init

README.md

@@ -1,6 +1,6 @@
 ---
 title: YOLOS Object Detection
-emoji: 😻
+emoji: 👤
 colorFrom: pink
 colorTo: purple
 sdk: gradio

app.py

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+from transformers import AutoFeatureExtractor, YolosForObjectDetection
+import gradio as gr
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+import io
+
+
+COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
+          [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]
+
+
+def infer(img, model_name):
+    feature_extractor = AutoFeatureExtractor.from_pretrained(f"hustvl/{model_name}")
+    model = YolosForObjectDetection.from_pretrained(f"hustvl/{model_name}")
+
+    img = Image.fromarray(img)
+    
+    pixel_values = feature_extractor(img, return_tensors="pt").pixel_values
+
+    with torch.no_grad():
+        outputs = model(pixel_values, output_attentions=True)
+    
+    probas = outputs.logits.softmax(-1)[0, :, :-1]
+    keep = probas.max(-1).values > 0.9
+
+    target_sizes = torch.tensor(img.size[::-1]).unsqueeze(0)
+    postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
+    bboxes_scaled = postprocessed_outputs[0]['boxes']
+    
+    res_img = plot_results(img, probas[keep], bboxes_scaled[keep], model)
+    
+    return res_img
+
+def plot_results(pil_img, prob, boxes, model):
+    plt.figure(figsize=(16,10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
+        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
+                                   fill=False, color=c, linewidth=3))
+        cl = p.argmax()
+        text = f'{model.config.id2label[cl.item()]}: {p[cl]:0.2f}'
+        ax.text(xmin, ymin, text, fontsize=15,
+                bbox=dict(facecolor='yellow', alpha=0.5))
+    plt.axis('off')
+    return fig2img(plt.gcf())
+    
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+description = """Object Detection with YOLOS. Choose your model and you're good to go."""
+
+image_in = gr.components.Image()
+image_out = gr.components.Image()
+model_choice = gr.components.Dropdown(["yolos-tiny", "yolos-small", "yolos_base", "yolos-small-300", "yolos-small-dwr"], value="yolos-small")
+
+Iface = gr.Interface(
+    fn=infer,
+    inputs=[image_in,model_choice],
+    outputs=image_out,
+    examples=[["examples/10_People_Marching_People_Marching_2_120.jpg"], ["examples/12_Group_Group_12_Group_Group_12_26.jpg"], ["examples/43_Row_Boat_Canoe_43_247.jpg"]],
+    title="Object Detection with YOLOS",
+    description=description,
+).launch()

requirements.txt

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+transformers
+pillow
+torch
+matplotlib