Add application file

app.py

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+import gradio as gr
+import cv2
+
+from ultralytics import YOLO
+
+#Cargar modelo entrenado
+model = YOLO('best.pt')
+
+#Definir funcion que ejecuta la interfaz definida (en este caso es solo una interfaz, pero pueden ser algunas)
+#La interfaz solo recibe una entrada (La imagen ingresada en el cargador de path de imagenes), por lo
+# q ue solo se define un parametro de entrada en la funcion.
+def show_results(loaded_image):
+    #Se generan las salidas (detecciones) pidiendo al modelo que prediga a partir de la imagen de entrada
+    outputs = model.predict(source=loaded_image)
+    results = outputs[0].cpu().numpy()
+    #Se carga la imagen usando openCV para poder editarla
+    image = cv2.imread(loaded_image)
+    #Se recorre cada boundingBox detectado y para cada uno se pinta un rectangulo y se escribe un id.
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(image,
+                      (int(det[0]), int(det[1])),
+                      (int(det[2]), int(det[3])),
+                      color=(0, 0, 255),
+                      thickness=2,
+                      lineType=cv2.LINE_AA
+                     )
+        cv2.putText(image,
+                    text =f"id:{i}",
+                    org=(int(det[0]), int(det[1])),
+                    fontFace =cv2.FONT_HERSHEY_SIMPLEX,
+                    fontScale=1,
+                    color=(0,0,255),
+                    thickness=1,
+                    lineType=cv2.LINE_AA
+                   )
+    #Se retornan las 2 salidas definidas(imagen y texto): la imagen resultante (image) y un texto indicando cuantos boundingBox se encontraron
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), len(results.boxes)
+
+
+inputs = [gr.components.Image(type="filepath", label="Input Image"),
+         ]
+outputs= [gr.components.Image(type="numpy", label="Output Image"),
+          gr.Textbox(label="Total:")
+         ]
+#examples  = [['demo1.png'], ['demo2.jpg'], ['demo3.jpg'], ['demo4.png']]
+
+interface = gr.Interface(fn=show_results, 
+                         inputs=inputs,
+                         outputs=outputs,
+                         title="Object Detection",
+                         #En la interfaz se pueden incluir ejemplos de lo que se espera como entrada o entradas. En este caso,
+                         # la entrada es una imagen por lo que se pueden poner imagenes de ejemplo (deben estar subidas en el repositorio
+                         # y con el path correctamente referenciado)
+                         #examples=examples,
+                        )
+interface.launch()

args.yaml

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+task: detect
+mode: train
+model: yolov8n.pt
+data: /content/drive/MyDrive/CuyDetector/data.yaml
+epochs: 5
+patience: 50
+batch: 32
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: yolov8n_testing_01
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 0
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+show: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+vid_stride: 1
+line_width: null
+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: null
+workspace: 4
+nms: false
+lr0: 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: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+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: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+cfg: null
+v5loader: false
+tracker: botsort.yaml
+save_dir: runs/detect/yolov8n_testing_01

events.out.tfevents.1687581375.5da9fcb9d510.1523.1

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+version https://git-lfs.github.com/spec/v1
+oid sha256:29a87f0811105bede02c8a35a9b95be286091781d1d1e0ce792e79603a536210
+size 88

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+hydra-core>=1.2.0
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+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
+ultralytics
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow
+
+# HUB -----------------------------------------
+GitPython>=3.1.24

results.csv

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+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      0,                  1.645,                 2.6314,                 1.9517,                0.00667,                      1,                0.19129,                0.11475,                 1.0169,                 2.8058,                 1.6284,                  2e-05,                  2e-05,                  2e-05
+                      1,                 1.6322,                 2.5365,                 1.9488,                0.00667,                      1,                0.24729,                0.11936,                 1.0316,                 2.8058,                 1.6437,              4.812e-05,              4.812e-05,              4.812e-05
+                      2,                 1.5943,                 2.5872,                  1.997,                0.00667,                      1,                0.28031,                0.12405,                0.99343,                 2.8341,                 1.6276,               6.04e-05,               6.04e-05,               6.04e-05
+                      3,                 1.4867,                 2.5266,                 1.8333,                0.00667,                      1,                0.28265,                0.15697,                 1.0849,                 2.8533,                 1.6092,              5.684e-05,              5.684e-05,              5.684e-05
+                      4,                 1.4518,                 2.4932,                 1.8417,                0.00667,                      1,                0.31144,                0.17644,                 1.2216,                 2.8967,                 1.6712,              3.744e-05,              3.744e-05,              3.744e-05

weights/best.pt

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+oid sha256:9162183dccc0878cca74fde687983e2e265dcd7d484f7d0a43ef838fd04edb26
+size 6241262

weights/last.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d139c476589b6e3c9fe67ac5a866bba1b7e29b389456beb2f01982b3420695e
+size 6241262