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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "333ede5f",
"metadata": {},
"outputs": [],
"source": [
"%cd \"../../MYSMP\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84d4c945",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b088ef9",
"metadata": {},
"outputs": [],
"source": [
"%cd \"../MYSMP/semantic-segmentation\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99937292",
"metadata": {},
"outputs": [],
"source": [
"from SemanticModel.model_core import SegmentationModel\n",
"from SemanticModel.training import ModelTrainer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab69a291",
"metadata": {},
"outputs": [],
"source": [
"# initialization loss function\n",
"model = SegmentationModel(\n",
" classes=['bg', 'cacao', 'matarraton', 'abarco'],\n",
" architecture='unet',\n",
" encoder='timm-regnety_120',\n",
" weights='imagenet',\n",
" loss='dice' # Try 'dice' or 'tversky' instead of default\n",
")\n",
"\n",
"# training parameters\n",
"trainer = ModelTrainer(\n",
" model_config=model,\n",
" root_dir='../data',\n",
" epochs=100,\n",
" train_size=1024,\n",
" batch_size=4,\n",
" learning_rate=1e-3, # Increased learning rate\n",
" step_count=3, # More learning rate adjustments\n",
" decay_factor=0.5 # Stronger decay\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38fc7c6f",
"metadata": {},
"outputs": [],
"source": [
"trained_model, metrics = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a053c2ae",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "AgLab - Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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