Add application file

app.py

@@ -0,0 +1,21 @@
+import gradio as gr
+import torch
+
+from PIL import Image
+
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True)
+
+def detect(image):
+    results = model(image)  
+    results.render() 
+    return Image.fromarray(results.imgs[0])
+
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+title = "Object detection from Infrared image using YOLOv5n"
+
+
+gr.Interface(detect, inputs, outputs, title=title, theme="huggingface").launch(debug=True)
+   

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8940b5b18fbda2e984f972f621c9e52c906a4b499533b13ee51f86f53c0d9838
+size 175088335

requirements.txt

@@ -0,0 +1,31 @@
+# pip install -r requirements.txt
+
+# base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python-headless
+Pillow
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# plotting ------------------------------------
+seaborn>=0.11.0
+pandas
+
+# export --------------------------------------
+# coremltools>=4.1
+# onnx>=1.9.0
+# scikit-learn==0.19.2  # for coreml quantization
+
+# extras --------------------------------------
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# albumentations>=1.0.3
+thop  # FLOPs computation