily organize, label, and prepare a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the roboflow pip package.

• Custom Training Example: https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/
• Custom Training Notebook:
  

Label images lightning fast (including with model-assisted labeling)

In [ ]:

#@title Select YOLOv5 🚀 logger {run: 'auto'}
logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML', 'W&B']

if logger == 'TensorBoard':
  %load_ext tensorboard
  %tensorboard --logdir runs/train
elif logger == 'Comet':
  %pip install -q comet_ml
  import comet_ml; comet_ml.init()
elif logger == 'ClearML':
  %pip install -q clearml && clearml-init
elif logger == 'W&B':
  %pip install -q wandb
  import wandb; wandb.login()

In [ ]:

# Train YOLOv5s on COCO128 for 3 epochs
!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache

train: weights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/

Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']
Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...
100% 6.66M/6.66M [00:00<00:00, 41.1MB/s]
Dataset download success ✅ (0.8s), saved to /content/datasets

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs

Transferred 349/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning '/content/datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 9659.25it/s]
train: New cache created: /content/datasets/coco128/labels/train2017.cache
train: Caching images (0.1GB ram): 100% 128/128 [00:00<00:00, 951.31it/s]
val: Scanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]
val: Caching images (0.1GB ram): 100% 128/128 [00:00<00:00, 274.67it/s]

AutoAnchor: 4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp/labels.jpg... 
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/train/exp
Starting training for 3 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/2      3.44G    0.04529    0.06712    0.01835        323        640: 100% 8/8 [00:04<00:00,  1.71it/s]
                 Class     Images  Instances          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.02it/s]
                   all        128        929      0.666      0.611      0.684      0.452

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/2      4.46G    0.04244    0.06423    0.01611        236        640: 100% 8/8 [00:01<00:00,  7.91it/s]
                 Class     Images  Instances          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.19it/s]
                   all        128        929      0.746      0.626      0.722      0.481

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        2/2      4.46G    0.04695    0.06875     0.0173        189        640: 100% 8/8 [00:00<00:00,  8.05it/s]
                 Class     Images  Instances          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.29it/s]
                   all        128        929      0.774      0.647      0.746      0.499

3 epochs completed in 0.003 hours.
Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB
Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB

Validating runs/train/exp/weights/best.pt...
Fusing layers... 
Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
                 Class     Images  Instances          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00,  1.21it/s]
                   all        128        929      0.774      0.647      0.746      0.499
                person        128        254       0.87      0.697      0.806      0.534
               bicycle        128          6      0.759      0.528      0.725      0.444
                   car        128         46      0.774      0.413      0.554      0.239
            motorcycle        128          5      0.791          1      0.962      0.595
              airplane        128          6      0.981          1      0.995      0.689
                   bus        128          7       0.65      0.714      0.755      0.691
                 train        128          3          1      0.573      0.995      0.602
                 truck        128         12      0.613      0.333      0.489      0.263
                  boat        128          6      0.933      0.333      0.507      0.209
         traffic light        128         14       0.76      0.228      0.367      0.209
             stop sign        128          2      0.821          1      0.995      0.821
                 bench        128          9      0.824      0.526      0.676       0.31
                  bird        128         16      0.974          1      0.995      0.611
                   cat        128          4      0.859          1      0.995      0.772
                   dog        128          9          1      0.666      0.883      0.647
                 horse        128          2       0.84          1      0.995      0.622
              elephant        128         17      0.926      0.882       0.93      0.716
                  bear        128          1      0.709          1      0.995      0.995
                 zebra        128          4      0.866          1      0.995      0.922
               giraffe        128          9      0.777      0.778      0.891      0.705
              backpack        128          6      0.894        0.5      0.753      0.294
              umbrella        128         18      0.876      0.783      0.899       0.54
               handbag        128         19      0.799      0.209      0.335      0.179
                   tie        128          7      0.782      0.714      0.787      0.478
              suitcase        128          4      0.658          1      0.945      0.581
               frisbee        128          5      0.726        0.8       0.76      0.701
                  skis        128          1        0.8          1      0.995      0.103
             snowboard        128          7      0.815      0.714      0.852      0.574
           sports ball        128          6      0.649      0.667      0.602      0.307
                  kite        128         10        0.7       0.47      0.546      0.206
          baseball bat        128          4          1      0.497      0.544      0.182
        baseball glove        128          7      0.598      0.429       0.47       0.31
            skateboard        128          5      0.851        0.6      0.685      0.495
         tennis racket        128          7      0.754      0.429      0.544       0.34
                bottle        128         18      0.564      0.333       0.53      0.264
            wine glass        128         16      0.715      0.875      0.907      0.528
                   cup        128         36      0.825      0.639      0.803      0.535
                  fork        128          6          1      0.329        0.5      0.384
                 knife        128         16      0.706      0.625      0.666      0.405
                 spoon        128         22      0.836      0.464      0.619      0.379
                  bowl        128         28      0.763      0.607      0.717      0.516
                banana        128          1      0.886          1      0.995      0.399
              sandwich        128          2          1          0       0.62      0.546
                orange        128          4          1       0.75      0.995      0.622
              broccoli        128         11      0.548      0.443      0.467       0.35
                carrot        128         24        0.7      0.585      0.699      0.458
               hot dog        128          2      0.502          1      0.995      0.995
                 pizza        128          5      0.813          1      0.962      0.747
                 donut        128         14      0.662          1       0.96      0.838
                  cake        128          4      0.868          1      0.995      0.822
                 chair        128         35      0.538      0.571      0.594      0.322
                 couch        128          6      0.924      0.667      0.828      0.538
          potted plant        128         14      0.731      0.786      0.824      0.495
                   bed        128          3      0.736      0.333       0.83      0.425
          dining table        128         13      0.624      0.259      0.494      0.336
                toilet        128          2       0.79          1      0.995      0.846
                    tv        128          2      0.574          1      0.995      0.796
                laptop        128          3          1          0      0.695      0.367
                 mouse        128          2          1          0      0.173     0.0864
                remote        128          8          1       0.62      0.634      0.557
            cell phone        128          8      0.612      0.397      0.437      0.221
             microwave        128          3      0.741          1      0.995      0.766
                  oven        128          5       0.33        0.4      0.449        0.3
                  sink        128          6      0.444      0.333      0.331      0.231
          refrigerator        128          5      0.561        0.8      0.798      0.546
                  book        128         29      0.635      0.276      0.355      0.164
                 clock        128          9      0.766      0.889      0.888       0.73
                  vase        128          2      0.303          1      0.995      0.895
              scissors        128          1          1          0      0.332     0.0397
            teddy bear        128         21      0.842      0.508      0.739      0.499
            toothbrush        128          5      0.787          1      0.928       0.59
Results saved to runs/train/exp

4. Visualize

Comet Logging and Visualization 🌟 NEW

Comet is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with Comet Custom Panels! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!

Getting started is easy:

pip install comet_ml  # 1. install
export COMET_API_KEY=<Your API Key>  # 2. paste API key
python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train

To learn more about all of the supported Comet features for this integration, check out the Comet Tutorial. If you'd like to learn more about Comet, head over to our documentation. Get started by trying out the Comet Colab Notebook:

ClearML Logging and Automation 🌟 NEW

ClearML is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):

• pip install clearml
• run clearml-init to connect to a ClearML server (deploy your own open-source server, or use our free hosted server)

You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).

You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the ClearML Tutorial for details!

Weights & Biases Logging

Weights & Biases (W&B) is integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B pip install wandb, and then train normally (you will be guided through setup on first use).

During training you will see live updates at https://wandb.ai/home, and you can create and share detailed Reports of your results. For more information see the YOLOv5 Weights & Biases Tutorial.

Local Logging

Training results are automatically logged with Tensorboard and CSV loggers to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc.

This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Appendix

Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example.

In [ ]:

import torch

# PyTorch Hub Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

In [ ]:

# YOLOv5 CI
%%shell
rm -rf runs  # remove runs/
m=yolov5n  # official weights
b=runs/train/exp/weights/best  # best.pt checkpoint
python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0  # train
for d in 0 cpu; do  # devices
  for w in $m $b; do  # weights
    python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d  # val
    python detect.py --imgsz 64 --weights $w.pt --device $d  # detect
  done
done
python hubconf.py --model $m  # hub
python models/tf.py --weights $m.pt  # build TF model
python models/yolo.py --cfg $m.yaml  # build PyTorch model
python export.py --weights $m.pt --img 64 --include torchscript  # export

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# Reproduce
for x in (f'yolov5{x}' for x in 'nsmlx'):
  !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed  # speed
  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP

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# Profile
from utils.torch_utils import profile

m1 = lambda x: x * torch.sigmoid(x)
m2 = torch.nn.SiLU()
results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)

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# VOC
for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']):  # batch, model
  !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache

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# Classification train
for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'resnet101.pt', 'efficientnet_b0.pt', 'efficientnet_b1.pt']:
  for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':
    !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}

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# Classification val
!bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G - 50000 images)
!python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate

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# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794
!bash data/scripts/get_coco.sh --test  # download COCO test-dev2017 (7G - 40000 images, test 20000)
!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test

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# TensorRT 
!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # install
!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0  # export
!python detect.py --weights yolov5s.engine --imgsz 640 --device 0  # inference