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{
"model_id": "dummy_discriminator_v1_uid_1",
"model_type": "detection",
"description": "Dummy discriminator model for testing",
"version": "1.0.0",
"author": "kenjon",
"miner_identity": {
"uid": 1,
"coldkey": "5Cvk3JRphVXXrwtJXP3xnDz9UF371P8ndAKfFA4JDxmTucQV",
"hotkey": "5FsPe1tZym7PgP9NqzEsiSG2bvuGCR9fPDBBFqUY1Hm56gwe",
"netuid": 379,
"network": "test",
"subnet": "BitMind"
},
"architecture": {
"base_model": "custom_cnn",
"num_classes": 3,
"input_shape": [
3,
224,
224
],
"output_shape": [
3
],
"model_type": "detection"
},
"preprocessing": {
"normalization": "imagenet",
"resize": [
224,
224
],
"augmentation": [
"random_horizontal_flip"
]
},
"training": {
"optimizer": "adam",
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 10,
"loss_function": "cross_entropy"
},
"performance": {
"accuracy": 0.85,
"precision": 0.83,
"recall": 0.87,
"f1_score": 0.85
},
"dependencies": {
"onnxruntime": ">=1.15.0",
"numpy": ">=1.21.0",
"torch": ">=2.0.0"
},
"usage": {
"input_format": "numpy array (3, 224, 224)",
"output_format": "numpy array (3,) - probabilities for [real, synthetic, semisynthetic]",
"example": "model.predict(image_array)"
},
"submission_info": {
"submitted_at": "2024-01-01T00:00:00Z",
"submission_block": 0,
"model_hash": "adf03e2aca622b5ec63e93af27f58a04f2dcd3e20229f1135099d36fe31e5e18",
"file_size": 22274
}
} |