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Image classification yolov5 model #301

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Image classification yolov5 model #301

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abb4601
updated the requirements.txt
deepthi1107 Mar 4, 2023
ba73896
added rotation of images as data augmentation process
deepthi1107 Mar 5, 2023
de2fd16
lab_image_added_in_generate
deepthi1107 Mar 10, 2023
9a41bcb
lab_image_added_in_generate
deepthi1107 Mar 10, 2023
845dae5
increase_the_number_contrast_images
deepthi1107 Mar 11, 2023
fad7191
objectDetection_train
deepthi1107 Mar 18, 2023
60c5e41
image_claasification
deepthi1107 Mar 21, 2023
271b8c9
image classification using cnn layers
deepthi1107 Mar 29, 2023
ec7af37
object_detection_yolov5s_model
deepthi1107 Mar 29, 2023
39b3057
object_detection_yolov5n_model
deepthi1107 Mar 29, 2023
64bf69e
image_classification_yolov5_model
deepthi1107 Mar 29, 2023
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2 changes: 1 addition & 1 deletion DataCollection/GenerateImages.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,7 @@ def _decay_value_radically_norm(x, centers, max_value, min_value, dev):

l, a, b = cv2.split(lab)

for num in range(0, 5):
for num in range(0, 6):
value = float(decimal.Decimal(random.randrange(10, 1000))/100)
clahe = cv2.createCLAHE(clipLimit=value, tileGridSize=(8,8))
cl = clahe.apply(l)
Expand Down
203 changes: 138 additions & 65 deletions DataCollection/ImageAugmentation.ipynb
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587 changes: 587 additions & 0 deletions ImageClassification/imageClassificationcnn.ipynb
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249 changes: 249 additions & 0 deletions ImageClassification/imageclassification.ipynb
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169 changes: 169 additions & 0 deletions ObjectDetectionYolov5/objectDetection_train.ipynb
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@@ -0,0 +1,169 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "wbvMlHd_QwMG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fd0147bf-9e26-41de-eb98-fd0c58c361cd"
},
"source": [
"!git clone https://github.com/ultralytics/yolov5 \n",
"%cd yolov5\n",
"%pip install -qr requirements.txt \n",
"\n",
"import torch\n",
"import utils\n",
"display = utils.notebook_init() "
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 CPU\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 25.5/107.7 GB disk)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# traing the YOLOV5 model\n",
"\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data /content/yolov5/data/data.yaml --weights yolov5s.pt "
],
"metadata": {
"id": "JIvXqot4f8d1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "86e52852-2746-4466-ea66-e1f3e46c2f45"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/yolov5/data/data.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=None, 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\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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\n",
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
"2023-03-15 11:20:33.924337: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-03-15 11:20:35.565625: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
"2023-03-15 11:20:35.565860: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
"2023-03-15 11:20:35.565890: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:00<00:00, 80.7MB/s]\n",
"\n",
"Overriding model.yaml nc=80 with nc=52\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 153729 models.yolo.Detect [52, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
"Model summary: 214 layers, 7159873 parameters, 7159873 gradients, 16.4 GFLOPs\n",
"\n",
"Transferred 343/349 items from yolov5s.pt\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(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))\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/train/labels.cache... 3619 images, 117 backgrounds, 0 corrupt: 100% 3736/3736 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/valid/images/labels.cache... 4000 images, 33 backgrounds, 0 corrupt: 100% 4033/4033 [00:00<?, ?it/s]\n",
"\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"Plotting labels to runs/train/exp/labels.jpg... \n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 0/2 0G 0.08917 0.04332 0.09752 43 640: 100% 234/234 [1:32:34<00:00, 23.74s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [36:36<00:00, 17.30s/it]\n",
" all 4033 15159 0.00765 0.571 0.00917 0.00362\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 1/2 0G 0.04627 0.03401 0.0956 42 640: 100% 234/234 [1:32:10<00:00, 23.64s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [33:53<00:00, 16.01s/it]\n",
" all 4033 15159 0.0113 0.895 0.0142 0.0071\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 2/2 0G 0.03428 0.03075 0.09459 33 640: 100% 234/234 [1:32:29<00:00, 23.72s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 127/127 [35:07<00:00, 16.59s/it]\n",
" all 4033 15159 0.0116 0.919 0.0149 0.00975\n",
"\n",
"3 epochs completed in 6.383 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.7MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.7MB\n",
"\n",
"Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7150369 parameters, 0 gradients, 16.2 GFLOPs\n",
" Class Images Instances P R mAP50 mAP50-95: 61% 78/127 [22:11<14:06, 17.28s/it]"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "-GdpCP0TItC5"
},
"execution_count": null,
"outputs": []
}
]
}
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