Add class filtering to LoadImagesAndLabels() dataloader

[glenn-jocher]

Allows for training on a subset of total classes if include_class list is defined on datasets.py L448:

        include_class = []  # filter labels to include only these classes (optional)

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Enhanced filtering and label modification in YOLOv5 dataset initialization 🔄

📊 Key Changes

• Removed outdated single-class label modification logic.
• Added new logic to:
  • Optionally filter labels to include only specified classes.
  • Combine all classes into a single class if single_cls training is enabled.

🎯 Purpose & Impact

• Purpose:
  • Streamline the process of handling datasets where only a subset of classes need to be used.
  • Provide cleaner support for training models on datasets with a single class.
• Impact:
  • Gives users greater flexibility when training models with specific class requirements, potentially improving model accuracy.
  • Makes it simpler for users to perform single-class training, enhancing the ease of use and effectiveness of the YOLOv5 framework. 💪🎯

[glenn-jocher]

Example training on only 'person' and 'handbag' classes 0 and 26:

train: weights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, 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 🚀 v6.0-6-gfafab84 torch 1.9.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

hyperparameters: lr0=0.01, lrf=0.1, 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 (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/

                 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, 16.5 GFLOPs

Transferred 349/349 items from yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 57 weight, 60 weight (no decay), 60 bias
albumentations: version 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed
train: Scanning '../datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 1470.85it/s]
train: New cache created: ../datasets/coco128/labels/train2017.cache
train: Caching images (0.1GB ram): 100% 128/128 [00:00<00:00, 237.31it/s]
val: Scanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]
val: Caching images (0.1GB ram): 100% 128/128 [00:01<00:00, 114.46it/s]
[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)
[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)
Plotting labels... 

autoanchor: Analyzing anchors... anchors/target = 4.42, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/train/exp
Starting training for 3 epochs...

     Epoch   gpu_mem       box       obj       cls    labels  img_size
       0/2     3.79G   0.04242   0.04363  0.008617        70       640: 100% 8/8 [00:06<00:00,  1.22it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.62it/s]
                 all        128        273      0.719      0.387       0.47      0.308

     Epoch   gpu_mem       box       obj       cls    labels  img_size
       1/2     4.61G   0.04462   0.03664     0.009        25       640: 100% 8/8 [00:03<00:00,  2.55it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.73it/s]
                 all        128        273       0.76      0.381      0.478       0.31

     Epoch   gpu_mem       box       obj       cls    labels  img_size
       2/2     4.61G   0.04471   0.03433   0.00943        89       640: 100% 8/8 [00:03<00:00,  2.54it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.86it/s]
                 all        128        273      0.738      0.391      0.486      0.315

3 epochs completed in 0.005 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, 16.5 GFLOPs
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00,  1.07it/s]
                 all        128        273      0.741       0.39      0.486      0.315
              person        128        254      0.819      0.675      0.778      0.513
             handbag        128         19      0.663      0.105      0.195      0.117
Results saved to runs/train/exp