diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..69bbded6d95ff861d2e129235a90347ca4ff6c5d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +assets/videos/vid_1.mp4 filter=lfs diff=lfs merge=lfs -text +assets/videos/vid_2.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..4de1aac20b366aadd25f7b72576caab96fbe79c5 --- /dev/null +++ b/app.py @@ -0,0 +1,149 @@ +from ultralytics import YOLO +import cv2 +import gradio as gr +import numpy as np +import os +import torch +from image_segmenter import ImageSegmenter + +# params +CANCEL_PROCESSING = False + +img_seg = ImageSegmenter(model_type="yolov8m-seg-custom") + +def resize(image): + """ + resize the input nd array + """ + h, w = image.shape[:2] + if h > w: + return cv2.resize(image, (480, 640)) + else: + return cv2.resize(image, (640, 480)) + +def process_image(image): + image = resize(image) + prediction, _ = img_seg.predict(image) + return prediction + + +def process_video(vid_path=None): + vid_cap = cv2.VideoCapture(vid_path) + while vid_cap.isOpened(): + ret, frame = vid_cap.read() + if ret: + print("Making frame predictions ....") + frame = resize(frame) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + prediction, _ = img_seg.predict(frame) + yield prediction + + return None + +def update_segmentation_options(options): + img_seg.is_show_bounding_boxes = True if 'Show Boundary Box' in options else False + img_seg.is_show_segmentation = True if 'Show Segmentation Region' in options else False + img_seg.is_show_segmentation_boundary = True if 'Show Segmentation Boundary' in options else False + +def update_confidence_threshold(thres_val): + img_seg.confidence_threshold = thres_val/100 + +def model_selector(model_type): + + if "Small - Better performance and less accuracy" == model_type: + yolo_model = "yolov8s_seg_custom" + elif "Medium - Balanced performance and accuracy" == model_type: + yolo_model = "yolov8m-seg-custom" + elif "Large - Slow performance and high accuracy" == model_type: + yolo_model = "yolov8m-seg-custom" + else: + yolo_model = "yolov8m-seg-custom" + + img_seg = ImageSegmenter(model_type=yolo_model) + +def cancel(): + CANCEL_PROCESSING = True + +if __name__ == "__main__": + + # gradio gui app + with gr.Blocks() as my_app: + + # title + gr.Markdown("

Hand detection and segmentation

") + + # tabs + with gr.Tab("Image"): + with gr.Row(): + with gr.Column(scale=1): + img_input = gr.Image() + model_type_img = gr.Dropdown( + ["Small - Better performance and less accuracy", + "Medium - Balanced performance and accuracy", + "Large - Slow performance and high accuracy"], + label="Model Type", value="Medium - Balanced performance and accuracy", + info="Select the inference model before running predictions!") + options_checkbox_img = gr.CheckboxGroup(["Show Boundary Box", "Show Segmentation Region"], label="Options") + conf_thres_img = gr.Slider(1, 100, value=60, label="Confidence Threshold", info="Choose the threshold above which objects should be detected") + submit_btn_img = gr.Button(value="Predict") + + with gr.Column(scale=2): + with gr.Row(): + img_output = gr.Image(height=300, label="Segmentation") + + gr.Markdown("## Sample Images") + gr.Examples( + examples=[os.path.join(os.path.dirname(__file__), "assets/images/img_1.jpg"), + os.path.join(os.path.dirname(__file__), "assets/images/img_1.jpg")], + inputs=img_input, + outputs=img_output, + fn=process_image, + cache_examples=True, + ) + + with gr.Tab("Video"): + with gr.Row(): + with gr.Column(scale=1): + vid_input = gr.Video() + model_type_vid = gr.Dropdown( + ["Small - Better performance and less accuracy", + "Medium - Balanced performance and accuracy", + "Large - Slow performance and high accuracy"], + label="Model Type", value="Medium - Balanced performance and accuracy", + info="Select the inference model before running predictions!") + + options_checkbox_vid = gr.CheckboxGroup(["Show Boundary Box", "Show Segmentation Region"], label="Options") + conf_thres_vid = gr.Slider(1, 100, value=60, label="Confidence Threshold", info="Choose the threshold above which objects should be detected") + with gr.Row(): + cancel_btn = gr.Button(value="Cancel") + submit_btn_vid = gr.Button(value="Predict") + + with gr.Column(scale=2): + with gr.Row(): + vid_output = gr.Image(height=300, label="Segmentation") + + gr.Markdown("## Sample Videos") + gr.Examples( + examples=[os.path.join(os.path.dirname(__file__), "assets/videos/vid_1.mp4"), + os.path.join(os.path.dirname(__file__), "assets/videos/vid_2.mp4"),], + inputs=vid_input, + # outputs=vid_output, + # fn=vid_segmenation, + ) + + + # image tab logic + submit_btn_img.click(process_image, inputs=img_input, outputs=img_output) + options_checkbox_img.change(update_segmentation_options, options_checkbox_img, []) + conf_thres_img.change(update_confidence_threshold, conf_thres_img, []) + model_type_img.change(model_selector, model_type_img, []) + + # video tab logic + submit_btn_vid.click(process_video, inputs=vid_input, outputs=vid_output) + model_type_vid.change(model_selector, model_type_vid, []) + cancel_btn.click(cancel, inputs=[], outputs=[]) + options_checkbox_vid.change(update_segmentation_options, options_checkbox_vid, []) + conf_thres_vid.change(update_confidence_threshold, conf_thres_vid, []) + + + my_app.queue(concurrency_count=5, max_size=20).launch(debug=True) \ No newline at end of file diff --git a/assets/images/img_1.jpg b/assets/images/img_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ff4e86e3cab29d45b9897ea588c11610c99975cf Binary files /dev/null and b/assets/images/img_1.jpg differ diff --git a/assets/images/img_2.jpg b/assets/images/img_2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f463982e619ca60dd4d021c5af6d8da96005a554 Binary files /dev/null and b/assets/images/img_2.jpg differ diff --git a/assets/images/img_3.jpg b/assets/images/img_3.jpg new file mode 100644 index 0000000000000000000000000000000000000000..be1e0214ea0fa067ae95c117a39de137301fef26 Binary files /dev/null and b/assets/images/img_3.jpg differ diff --git a/assets/videos/vid_1.mp4 b/assets/videos/vid_1.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..bead0f8139c8c23eb9c003aede602b87d10caca4 --- /dev/null +++ b/assets/videos/vid_1.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce4d3c0dc28cea27fdedbc54f9de88a57cf56981ffe3a31984631341375880af +size 4663136 diff --git a/assets/videos/vid_2.mp4 b/assets/videos/vid_2.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..99544a4c7d5eda3deff46a595eb4eda051d3d19d --- /dev/null +++ b/assets/videos/vid_2.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d041fda27e5fa33e46260e212fc61ed58c6155dd80299b7b1f71ee99629449a5 +size 5585030 diff --git a/datasets/hand_dataset_bbox/README.dataset.txt b/datasets/hand_dataset_bbox/README.dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..ccc2c5f91498b1c2b5a025d6a67463e8ca35bae7 --- /dev/null +++ b/datasets/hand_dataset_bbox/README.dataset.txt @@ -0,0 +1,6 @@ +# Hand Detector > 2023-07-16 1:39pm +https://universe.roboflow.com/vaishanth-ramaraj-uub2b/hand-detector-pjtzx + +Provided by a Roboflow user +License: Public Domain + diff --git a/datasets/hand_dataset_bbox/README.roboflow.txt b/datasets/hand_dataset_bbox/README.roboflow.txt new file mode 100644 index 0000000000000000000000000000000000000000..756225969b04e065f82785b99e122fd74d786e6d --- /dev/null +++ b/datasets/hand_dataset_bbox/README.roboflow.txt @@ -0,0 +1,35 @@ + +Hand Detector - v3 2023-07-16 1:39pm +============================== + +This dataset was exported via roboflow.com on July 16, 2023 at 5:41 PM GMT + +Roboflow is an end-to-end computer vision platform that helps you +* collaborate with your team on computer vision projects +* collect & organize images +* understand and search unstructured image data +* annotate, and create datasets +* export, train, and deploy computer vision models +* use active learning to improve your dataset over time + +For state of the art Computer Vision training notebooks you can use with this dataset, +visit https://github.com/roboflow/notebooks + +To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com + +The dataset includes 262 images. +Hands are annotated in YOLOv8 format. + +The following pre-processing was applied to each image: +* Auto-orientation of pixel data (with EXIF-orientation stripping) +* Resize to 640x640 (Stretch) + +The following augmentation was applied to create 3 versions of each source image: +* Random rotation of between -15 and +15 degrees +* Random shear of between -15° to +15° horizontally and -15° to +15° vertically +* Salt and pepper noise was applied to 5 percent of pixels + +The following transformations were applied to the bounding boxes of each image: +* Random Gaussian blur of between 0 and 0.75 pixels + + diff --git a/datasets/hand_dataset_bbox/data.yaml b/datasets/hand_dataset_bbox/data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..321a97729b23d332b3bfcc20d3d9e88be5aa0fff --- /dev/null +++ b/datasets/hand_dataset_bbox/data.yaml @@ -0,0 +1,12 @@ +names: +- hands +nc: 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--git a/datasets/hand_dataset_seg_v1/README.roboflow.txt b/datasets/hand_dataset_seg_v1/README.roboflow.txt new file mode 100644 index 0000000000000000000000000000000000000000..a20f1b3987effca27a9adf8357b9d340e936658f --- /dev/null +++ b/datasets/hand_dataset_seg_v1/README.roboflow.txt @@ -0,0 +1,33 @@ + +Hand Instance Segmentation - v1 2023-07-16 4:04pm +============================== + +This dataset was exported via roboflow.com on July 16, 2023 at 8:09 PM GMT + +Roboflow is an end-to-end computer vision platform that helps you +* collaborate with your team on computer vision projects +* collect & organize images +* understand and search unstructured image data +* annotate, and create datasets +* export, train, and deploy computer vision models +* use active learning to improve your dataset over time + +For state of the art Computer Vision training notebooks you can use with this dataset, +visit https://github.com/roboflow/notebooks + +To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com + +The dataset includes 165 images. +Hands are annotated in YOLOv8 format. + +The following pre-processing was applied to each image: +* Auto-orientation of pixel data (with EXIF-orientation stripping) +* Resize to 640x640 (Stretch) + +The following augmentation was applied to create 3 versions of each source image: +* Random rotation of between -15 and +15 degrees +* Random shear of between -15° to +15° horizontally and -15° to +15° vertically +* Random Gaussian blur of between 0 and 2.5 pixels +* Salt and pepper noise was applied to 5 percent of pixels + + diff --git a/datasets/hand_dataset_seg_v1/data.yaml b/datasets/hand_dataset_seg_v1/data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d328a5ced94bcb231fc5b4b989b6a142dadc4d94 --- /dev/null +++ b/datasets/hand_dataset_seg_v1/data.yaml @@ -0,0 +1,12 @@ +names: +- hands +nc: 1 +roboflow: + license: Public Domain + project: hand-instance-segmentation + url: 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100644 index 0000000000000000000000000000000000000000..0d203af6c0036ddec3988bc5e64ce52e1aff232d --- /dev/null +++ b/datasets/hand_dataset_seg_v2/README.dataset.txt @@ -0,0 +1,6 @@ +# Hand Instance Segmentation > 2023-07-16 4:28pm +https://universe.roboflow.com/vaishanth-ramaraj-uub2b/hand-instance-segmentation + +Provided by a Roboflow user +License: Public Domain + diff --git a/datasets/hand_dataset_seg_v2/README.roboflow.txt b/datasets/hand_dataset_seg_v2/README.roboflow.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0ff6ab6f3a3502486ce2966307d26e4537be657 --- /dev/null +++ b/datasets/hand_dataset_seg_v2/README.roboflow.txt @@ -0,0 +1,33 @@ + +Hand Instance Segmentation - v3 2023-07-16 4:28pm +============================== + +This dataset was exported via roboflow.com on July 16, 2023 at 8:31 PM GMT + +Roboflow is an end-to-end computer vision platform that helps you +* collaborate with your team on computer vision projects +* collect & organize images +* understand and search unstructured image data +* annotate, and create datasets +* export, train, and deploy computer vision models +* use active learning to improve your dataset over time + +For state of the art Computer Vision training notebooks you can use with this dataset, +visit https://github.com/roboflow/notebooks + +To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com + +The dataset includes 178 images. +Hands are annotated in YOLOv8 format. + +The following pre-processing was applied to each image: +* Auto-orientation of pixel data (with EXIF-orientation stripping) +* Resize to 640x640 (Stretch) + +The following augmentation was applied to create 3 versions of each source image: +* Random rotation of between -15 and +15 degrees +* Random shear of between -15° to +15° horizontally and -15° to +15° vertically +* Random Gaussian blur of between 0 and 2.5 pixels +* Salt and pepper noise was applied to 5 percent of pixels + + diff --git a/datasets/hand_dataset_seg_v2/data.yaml b/datasets/hand_dataset_seg_v2/data.yaml new file mode 100644 index 0000000000000000000000000000000000000000..26d334f6684f0b88c0bf3d6ebcd97db35d5605dc --- /dev/null +++ b/datasets/hand_dataset_seg_v2/data.yaml @@ -0,0 +1,13 @@ +names: +- left_hand +- right_hand +nc: 2 +roboflow: + license: Public Domain + project: hand-instance-segmentation + url: https://universe.roboflow.com/vaishanth-ramaraj-uub2b/hand-instance-segmentation/dataset/3 + version: 3 + workspace: vaishanth-ramaraj-uub2b +test: ../test/images +train: hand_dataset_seg_v2/train/images +val: hand_dataset_seg_v2/valid/images diff --git a/datasets/hand_dataset_seg_v2/test/images/Sign_language_interpreter_vid_3_mp4-20_jpg.rf.8f984f702c876212f6e43bd4c70d27a6.jpg b/datasets/hand_dataset_seg_v2/test/images/Sign_language_interpreter_vid_3_mp4-20_jpg.rf.8f984f702c876212f6e43bd4c70d27a6.jpg new file mode 100644 index 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self.model = YOLO('models/'+ model_type +'.pt') + self.model.to(self.device) + + self.is_show_bounding_boxes = True + self.is_show_segmentation_boundary = False + self.is_show_segmentation = False + self.confidence_threshold = 0.5 + self.cls_clr = {} + + # params + self.bb_thickness = 2 + self.bb_clr = (255, 0, 0) + + # variables + self.masks = {} + + + def get_cls_clr(self, cls_id): + if cls_id in self.cls_clr: + return self.cls_clr[cls_id] + + # gen rand color + r = random.randint(50, 200) + g = random.randint(50, 200) + b = random.randint(50, 200) + self.cls_clr[cls_id] = (r, g, b) + return (r, g, b) + + def predict(self, image): + # params + objects_data = [] + image = image.copy() + predictions = self.model.predict(image) + + cls_ids = predictions[0].boxes.cls.cpu().numpy() + bounding_boxes = predictions[0].boxes.xyxy.int().cpu().numpy() + cls_conf = predictions[0].boxes.conf.cpu().numpy() + # segmentation + if predictions[0].masks: + seg_mask = predictions[0].masks.data.cpu().numpy() + else: + seg_mask = np.array([]) + + for id, cls in enumerate(cls_ids): + cls_clr = self.get_cls_clr(cls) + + # draw filled segmentation region + if seg_mask.any() and cls_conf[id] > self.confidence_threshold: + + self.masks[id] = seg_mask[id] + + if self.is_show_segmentation: + alpha = 0.8 + + # converting the mask from 1 channel to 3 channels + colored_mask = np.expand_dims(seg_mask[id], 0).repeat(3, axis=0) + colored_mask = np.moveaxis(colored_mask, 0, -1) + + # Resize the mask to match the image size, if necessary + if image.shape[:2] != seg_mask[id].shape[:2]: + colored_mask = cv2.resize(colored_mask, (image.shape[1], image.shape[0])) + + # filling the mased area with class color + masked = np.ma.MaskedArray(image, mask=colored_mask, fill_value=cls_clr) + image_overlay = masked.filled() + image = cv2.addWeighted(image, 1 - alpha, image_overlay, alpha, 0) + + # draw bounding box with class name and score + if self.is_show_bounding_boxes and cls_conf[id] > self.confidence_threshold: + (x1, y1, x2, y2) = bounding_boxes[id] + cls_name = self.model.names[cls] + cls_confidence = cls_conf[id] + disp_str = cls_name +' '+ str(round(cls_confidence, 2)) + cv2.rectangle(image, (x1, y1), (x2, y2), cls_clr, self.bb_thickness) + cv2.rectangle(image, (x1, y1), (x1+(len(disp_str)*9), y1+15), cls_clr, -1) + cv2.putText(image, disp_str, (x1+5, y1+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + # object variables + (x1, y1, x2, y2) = bounding_boxes[id] + center = x1+(x2-x1)//2, y1+(y2-y1)//2 + objects_data.append([cls, self.model.names[cls], center, self.masks[id], cls_clr]) + + return image, objects_data \ No newline at end of file diff --git a/inference.ipynb b/inference.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..45389187f7ae511196836c4b93bd26463947e6bc --- /dev/null +++ b/inference.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNtqohVe4YzN49G7sIQBOXj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JLDGjMsgNFCM","executionInfo":{"status":"ok","timestamp":1690240900418,"user_tz":240,"elapsed":16973,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"74300df8-48fc-4764-ba8b-57a42066d65a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/My Drive/My Projects/yolo_custom_training\n"]}],"source":["# This mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","%cd /content/drive/My\\ Drive/My\\ Projects/yolo_custom_training"]},{"cell_type":"markdown","source":["## Install YOLOv8"],"metadata":{"id":"I6NSNz9zbTDA"}},{"cell_type":"code","source":["# Pip install method (recommended)\n","!pip install ultralytics==8.0.20\n","\n","from IPython import display\n","display.clear_output()\n","\n","import ultralytics\n","ultralytics.checks()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NO5AyAFMbH1I","executionInfo":{"status":"ok","timestamp":1690240965944,"user_tz":240,"elapsed":13898,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"bb3ab891-e4f3-432e-f327-53be926606b9"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["Ultralytics YOLOv8.0.20 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.3/78.2 GB disk)\n"]}]},{"cell_type":"markdown","source":["### Inference"],"metadata":{"id":"DNHgOq7yNulq"}},{"cell_type":"markdown","source":["1. On Image"],"metadata":{"id":"c3vO2QsqN2Wl"}},{"cell_type":"code","source":["import cv2\n","import matplotlib.pyplot as plt\n","from image_segmenter import ImageSegmenter\n","from ultralytics import YOLO"],"metadata":{"id":"nc_PmVngNuTb","executionInfo":{"status":"ok","timestamp":1690241018520,"user_tz":240,"elapsed":442,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["img_path = \"assets/images/img_1.jpg\"\n","image = cv2.imread(img_path)\n","image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\n","seg_model = ImageSegmenter(model_type=\"yolov8m-seg-custom\")\n","seg_model.is_show_segmentation = True\n","prediction,_ = seg_model.predict(image)\n","\n","plt.imshow(prediction)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"id":"XT_Y_qSYNiZM","executionInfo":{"status":"ok","timestamp":1690241457353,"user_tz":240,"elapsed":1212,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"4042284b-8d1d-4eaf-e37a-c3ff5a8ae318"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["Ultralytics YOLOv8.0.20 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","YOLOv8m-seg summary (fused): 245 layers, 27223542 parameters, 0 gradients, 110.0 GFLOPs\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["2. On Video"],"metadata":{"id":"g0bEFbOPOJ76"}},{"cell_type":"code","source":["seg_model = ImageSegmenter(model_type=\"yolov8m-seg-custom\")\n","seg_model.is_show_segmentation = True\n","seg_model.is_show_bounding_boxes = False\n","\n","vid_path = \"assets/videos/Sign_language_interpreter_vid_4.mp4\"\n","cap = cv2.VideoCapture(vid_path)\n","\n","# Get video properties\n","fps = cap.get(cv2.CAP_PROP_FPS)\n","width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n","height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n","\n","# Create VideoWriter object\n","output_path = \"output/video_with_segmentation_3.mp4\"\n","fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n","out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n","\n","while cap.isOpened():\n"," ret, frame = cap.read()\n"," if ret:\n"," frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n"," prediction, _ = seg_model.predict(frame)\n"," prediction = cv2.cvtColor(prediction, cv2.COLOR_RGB2BGR)\n","\n"," # Save frame with segmentation overlay\n"," out.write(prediction)\n","\n"," # does not work in goole colab\n"," # cv2.imshow(\"Segmentation\", prediction)\n","\n"," if cv2.waitKey(1) & 0xFF == ord('q'):\n"," break\n"," else:\n"," break\n","\n","# Release resources\n","cap.release()\n","out.release()\n","cv2.destroyAllWindows()"],"metadata":{"id":"wBIIY_N_N5MR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Gradio GUI App"],"metadata":{"id":"zoBlB8ZvPYMn"}},{"cell_type":"code","source":["!pip install gradio"],"metadata":{"id":"2dmPnaKMP6nT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from ultralytics import YOLO\n","import cv2\n","import gradio as gr\n","import numpy as np\n","import os\n","import torch\n","from image_segmenter import ImageSegmenter\n","\n","# params\n","CANCEL_PROCESSING = False\n","\n","img_seg = ImageSegmenter(model_type=\"yolov8m-seg-custom\")\n","\n","def resize(image):\n"," \"\"\"\n"," resize the input nd array\n"," \"\"\"\n"," h, w = image.shape[:2]\n"," if h > w:\n"," return cv2.resize(image, (480, 640))\n"," else:\n"," return cv2.resize(image, (640, 480))\n","\n","def process_image(image):\n"," image = resize(image)\n"," prediction, _ = img_seg.predict(image)\n"," return prediction\n","\n","\n","def process_video(vid_path=None):\n"," vid_cap = cv2.VideoCapture(vid_path)\n"," while vid_cap.isOpened():\n"," ret, frame = vid_cap.read()\n"," if ret:\n"," print(\"Making frame predictions ....\")\n"," frame = resize(frame)\n"," frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n"," prediction, _ = img_seg.predict(frame)\n"," yield prediction\n","\n"," return None\n","\n","def update_segmentation_options(options):\n"," img_seg.is_show_bounding_boxes = True if 'Show Boundary Box' in options else False\n"," img_seg.is_show_segmentation = True if 'Show Segmentation Region' in options else False\n"," img_seg.is_show_segmentation_boundary = True if 'Show Segmentation Boundary' in options else False\n","\n","def update_confidence_threshold(thres_val):\n"," img_seg.confidence_threshold = thres_val/100\n","\n","def model_selector(model_type):\n","\n"," if \"Small - Better performance and less accuracy\" == model_type:\n"," yolo_model = \"yolov8s_seg_custom\"\n"," elif \"Medium - Balanced performance and accuracy\" == model_type:\n"," yolo_model = \"yolov8m-seg-custom\"\n"," elif \"Large - Slow performance and high accuracy\" == model_type:\n"," yolo_model = \"yolov8m-seg-custom\"\n"," else:\n"," yolo_model = \"yolov8m-seg-custom\"\n","\n"," img_seg = ImageSegmenter(model_type=yolo_model)\n","\n","def cancel():\n"," CANCEL_PROCESSING = True\n","\n","if __name__ == \"__main__\":\n","\n"," # gradio gui app\n"," with gr.Blocks() as my_app:\n","\n"," # title\n"," gr.Markdown(\"

Hand detection and segmentation

\")\n","\n"," # tabs\n"," with gr.Tab(\"Image\"):\n"," with gr.Row():\n"," with gr.Column(scale=1):\n"," img_input = gr.Image()\n"," model_type_img = gr.Dropdown(\n"," [\"Small - Better performance and less accuracy\",\n"," \"Medium - Balanced performance and accuracy\",\n"," \"Large - Slow performance and high accuracy\"],\n"," label=\"Model Type\", value=\"Medium - Balanced performance and accuracy\",\n"," info=\"Select the inference model before running predictions!\")\n"," options_checkbox_img = gr.CheckboxGroup([\"Show Boundary Box\", \"Show Segmentation Region\"], label=\"Options\")\n"," conf_thres_img = gr.Slider(1, 100, value=60, label=\"Confidence Threshold\", info=\"Choose the threshold above which objects should be detected\")\n"," submit_btn_img = gr.Button(value=\"Predict\")\n","\n"," with gr.Column(scale=2):\n"," with gr.Row():\n"," img_output = gr.Image(height=300, label=\"Segmentation\")\n","\n"," # gr.Markdown(\"## Sample Images\")\n"," # gr.Examples(\n"," # examples=[os.path.join(os.path.dirname(__file__), \"assets/images/img_1.jpg\"),\n"," # os.path.join(os.path.dirname(__file__), \"assets/images/img_1.jpg\")],\n"," # inputs=img_input,\n"," # outputs=img_output,\n"," # fn=process_image,\n"," # cache_examples=True,\n"," # )\n","\n"," with gr.Tab(\"Video\"):\n"," with gr.Row():\n"," with gr.Column(scale=1):\n"," vid_input = gr.Video()\n"," model_type_vid = gr.Dropdown(\n"," [\"Small - Better performance and less accuracy\",\n"," \"Medium - Balanced performance and accuracy\",\n"," \"Large - Slow performance and high accuracy\"],\n"," label=\"Model Type\", value=\"Medium - Balanced performance and accuracy\",\n"," info=\"Select the inference model before running predictions!\")\n","\n"," options_checkbox_vid = gr.CheckboxGroup([\"Show Boundary Box\", \"Show Segmentation Region\"], label=\"Options\")\n"," conf_thres_vid = gr.Slider(1, 100, value=60, label=\"Confidence Threshold\", info=\"Choose the threshold above which objects should be detected\")\n"," with gr.Row():\n"," cancel_btn = gr.Button(value=\"Cancel\")\n"," submit_btn_vid = gr.Button(value=\"Predict\")\n","\n"," with gr.Column(scale=2):\n"," with gr.Row():\n"," vid_output = gr.Image(height=300, label=\"Segmentation\")\n","\n"," # gr.Markdown(\"## Sample Videos\")\n"," # gr.Examples(\n"," # examples=[os.path.join(os.path.dirname(__file__), \"assets/videos/vid_1.mp4\"),\n"," # os.path.join(os.path.dirname(__file__), \"assets/videos/vid_2.mp4\"),],\n"," # inputs=vid_input,\n"," # # outputs=vid_output,\n"," # # fn=vid_segmenation,\n"," # )\n","\n","\n"," # image tab logic\n"," submit_btn_img.click(process_image, inputs=img_input, outputs=img_output)\n"," options_checkbox_img.change(update_segmentation_options, options_checkbox_img, [])\n"," conf_thres_img.change(update_confidence_threshold, conf_thres_img, [])\n"," model_type_img.change(model_selector, model_type_img, [])\n","\n"," # video tab logic\n"," submit_btn_vid.click(process_video, inputs=vid_input, outputs=vid_output)\n"," model_type_vid.change(model_selector, model_type_vid, [])\n"," cancel_btn.click(cancel, inputs=[], outputs=[])\n"," options_checkbox_vid.change(update_segmentation_options, options_checkbox_vid, [])\n"," conf_thres_vid.change(update_confidence_threshold, conf_thres_vid, [])\n","\n","\n"," my_app.queue(concurrency_count=5, max_size=20).launch(debug=True)"],"metadata":{"id":"_-w7VeyyPbIR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"d6MR47PZTIDs"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/models/yolov8m-seg-custom.pt b/models/yolov8m-seg-custom.pt new file mode 100644 index 0000000000000000000000000000000000000000..7d59b789af144de596efd574046249fb8d6c5582 --- /dev/null +++ b/models/yolov8m-seg-custom.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a93fc97837d963d4e62a28501d01ee1713f34e33239c033b15d6ef639287f8e +size 54813802 diff --git a/models/yolov8s-seg-custom.pt b/models/yolov8s-seg-custom.pt new file mode 100644 index 0000000000000000000000000000000000000000..2fef448e5561753e52d48b481e8eca31130576d8 --- /dev/null +++ b/models/yolov8s-seg-custom.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:661e3e8553e8a6fa95d0eabe6531f72200646be2b2b2ba37fc9617c8857c2921 +size 23842562 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..07d2d3da6f0a687db0ef0febd9505d063b36ca06 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,30 @@ +# Ultralytics requirements +# Usage: pip install -r requirements.txt + +# Base ---------------------------------------- +matplotlib>=3.2.2 +numpy>=1.21.6 +opencv-python>=4.6.0 +Pillow>=7.1.2 +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 +torch>=1.7.0 +torchvision>=0.8.1 +tqdm>=4.64.0 +gradio>=3.36.1 +ultralytics>=8.0.132 +urllib3>=1.26.14 + +# Logging ------------------------------------- +# tensorboard>=2.4.1 +# clearml +# comet + +# Plotting ------------------------------------ +pandas>=1.1.4 +seaborn>=0.11.0 + +# Extras -------------------------------------- +psutil # system utilization +thop>=0.1.1 # FLOPs computation diff --git a/runs/segment/yolo8m_seg_hand/BoxF1_curve.png b/runs/segment/yolo8m_seg_hand/BoxF1_curve.png new file mode 100644 index 0000000000000000000000000000000000000000..348313f2a38f5b38ad7404737b142c0d52d502f4 Binary files /dev/null and b/runs/segment/yolo8m_seg_hand/BoxF1_curve.png differ diff --git a/runs/segment/yolo8m_seg_hand/BoxPR_curve.png b/runs/segment/yolo8m_seg_hand/BoxPR_curve.png new file mode 100644 index 0000000000000000000000000000000000000000..15cfedce42cbb174a17f2e9525ee23b82d134d6f Binary files /dev/null and b/runs/segment/yolo8m_seg_hand/BoxPR_curve.png differ diff --git a/runs/segment/yolo8m_seg_hand/BoxP_curve.png b/runs/segment/yolo8m_seg_hand/BoxP_curve.png new file mode 100644 index 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b/runs/segment/yolo8m_seg_hand/MaskPR_curve.png differ diff --git a/runs/segment/yolo8m_seg_hand/MaskP_curve.png b/runs/segment/yolo8m_seg_hand/MaskP_curve.png new file mode 100644 index 0000000000000000000000000000000000000000..feda36abcc226dcdb83a269649106114fc150825 Binary files /dev/null and b/runs/segment/yolo8m_seg_hand/MaskP_curve.png differ diff --git a/runs/segment/yolo8m_seg_hand/MaskR_curve.png b/runs/segment/yolo8m_seg_hand/MaskR_curve.png new file mode 100644 index 0000000000000000000000000000000000000000..e310cf8cf28fb46c1232b95731934fef056614b2 Binary files /dev/null and b/runs/segment/yolo8m_seg_hand/MaskR_curve.png differ diff --git a/runs/segment/yolo8m_seg_hand/args.yaml b/runs/segment/yolo8m_seg_hand/args.yaml new file mode 100644 index 0000000000000000000000000000000000000000..42c5547b4b8127e3af970a39f8b403d65c637e25 --- /dev/null +++ b/runs/segment/yolo8m_seg_hand/args.yaml @@ -0,0 +1,91 @@ +task: segment +mode: train +model: yolov8m-seg.yaml +data: datasets/hand_dataset_seg_v2/data.yaml +epochs: 50 +patience: 50 +batch: 8 +imgsz: 640 +save: true +cache: false +device: '' +workers: 8 +project: null +name: yolo8m_seg_hand +exist_ok: false +pretrained: false +optimizer: SGD +verbose: false +seed: 0 +deterministic: true +single_cls: false +image_weights: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +overlap_mask: true +mask_ratio: 4 +dropout: false +val: true +save_json: false +save_hybrid: false +conf: 0.001 +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: ultralytics/assets/ +show: false +save_txt: false +save_conf: false +save_crop: false +hide_labels: false +hide_conf: false +vid_stride: 1 +line_thickness: 3 +visualize: false +augment: false +agnostic_nms: false +classes: null +retina_masks: false +boxes: true +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: false +opset: 17 +workspace: 4 +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.937 +weight_decay: 0.001 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +fl_gamma: 0.0 +label_smoothing: 0.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.9 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.1 +copy_paste: 0.1 +cfg: null +v5loader: false +save_dir: runs/segment/yolo8m_seg_hand diff --git a/runs/segment/yolo8m_seg_hand/confusion_matrix.png b/runs/segment/yolo8m_seg_hand/confusion_matrix.png new file mode 100644 index 0000000000000000000000000000000000000000..b096645fda48d44fa3d6d6d7753e9c63c1371ad9 Binary files /dev/null and b/runs/segment/yolo8m_seg_hand/confusion_matrix.png differ diff --git a/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.1 b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.1 new file mode 100644 index 0000000000000000000000000000000000000000..b0713bbe5b5189f39b9684df39c1a6f4c9dba9b9 --- /dev/null +++ b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c819ac16e89c86edf6b2512847ebd1a6543ed7a0cb057612425cd2a1588973cd +size 88 diff --git a/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.2 b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.2 new file mode 100644 index 0000000000000000000000000000000000000000..de7f66da34f986db615e5883da8f2867cec2ad4d --- /dev/null +++ b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.2 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d02cf83e3ba38d80c4e8fd5ab99960c0a7b5cb91998f88b68f413b7f8fbe4766 +size 88 diff --git a/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.3 b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.3 new file mode 100644 index 0000000000000000000000000000000000000000..6f5f9cdcfb52ec0c33528fdacda8b5983dcd2402 --- /dev/null +++ b/runs/segment/yolo8m_seg_hand/events.out.tfevents.1689617564.b8fed2e06310.522.3 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85c1f51b117f267085757446eb680b69770b291ccff2ebc52842a42c64929a9b +size 317480 diff --git a/runs/segment/yolo8m_seg_hand/results.csv b/runs/segment/yolo8m_seg_hand/results.csv new file mode 100644 index 0000000000000000000000000000000000000000..fa72056c77cbfe39001a0f460b74dbd86e20d8ab --- /dev/null +++ b/runs/segment/yolo8m_seg_hand/results.csv @@ -0,0 +1,51 @@ + epoch, train/box_loss, train/seg_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), metrics/precision(M), metrics/recall(M), metrics/mAP50(M), metrics/mAP50-95(M), val/box_loss, val/seg_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2 + 0, 1.7943, 3.4799, 3.8508, 1.6434, 0.20389, 0.4881, 0.34659, 0.27039, 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0000000000000000000000000000000000000000..f9fc190b7c2d583abf799763b596c55c687c85ca --- /dev/null +++ b/runs/segment/yolo8s_seg_hand4/weights/last.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aca6072e8a1bc81ba439b707998c39bdb90f59909235698d8e1e1bc770a61bf6 +size 23842562 diff --git a/training.ipynb b/training.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..30ec3b0a6bd9d5c79bbf2261001229443815d83e --- /dev/null +++ b/training.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyOCqa7FkdGyF/CF5ru7IEXm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["# This mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","%cd /content/drive/My\\ Drive/My\\ Projects/yolo_custom_training"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fl0fMqnr1O1V","executionInfo":{"status":"ok","timestamp":1689616601309,"user_tz":240,"elapsed":23958,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"2add0d4d-0982-47f9-8bbd-a0ead1a50a06"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/My Drive/My Projects/yolo_custom_training\n"]}]},{"cell_type":"code","source":["import os\n","HOME = os.getcwd()\n","print(HOME)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5lwQa3PijESC","executionInfo":{"status":"ok","timestamp":1689616604881,"user_tz":240,"elapsed":136,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"964cd55b-1f1e-44f9-c853-d6d9fcee16f0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/My Drive/My Projects/yolo_custom_training\n"]}]},{"cell_type":"markdown","source":["## Install YOLOv8"],"metadata":{"id":"I6NSNz9zbTDA"}},{"cell_type":"code","source":["# Pip install method (recommended)\n","!pip install ultralytics==8.0.20\n","\n","from IPython import display\n","display.clear_output()\n","\n","import ultralytics\n","ultralytics.checks()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NO5AyAFMbH1I","executionInfo":{"status":"ok","timestamp":1689616619862,"user_tz":240,"elapsed":13292,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"81894275-1307-4f83-90ce-8cc31b480d4e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Ultralytics YOLOv8.0.20 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.3/78.2 GB disk)\n"]}]},{"cell_type":"markdown","source":["### Loading Custom Dataset"],"metadata":{"id":"ntvmUKx7ty-k"}},{"cell_type":"markdown","source":["1. From RoboFlow"],"metadata":{"id":"nQUe0Mect5yu"}},{"cell_type":"code","source":["!pip install roboflow --quiet\n","\n","from roboflow import Roboflow\n","rf = Roboflow(api_key=\"dNG5RONSeTdSdNyKbktT\")\n","project = rf.workspace(\"vaishanth-ramaraj-uub2b\").project(\"hand-detector-pjtzx\")\n","dataset = project.version(3).download(\"yolov8\")"],"metadata":{"id":"uAGS2ETut9Q8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["2. Locally"],"metadata":{"id":"e2Nt70vLuHKv"}},{"cell_type":"code","source":["dataset_type = \"hand_dataset_seg_v2\" # [\"hand_dataset_seg_v1\", \"hand_dataset_seg_v2\"]\n","\n","dataset_path_abs = \"/content/drive/My\\ Drive/My\\ Projects/yolo_custom_training/datasets/\"+dataset_type+\"/data.yaml\"\n","dataset_path_rel = \"datasets/\"+dataset_type+\"/data.yaml\"\n","\n","print(\"Absolute path: \", dataset_path_abs)\n","print(\"Relative path: \", dataset_path_rel)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s7cfNjBbuMiQ","executionInfo":{"status":"ok","timestamp":1689616623473,"user_tz":240,"elapsed":99,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"52efe37e-f1c8-4a65-a3e8-6ed88ef250db"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Absolute path: /content/drive/My\\ Drive/My\\ Projects/yolo_custom_training/datasets/hand_dataset_seg_v2/data.yaml\n","Relative path: datasets/hand_dataset_seg_v2/data.yaml\n"]}]},{"cell_type":"markdown","source":["### Training Custom Model"],"metadata":{"id":"N90ZJOD8vXjF"}},{"cell_type":"code","source":["from ultralytics import YOLO\n","\n","# Load the model.\n","model = YOLO('yolov8m-seg.pt')\n","\n","CUSTOM_MODEL_NAME = \"yolo8m_seg_hand\"\n","\n","# Training with custom dataset\n","results = model.train(\n"," data=dataset_path_rel,\n"," imgsz=640,\n"," epochs=50,\n"," batch=8,\n"," name=CUSTOM_MODEL_NAME)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"60QljZEJvarc","executionInfo":{"status":"ok","timestamp":1689618405675,"user_tz":240,"elapsed":844714,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"1dd83ca3-c94a-4a38-e215-c164fca7b48d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt to yolov8m-seg.pt...\n","100%|██████████| 52.4M/52.4M [00:00<00:00, 95.8MB/s]\n","\n","Ultralytics YOLOv8.0.20 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8m-seg.yaml, data=datasets/hand_dataset_seg_v2/data.yaml, epochs=50, patience=50, batch=8, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=yolo8m_seg_hand, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.1, copy_paste=0.1, cfg=None, v5loader=False, save_dir=runs/segment/yolo8m_seg_hand\n","Overriding model.yaml nc=80 with nc=2\n","\n"," from n params module arguments \n"," 0 -1 1 1392 ultralytics.nn.modules.Conv [3, 48, 3, 2] \n"," 1 -1 1 41664 ultralytics.nn.modules.Conv [48, 96, 3, 2] \n"," 2 -1 2 111360 ultralytics.nn.modules.C2f [96, 96, 2, True] \n"," 3 -1 1 166272 ultralytics.nn.modules.Conv [96, 192, 3, 2] \n"," 4 -1 4 813312 ultralytics.nn.modules.C2f [192, 192, 4, True] \n"," 5 -1 1 664320 ultralytics.nn.modules.Conv [192, 384, 3, 2] \n"," 6 -1 4 3248640 ultralytics.nn.modules.C2f [384, 384, 4, True] \n"," 7 -1 1 1991808 ultralytics.nn.modules.Conv [384, 576, 3, 2] \n"," 8 -1 2 3985920 ultralytics.nn.modules.C2f [576, 576, 2, True] \n"," 9 -1 1 831168 ultralytics.nn.modules.SPPF [576, 576, 5] \n"," 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1] \n"," 12 -1 2 1993728 ultralytics.nn.modules.C2f [960, 384, 2] \n"," 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1] \n"," 15 -1 2 517632 ultralytics.nn.modules.C2f [576, 192, 2] \n"," 16 -1 1 332160 ultralytics.nn.modules.Conv [192, 192, 3, 2] \n"," 17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1] \n"," 18 -1 2 1846272 ultralytics.nn.modules.C2f [576, 384, 2] \n"," 19 -1 1 1327872 ultralytics.nn.modules.Conv [384, 384, 3, 2] \n"," 20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1] \n"," 21 -1 2 4207104 ultralytics.nn.modules.C2f [960, 576, 2] \n"," 22 [15, 18, 21] 1 5160182 ultralytics.nn.modules.Segment [2, 32, 192, [192, 384, 576]] \n","YOLOv8m-seg summary: 331 layers, 27240806 parameters, 27240790 gradients, 110.4 GFLOPs\n","\n","Transferred 531/537 items from pretrained weights\n","\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 86 weight(decay=0.0), 97 weight(decay=0.001), 96 bias\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/My Drive/My Projects/yolo_custom_training/datasets/hand_dataset_seg_v2/train/labels.cache... 165 images, 0 backgrounds, 0 corrupt: 100%|██████████| 165/165 [00:00