Performance report

PERFORMANCE.md

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+# Performances
+
+- Baseline  
+  - Model: se_resnext50_32x4d
+  - image_size: 512x512
+  - batch_size: 64
+  - grad_accum: 2
+  - augmentations:
+  ```pythonstub
+    def train_aug(image_size=512):
+        return Compose([
+            Resize(image_size, image_size),
+            RandomRotate90(),
+            Flip(),
+            Transpose(),
+        ], p=1)
+
+
+    def valid_aug(image_size=512):
+        return Compose([
+            # CenterCrop(448, 448),
+            Resize(image_size, image_size)
+            # Normalize(),
+        ], p=1)
+  ```
+
+  - Optimizers: 
+  ```yaml
+  criterion_params:
+    criterion: CrossEntropyLoss
+
+  optimizer_params:
+    optimizer: Adam
+    lr: 0.0003
+    weight_decay: 0.0001
+
+  scheduler_params:
+    scheduler: MultiStepLR
+    milestones: [25, 30, 40]
+    gamma: 0.5
+
+  data_params:
+    batch_size: 64
+    num_workers: 4
+    drop_last: False
+
+    image_size: &image_size 512
+    train_csv: "./csv/train_0.csv"
+    valid_csv: "./csv/valid_0.csv"
+    root: "/raid/data/kaggle/recursion-cellular-image-classification/"
+    site: 2
+    channels: [1, 2, 3]
+  ```
+  Results: (fold 0)
+  
+  | Experiment | CV | LB |   
+  |:---------|----|---:|  
+  |c123_s1| 42.9%| 30.6%|  
+  |c123_s2| 41%| 23.6%|  
+  |Ensemble 0.7 * c123_s1 + 0.3 * c123_s2 | - | 32.5 |  
+  
+  c123_s1: using channels=[1,2,3] and site = 1
+