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README.md

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+---
+tags:
+- image-classification
+- pytorch
+---
+
+### Description
+
+This model with transfer learning is trained to recognise Sign Language. 
+The model was trained on the basis of ResNet50. 
+The dataset was used for training https://huggingface.co/datasets/aliciiavs/sign_language_image_dataset
+
+In `model.py` file you can find class of custom model.
+For using model you need download `model.py`, import from it class `CustomResNetModel`, 
+create an example of this class and load and apply model weight from file 
+`sign_language_resnet50.pth`
+Model's metrics:  `loss: 0.5739429252889922` `accuracy: 0.901717`
+
+#### Example of code
+
+```
+import torch
+from huggingface_hub import hf_hub_download
+
+from model import CustomResNetModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = CustomResNetModel(num_classes=24)
+weight_path = hf_hub_download(
+    repo_id="Irgenija/sign_language_resnet50", 
+    filename="sign_language_resnet50.pth",
+)
+if cls.device == "cpu":
+    checkpoint = torch.load(
+        weight_path, map_location=torch.device("cpu")
+    )
+else:
+    checkpoint = torch.load(weight_path)
+model.load_state_dict(checkpoint)
+model = model.to(device) # Optional
+model.eval()
+```

model.py

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+from torch import nn
+from torchvision import models
+
+
+class CustomResNetModel(nn.Module):
+    """Custom model based on ResNet50."""
+
+    def __init__(self, num_classes=24):
+        super(CustomResNetModel, self).__init__()
+        self.model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)
+        for param in self.model.parameters():
+            param.requires_grad = False
+        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
+
+    def forward(self, x):
+        return self.model(x)

sign_language_resnet50.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3493e265ec4d110d036dfe7fe79403fb0208956ab473f979d8ee0a7a49624c2
+size 94560442