Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,454 Bytes
76adb70 d14e05c 447aeb3 d14e05c 81c8492 8be205e 81c8492 77ea250 d14e05c e78a6e2 76adb70 f4f64b2 77ea250 76adb70 d14e05c 447aeb3 d14e05c f99f7da 76adb70 d14e05c 76adb70 29ee754 76adb70 29ee754 76adb70 50e326d 76adb70 50e326d 76adb70 29ee754 76adb70 29ee754 76adb70 2a05c12 76adb70 d4728ae 5009395 d4728ae 76adb70 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
import gradio as gr
from ultralytics import YOLO
import spaces
import supervision as sv
BOX_ANNOTATOR = sv.BoxAnnotator()
LABEL_ANNOTATOR = sv.LabelAnnotator()
category_dict = {
0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
}
def attempt_download_from_hub(repo_id, hf_token=None):
# https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py
from huggingface_hub import hf_hub_download, list_repo_files
from huggingface_hub.utils._errors import RepositoryNotFoundError
from huggingface_hub.utils._validators import HFValidationError
try:
repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token)
model_file = [f for f in repo_files if f.endswith('.safetensors')][0]
file = hf_hub_download(
repo_id=repo_id,
filename=model_file,
repo_type='model',
token=hf_token,
)
return file
except (RepositoryNotFoundError, HFValidationError):
return None
@spaces.GPU(duration=10)
def LeYOLO_inference(image, model_id, image_size, conf_threshold, iou_threshold):
MODEL_PATH = attempt_download_from_hub(model_id)
model = model = YOLO(MODEL_PATH)
model.to('cuda')
results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
labels = [
f"{category_dict[class_id]} {confidence:.2f}"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = BOX_ANNOTATOR.annotate(
scene=image, detections=detections)
annotated_image = LABEL_ANNOTATOR.annotate(
scene=annotated_image, detections=detections, labels=labels)
return annotated_image
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image")
model_id = gr.Dropdown(
label="Model",
choices=[
"lhollard/leyolo-nano",
"lhollard/leyolo-small",
"lhollard/leyolo-medium",
"lhollard/leyolo-large",
],
value="lhollard/leyolo-medium",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.25,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.45,
)
LeYOLO_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="pil", label="Annotated Image")
LeYOLO_infer.click(
fn=LeYOLO_inference,
inputs=[
image,
model_id,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
)
gr.Examples(
examples=[
[
"dog.jpeg",
"lhollard/leyolo-medium",
640,
0.25,
0.45,
],
[
"zidane.jpg",
"lhollard/leyolo-medium",
640,
0.25,
0.45,
],
],
fn=LeYOLO_inference,
inputs=[
image,
model_id,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
cache_examples="lazy",
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
LeYOLO: New Scalable and Efficient CNN Architecture for Object Detection
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!<br>
<a href='https://github.com/LilianHollard' target='_blank'>LilianHollard</a> |
<a href='https://github.com/LilianHollard/LeYOLO' target='_blank'>LeYOLO Github</a> |
<a href='https://arxiv.org/abs/2406.14239' target='_blank'>Paper</a> |
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> |
<a href='https://github.com/kadirnar' target='_blank'>Github</a> |
<a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> |
<a href='https://github.com/roboflow/supervision/' target='_blank'>Supervision</a>
</h3>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True) |