Upload 5 files

inference.py

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+import json
+import math
+import time
+from PIL import Image
+import torch
+from torchvision.transforms import transforms
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = torch.load("path/to/your/model.pth")
+model.to(device)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+with open("tags_8034.json", "r") as f:
+    tags = json.load(f)
+    tags.append("placeholder0")
+    tags = sorted(tags)
+
+image_path = "path/to/your/image.jpg"
+start = time.time()
+img = Image.open(image_path).convert('RGB')
+aspect_ratio = img.width / img.height
+new_height = math.sqrt(512 ** 2 / aspect_ratio)
+new_width = aspect_ratio * new_height
+img.resize((int(new_width), int(new_height)), Image.LANCZOS)
+tensor = transform(img).unsqueeze(0).to(device)
+with torch.no_grad():
+    out = model(tensor)
+probabilities = torch.nn.functional.sigmoid(out[0])
+
+indices = torch.where(probabilities > 0.3)[0]
+values = probabilities[indices]
+
+for i in range(indices.size(0)):
+    print(tags[indices[i]], values[i].item())
+
+end = time.time()
+print(f'Executed in {end - start} seconds')
+print("\n\n", end="")

model_adventurous.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff3bb8d813d7dbc6a017865aefa201feebaadad72f234d5deadc41a9ad163edd
+size 254284770

model_balanced.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c04afa3cb5d9848f642f6ed25ac457d4adba5ea300d55c8c86e52dcfc7f2727a
+size 254284770

model_conservative.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56eca9dbaf3ce1fcb158076c4c47c47bcbff7acf6aac02ec398e4e3b1b7e3a31
+size 254284770