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README.md

@@ -9,50 +9,57 @@ metrics:
 # Model Card for ECGDenoiser
 
     
-- Collection: NeuralLib: Deep Learning Models for Biosignals Processing
-
-- Description: GRU-based model for ECG peak detection
-
-
-```json
-{
-    "architecture": "GRUseq2seq",
-    "model": "ECGDenoiser",
-    "train_dataset": "PTB-XL+MIT-BIH-Noise-Stress-Test-Database",
-    "task": "ecg denoising: removing MA, BW and EM noise",
-    "gpu_model": "NVIDIA GeForce GTX 1080 Ti",
-    "epochs": 200,
-    "optimizer": "Adam",
-    "learning_rate": 0.005,
-    "validation_loss": "NA",
-    "training_time": "NA",
-    "retraining": false
-}
-
-
-## Hyperparameters
-
-bidirectional: true
-dropout: 0
-hid_dim:
-- 64
-- 64
-learning_rate: 0.005
-model_name: ECGDenoiser
-multi_label: false
-n_features: 1
-n_layers: 2
-num_classes: NA
-task: regression
+Collection: NeuralLib: Deep Learning Models for Biosignals Processing
+
+Description: GRU-based model for ECG noise removal. Model and results published in the paper 'Cleaning ECG with Deep Learning: A Denoiser Tested in Industrial Settings'
+
+
+- **Architecture**: GRUseq2seq
+- **Model Name**: ECGDenoiser
+- **Task**: ecg denoising: removing MA, BW and EM noise
+- **Train Dataset**: PTB-XL+MIT-BIH-Noise-Stress-Test-Database
+
+Biosignal(s): ECG
+
+Sampling frequency: 360
+
+
+# Benchmark Results
+
+**Validation Loss**: 0.0000 
+
+**Training Time**: 0.00 seconds 
+
+**FLOPs per timestep**: 0 
+
+**Number of trainable parameters**: 26121 
+
+
+
+# Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| bidirectional | True |
+| dropout | 0 |
+| hid_dim | [64, 64] |
+| learning_rate | 0.005 |
+| model_name | ECGDenoiser |
+| multi_label | False |
+| n_features | 1 |
+| n_layers | 2 |
+| num_classes | NA |
+| task | regression |
+| fc_out_bool | False |
 
 
 # Example
 
-import torch
+import NeuralLib.model_hub as mh
 
-from production_models import ECGDenoiser
+model_name = ECGDenoiser()
 
-model = ECGDenoiser()
+model = mh.ProductionModel(model_name=model_name)
 
 signal = torch.rand(1, 100, 1)  # Example input signal
 

hparams.yaml

@@ -10,3 +10,4 @@ n_features: 1
 n_layers: 2
 num_classes: NA
 task: regression
+fc_out_bool: false

model_weights.pth

@@ -1,3 +1,3 @@
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+oid sha256:b04c18e6862488314f14c1c4a39a1d582bb022aebe016dc72af08586961cfd5a
+size 107002

model_weights_original.pth

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+size 106407

training_info.json

@@ -1,13 +1,17 @@
 {
     "architecture": "GRUseq2seq",
-    "model": "ECGDenoiser",
+    "model_name": "ECGDenoiser",
     "train_dataset": "PTB-XL+MIT-BIH-Noise-Stress-Test-Database",
+    "biosignal": "ECG",
+    "sampling_frequency": 360,
     "task": "ecg denoising: removing MA, BW and EM noise",
     "gpu_model": "NVIDIA GeForce GTX 1080 Ti",
     "epochs": 200,
     "optimizer": "Adam",
     "learning_rate": 0.005,
-    "validation_loss": "NA",
-    "training_time": "NA",
-    "retraining": false
+    "validation_loss": 0,
+    "training_time": 0,
+    "retraining": false,
+    "efficiency_flops": 0,
+    "efficiency_params": 26121
 }