Comet Logging and Visualization Integration (#9232)

* add comet to logger interface

* add comet logger

* add support for updated parameters

* clean up offline logger creation

* update callback args for comet logger

* add comet optimizer

* add optimizer config

* add comet README

* update tutorial notebook with Comet section

* add option to log class level metrics

* add support for class level metrics and confusion matrix

* handle errors when adding files to artifacts

* fix typo

* clean resume workflow

* updates for HPO

* update comet README

* fix typo in comet README

* update code snippets in comet README

* update comet links in tutorial

* updated links

* change optimizer batch size param and update comet README image

* update comet section in tutorial

* use prexisting cmd line flags to configure logger

* update artifact upload/download flow

* remove come remove comet logger specific cmd line args

* move downloading weights into comet logger code

* remove extra argparse

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* change checkpoint logging flow to follow offline logger

* update resume flow

* add comet logger to remote dataset property

* update cmd line args in hpo

* set types for integer/float env variables

* update README

* fix typo in README

* default to always logging model predictions

* Update tutorial.ipynb

* Update train.py

* Add Comet to Integrations table

* Update README.md

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

val.py

@@ -259,7 +259,7 @@ def run(
             plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)  # labels
             plot_images(im, output_to_target(out), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names)  # pred
 
-        callbacks.run('on_val_batch_end')
+        callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, out)
 
     # Compute metrics
     stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]  # to numpy
@@ -289,7 +289,7 @@ def run(
     # Plots
     if plots:
         confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        callbacks.run('on_val_end')
+        callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
 
     # Save JSON
     if save_json and len(jdict):