dataset from workflow

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

config.pbtxt

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+testing

model.py

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+# model.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=3, pool=True):
+        super(ConvBlock, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=1)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.pool = nn.MaxPool2d(2) if pool else nn.Identity()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        x = self.pool(x)
+        return x
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super(FeatureExtractor, self).__init__()
+        self.layer1 = ConvBlock(3, 32)
+        self.layer2 = ConvBlock(32, 64)
+        self.layer3 = ConvBlock(64, 128)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        return x
+
+class Classifier(nn.Module):
+    def __init__(self, num_classes=10):
+        super(Classifier, self).__init__()
+        self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        x = self.global_pool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+        return x
+
+class VisionModel(nn.Module):
+    def __init__(self, num_classes=10):
+        super(VisionModel, self).__init__()
+        self.backbone = FeatureExtractor()
+        self.classifier = Classifier(num_classes)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.classifier(x)
+        return x
+
+if __name__ == "__main__":
+    model = VisionModel()
+    dummy_input = torch.randn(1, 3, 224, 224)
+    output = model(dummy_input)
+    print("Output shape:", output.shape)
+