SKU-110K CVPR2019 Dataset Auto-Download #3167

glenn-jocher · 2021-05-14T12:48:35Z

This PR adds the SKU-110K CVPR2019 Dataset https://github.com/eg4000/SKU110K_CVPR19 to the list of YOLOv5 🚀 auto-download list. This dataset is useful for retail store item detection on shelves.

# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
# Train command: python train.py --data SKU-110K.yaml
# Default dataset location is next to YOLOv5:
#   /parent_folder
#     /datasets/SKU-110K
#     /yolov5

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Integration of the SKU-110K dataset into YOLOv5 for retail object detection.

📊 Key Changes

Added SKU-110K.yaml, which contains paths to training, validation, and test image sets.
Specified single-class detection with the class name 'object'.
Implemented automatic download and setup of the SKU-110K dataset.
Included a script within YAML to convert annotations to YOLOv5 format.

🎯 Purpose & Impact

Purpose: To enable users to train YOLOv5 on the SKU-110K dataset without manual configuration, facilitating research and development in retail object detection.
Impact:
- Streamlines the process of using YOLOv5 with the SKU-110K dataset, promoting ease of use.
- Encourages broader adoption and experimentation with a dataset that's especially relevant for retail inventory and shelf analytics.
- Could lead to improvements in retail automation and management by providing a more accessible tool for object detection model training.

This PR adds the **SKU-110K CVPR2019 Dataset** https://github.com/eg4000/SKU110K_CVPR19 to the list of YOLOv5 🚀 auto-download list. - [x] [COCO](https://cocodataset.org/#home) - [x] [COCO128](https://www.kaggle.com/ultralytics/coco128) - [x] [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - [x] [Argoverse-HD](http://www.cs.cmu.edu/~mengtial/proj/streaming/) - [x] [VisDrone2019-DET](https://github.com/VisDrone/VisDrone-Dataset) - [x] [GlobalWheat-2020](http://www.global-wheat.com/) - [ ] [KITTI](https://www.kaggle.com/twaldo/kitti-object-detection) - [x] [SKU110](https://github.com/eg4000/SKU110K_CVPR19) (grocery store items) - [ ] [CityScapes](https://www.cityscapes-dataset.com/) - [x] [Objects365](https://www.objects365.org/overview.html) - [ ] [OpenImages](https://storage.googleapis.com/openimages/web/index.html) ```yaml # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 # Train command: python train.py --data SKU-110K.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /datasets/SKU-110K # /yolov5 ```

glenn-jocher · 2021-05-14T15:02:55Z

SKU-110K Labels

glenn-jocher · 2021-05-14T15:05:07Z

SKU-110K Train Batches (first 3)

Train Batch 0

Train Batch 1

Train Batch 2

* SKU-110K CVPR2019 Dataset Auto-Download This PR adds the **SKU-110K CVPR2019 Dataset** https://github.com/eg4000/SKU110K_CVPR19 to the list of YOLOv5 🚀 auto-download list. - [x] [COCO](https://cocodataset.org/#home) - [x] [COCO128](https://www.kaggle.com/ultralytics/coco128) - [x] [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - [x] [Argoverse-HD](http://www.cs.cmu.edu/~mengtial/proj/streaming/) - [x] [VisDrone2019-DET](https://github.com/VisDrone/VisDrone-Dataset) - [x] [GlobalWheat-2020](http://www.global-wheat.com/) - [ ] [KITTI](https://www.kaggle.com/twaldo/kitti-object-detection) - [x] [SKU110](https://github.com/eg4000/SKU110K_CVPR19) (grocery store items) - [ ] [CityScapes](https://www.cityscapes-dataset.com/) - [x] [Objects365](https://www.objects365.org/overview.html) - [ ] [OpenImages](https://storage.googleapis.com/openimages/web/index.html) ```yaml # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 # Train command: python train.py --data SKU-110K.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /datasets/SKU-110K # /yolov5 ``` * Update SKU-110K.yaml * Update SKU-110K.yaml (cherry picked from commit 17b0f71)

glenn-jocher · 2021-10-08T22:23:08Z

Example output when SKU-110K is autodownloaded:

(venv) (base) glennjocher@Glenns-iMac yolov5 % python train.py --data SKU-110K.yaml
train: weights=yolov5s.pt, cfg=, data=SKU-110K.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=300, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, 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 🚀 v6.0-70-gfa2344c torch 1.10.0 CPU

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/

WARNING: Dataset not found, nonexistent paths: ['/Users/glennjocher/PycharmProjects/datasets/SKU-110K/val.txt']
Unzipping ../datasets/SKU110K_fixed.tar.gz...
Converting ../datasets/SKU-110K/annotations_train.csv: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8219/8219 [03:30<00:00, 39.05it/s]
Converting ../datasets/SKU-110K/annotations_val.csv: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 588/588 [00:03<00:00, 179.21it/s]
Converting ../datasets/SKU-110K/annotations_test.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2936/2936 [00:31<00:00, 93.65it/s]
Dataset autodownload success, saved to ../datasets

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt to yolov5s.pt...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.0M/14.0M [00:00<00:00, 35.3MB/s]

Overriding model.yaml nc=80 with nc=1

                 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     16182  models.yolo.Detect                      [1, [[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, 7022326 parameters, 7022326 gradients, 15.8 GFLOPs

Transferred 343/349 items from yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 57 weight, 60 weight (no decay), 60 bias
train: Scanning '../datasets/SKU-110K/train' images and labels...8216 found, 0 missing, 0 empty, 3 corrupted: 100%|████████████████████████████████████████████████████████████████████████████████████████| 8219/8219 [00:52<00:00, 157.45it/s]
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train: WARNING: ../datasets/SKU-110K/images/train_1797.jpg: 3 duplicate labels removed
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train: New cache created: ../datasets/SKU-110K/train.cache
val: Scanning '../datasets/SKU-110K/val' images and labels...459 found, 0 missing, 0 empty, 0 corrupted:  78%|██████████████████████████████████████████████████████████████████████████▏                    | 459/588 [00:13<00:01, 118.17it/s]Corrupt JPEG data: bad Huffman code
val: Scanning '../datasets/SKU-110K/val' images and labels...588 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 588/588 [00:15<00:00, 38.75it/s]
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Plotting labels... 
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autoanchor: Analyzing anchors... anchors/target = 4.72, Best Possible Recall (BPR) = 0.9996
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/train/exp
Starting training for 300 epochs...

* SKU-110K CVPR2019 Dataset Auto-Download This PR adds the **SKU-110K CVPR2019 Dataset** https://github.com/eg4000/SKU110K_CVPR19 to the list of YOLOv5 🚀 auto-download list. - [x] [COCO](https://cocodataset.org/#home) - [x] [COCO128](https://www.kaggle.com/ultralytics/coco128) - [x] [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - [x] [Argoverse-HD](http://www.cs.cmu.edu/~mengtial/proj/streaming/) - [x] [VisDrone2019-DET](https://github.com/VisDrone/VisDrone-Dataset) - [x] [GlobalWheat-2020](http://www.global-wheat.com/) - [ ] [KITTI](https://www.kaggle.com/twaldo/kitti-object-detection) - [x] [SKU110](https://github.com/eg4000/SKU110K_CVPR19) (grocery store items) - [ ] [CityScapes](https://www.cityscapes-dataset.com/) - [x] [Objects365](https://www.objects365.org/overview.html) - [ ] [OpenImages](https://storage.googleapis.com/openimages/web/index.html) ```yaml # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 # Train command: python train.py --data SKU-110K.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /datasets/SKU-110K # /yolov5 ``` * Update SKU-110K.yaml * Update SKU-110K.yaml

glenn-jocher added 3 commits May 14, 2021 14:47

Update SKU-110K.yaml

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glenn-jocher merged commit 17b0f71 into master May 14, 2021

glenn-jocher deleted the SKU branch May 14, 2021 15:05

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YOLOv5 release v6.0 #5141

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RuntimeError: non-positive stride is not supported #5535

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YOLOv5 v6.0 compatibility update (draft) ultralytics/yolov3#1855

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YOLOv5 v6.0 compatibility update ultralytics/yolov3#1857

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SKU-110K CVPR2019 Dataset Auto-Download #3167

SKU-110K CVPR2019 Dataset Auto-Download #3167

glenn-jocher commented May 14, 2021 •

edited by UltralyticsAssistant

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glenn-jocher commented May 14, 2021

glenn-jocher commented Oct 8, 2021 •

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SKU-110K CVPR2019 Dataset Auto-Download #3167

SKU-110K CVPR2019 Dataset Auto-Download #3167

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🛠️ PR Summary

🌟 Summary

📊 Key Changes

🎯 Purpose & Impact

glenn-jocher commented May 14, 2021

SKU-110K Labels

glenn-jocher commented May 14, 2021

SKU-110K Train Batches (first 3)

Train Batch 0

Train Batch 1

Train Batch 2

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