New YOLOv5 Classification Models (#8956)

* Update

* Logger step fix: Increment step with epochs (#8654)

* enhance

* revert

* allow training from scratch

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* Update --img argument from train.py 

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* fix image size from 640 to 128

* suport custom dataloader and augmentation

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* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

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* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

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* Cleanup2

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* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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* Update

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* Update

* Update

* Allow logging models from GenericLogger (#8676)

* enhance

* revert

* allow training from scratch

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

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

* Update --img argument from train.py 

single line

* fix image size from 640 to 128

* suport custom dataloader and augmentation

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

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

* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

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

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

* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

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

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

* Cleanup2

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

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* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

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

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* replace print with logger

* commit steps

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* support final model logging

* update

* update

* update

* update

* remove curses

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* Update classifier.py

* Update __init__.py

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update

* Update

* Update

* Update

* Update dataset download

* Update dataset download

* Update

* Update

* Update

* Update

* Update

* Update

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* Pass imgsz to classify_transforms()

* Update

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* Cos scheduler

* Cos scheduler

* Remove unused args

* Update

* Add seed

* Add seed

* Update

* Update

* Add run(), main()

* Merge master

* Merge master

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Create YOLOv5 BaseModel class (#8829)

* Create BaseModel

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* fix

* Hub load device fix

* Update

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* Add experiment

* Merge master

* Attach names

* weight decay = 1e-4

* weight decay = 5e-5

* update smart_optimizer console printout

* fashion-mnist fix

* Merge master

* Update Table

* Update Table

* Remove destroy process group

* add kwargs to forward()

* fuse fix for resnet50

* nc, names fix for resnet50

* nc, names fix for resnet50

* ONNX CPU inference fix

* revert

* cuda

* if augment or visualize

* if augment or visualize

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* New smart_inference_mode()

* Update README

* Refactor into /classify dir

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* reset defaults

* reset defaults

* fix gpu predict

* warmup

* ema half fix

* spacing

* remove data

* remove cache

* remove denormalize

* save run settings

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* verbose false on initial plots

* new save_yaml() function

* Update ci-testing.yml

* Path(data) CI fix

* Separate classification CI

* fix val

* fix val

* fix val

* smartCrossEntropyLoss

* skip validation on hub load

* autodownload with working dir root

* str(data)

* Dataset usage example

* im_show normalize

* im_show normalize

* add imagenet simple names to multibackend

* Add validation speeds

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* 24-space names

* Update bash scripts

* Update permissions

* Add bash script arguments

* remove verbose

* TRT data fix

* names generator fix

* optimize if names

* update usage

* Add local loading

* Verbose=False

* update names printing

* Add Usage examples

* Add Usage examples

* Add Usage examples

* Add Usage examples

* named_children

* reshape_classifier_outputs

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* update

* update

* fix CI

* fix incorrect class substitution

* fix incorrect class substitution

* remove denormalize

* ravel fix

* cleanup

* update opt file printing

* update opt file printing

* update defaults

* add opt to checkpoint

* Add warning

* Add comment

* plot half bug fix

* Use NotImplementedError

* fix export shape report

* Fix TRT load

* cleanup CI

* profile comment

* CI fix

* Add cls models

* avoid inplace error

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* Fix usage examples

* Update README

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* Update README

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* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

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

utils/plots.py

@@ -388,6 +388,35 @@ def plot_labels(labels, names=(), save_dir=Path('')):
     plt.close()
 
 
+def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
+    # Show classification image grid with labels (optional) and predictions (optional)
+    from utils.augmentations import denormalize
+
+    names = names or [f'class{i}' for i in range(1000)]
+    blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
+                         dim=0)  # select batch index 0, block by channels
+    n = min(len(blocks), nmax)  # number of plots
+    m = min(8, round(n ** 0.5))  # 8 x 8 default
+    fig, ax = plt.subplots(math.ceil(n / m), m)  # 8 rows x n/8 cols
+    ax = ax.ravel() if m > 1 else [ax]
+    # plt.subplots_adjust(wspace=0.05, hspace=0.05)
+    for i in range(n):
+        ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
+        ax[i].axis('off')
+        if labels is not None:
+            s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
+            ax[i].set_title(s, fontsize=8, verticalalignment='top')
+    plt.savefig(f, dpi=300, bbox_inches='tight')
+    plt.close()
+    if verbose:
+        LOGGER.info(f"Saving {f}")
+        if labels is not None:
+            LOGGER.info('True:     ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
+        if pred is not None:
+            LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
+    return f
+
+
 def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()
     # Plot evolve.csv hyp evolution results
     evolve_csv = Path(evolve_csv)