\n",
"Quickstart\n",
"
\n",
" \n",
"Already have a ML model? Run cross-validation to get out-of-sample `pred_probs` and provide `features` (embeddings of the data). Then use the code below to find any potential issues in your dataset (you can also run this code with one of `pred_probs` or `features` instead of both, but less issue types will be considered).\n",
"\n",
"\n",
"
\n",
" \n",
"```python\n",
"from cleanlab import Datalab\n",
"\n",
"lab = Datalab(data=your_dataset, label_name=\"column_name_of_labels\") # include `image_key` to detect low-quality images\n",
"lab.find_issues(pred_probs=pred_probs, features=features)\n",
"\n",
"lab.report()\n",
"```\n",
" \n",
"
\n",
"
\n",
"Bringing Your Own Data (BYOD)?\n",
"\n",
"Load any huggingface dataset or your local image folder dataset, apply relevant transformations, and continue with the rest of the tutorial.\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Define a classification model\n",
"Here, we define a simple neural network with PyTorch. Note this is just a toy model to ensure quick runtimes for the tutorial, you can replace it with any other (larger) PyTorch network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.cnn = nn.Sequential(\n",
" nn.Conv2d(1, 6, 5),\n",
" nn.ReLU(),\n",
" nn.BatchNorm2d(6),\n",
" nn.MaxPool2d(2, 2),\n",
" nn.Conv2d(6, 16, 5, bias=False),\n",
" nn.ReLU(),\n",
" nn.BatchNorm2d(16),\n",
" nn.MaxPool2d(2, 2),\n",
" )\n",
" self.linear = nn.Sequential(nn.LazyLinear(128), nn.ReLU())\n",
" self.output = nn.Sequential(nn.Linear(128, num_classes))\n",
"\n",
" def forward(self, x):\n",
" x = self.embeddings(x)\n",
" x = self.output(x)\n",
" return x\n",
"\n",
" def embeddings(self, x):\n",
" x = self.cnn(x)\n",
" x = torch.flatten(x, 1) # flatten all dimensions except batch\n",
" x = self.linear(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [],
"source": [
"# This (optional) cell is hidden from docs.cleanlab.ai\n",
"\n",
"SEED = 123 # for reproducibility\n",
"np.random.seed(SEED)\n",
"torch.manual_seed(SEED)\n",
"torch.backends.cudnn.deterministic = True\n",
"torch.backends.cudnn.benchmark = True\n",
"torch.cuda.manual_seed_all(SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"