Add efficientnet_custom_v2_2 trained on 1000 images per class and augmentation of unbalanced classes

efficientnet_custom_v2_2/benchmark_results.json

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+{
+  "benchmarked_devices": [
+    "cuda"
+  ],
+  "parameters": {
+    "total_params": 4030606,
+    "trainable_params": 3178798,
+    "total_params_m": 4.030606,
+    "trainable_params_m": 3.178798
+  },
+  "cuda": {
+    "memory": {
+      "baseline_memory_mb": 70.193359375,
+      "peak_memory_mb": 80.720703125,
+      "current_memory_mb": 70.76806640625,
+      "inference_memory_mb": 10.52734375,
+      "device": "cuda"
+    },
+    "inference_single": {
+      "batch_size": 1,
+      "mean_latency_ms": 5.136066239720094,
+      "std_latency_ms": 0.008697872240662149,
+      "median_latency_ms": 5.135121497005457,
+      "p95_latency_ms": 5.151860694604693,
+      "p99_latency_ms": 5.156857259207754,
+      "min_latency_ms": 5.121371003042441,
+      "max_latency_ms": 5.1598530044429936,
+      "latency_per_image_ms": 5.136066239720094,
+      "throughput_img_per_sec": 194.70153875089005,
+      "device": "cuda"
+    },
+    "inference_batch32": {
+      "batch_size": 32,
+      "mean_latency_ms": 18.34433564043138,
+      "std_latency_ms": 0.22169971671972608,
+      "median_latency_ms": 18.217552998976316,
+      "p95_latency_ms": 18.770105749717914,
+      "p99_latency_ms": 18.77734032314038,
+      "min_latency_ms": 18.15599100518739,
+      "max_latency_ms": 18.78215800388716,
+      "latency_per_image_ms": 0.5732604887634807,
+      "throughput_img_per_sec": 1744.407681326501,
+      "device": "cuda"
+    }
+  }
+}

efficientnet_custom_v2_2/class_mapping.txt

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+battery:0
+car_battery:1
+cardboard:2
+food_organics:3
+glass:4
+light_bulb:5
+metal:6
+mirror:7
+miscellaneous_trash:8
+neon:9
+paper:10
+pharmacy:11
+plastic:12
+printer_cartridge:13
+textile_trash:14
+tire:15
+vegetation:16
+wood:17

efficientnet_custom_v2_2/classification_report.txt

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+Classification Report
+================================================================================
+
+                     precision    recall  f1-score   support
+
+            battery     1.0000    0.9928    0.9964       276
+        car_battery     0.9959    1.0000    0.9979       243
+          cardboard     0.9062    0.9170    0.9116       253
+      food_organics     0.9436    0.9366    0.9401       268
+              glass     0.9685    0.9179    0.9425       268
+         light_bulb     1.0000    0.9883    0.9941       256
+              metal     0.8826    0.8859    0.8843       263
+             mirror     0.9957    1.0000    0.9978       229
+miscellaneous_trash     0.9121    0.8755    0.8934       249
+               neon     0.9784    0.9956    0.9869       227
+              paper     0.8996    0.8630    0.8809       270
+           pharmacy     0.9881    0.9921    0.9901       252
+            plastic     0.8000    0.8681    0.8327       235
+  printer_cartridge     0.9668    0.9957    0.9811       234
+      textile_trash     0.9277    0.9083    0.9179       240
+               tire     0.9763    0.9960    0.9860       248
+         vegetation     0.9810    0.9961    0.9885       259
+               wood     1.0000    1.0000    1.0000       230
+
+           accuracy                         0.9507      4500
+          macro avg     0.9512    0.9516    0.9512      4500
+       weighted avg     0.9511    0.9507    0.9507      4500

efficientnet_custom_v2_2/model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cabaa0021ae26cccf4a352697b9b86878a3ef4f8b6f535db1acbbd9054f1837e
+size 16402067

efficientnet_custom_v2_2/training_history.txt

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+Training History
+================================================================================
+
+Epoch 1:
+  Train Loss: 1.225998
+  Train Accuracy: 0.682222
+  Val Accuracy: 0.852000
+  Learning Rate: 0.00010000
+  Epoch Time: 18.49s
+
+Epoch 2:
+  Train Loss: 0.458198
+  Train Accuracy: 0.858444
+  Val Accuracy: 0.902444
+  Learning Rate: 0.00010000
+  Epoch Time: 17.16s
+
+Epoch 3:
+  Train Loss: 0.304337
+  Train Accuracy: 0.906889
+  Val Accuracy: 0.917333
+  Learning Rate: 0.00010000
+  Epoch Time: 17.17s
+
+Epoch 4:
+  Train Loss: 0.238223
+  Train Accuracy: 0.924741
+  Val Accuracy: 0.925778
+  Learning Rate: 0.00010000
+  Epoch Time: 17.38s
+
+Epoch 5:
+  Train Loss: 0.191649
+  Train Accuracy: 0.939407
+  Val Accuracy: 0.930444
+  Learning Rate: 0.00010000
+  Epoch Time: 17.66s
+
+Epoch 6:
+  Train Loss: 0.153464
+  Train Accuracy: 0.951407
+  Val Accuracy: 0.929333
+  Learning Rate: 0.00010000
+  Epoch Time: 17.50s
+
+Epoch 7:
+  Train Loss: 0.124825
+  Train Accuracy: 0.960815
+  Val Accuracy: 0.937778
+  Learning Rate: 0.00010000
+  Epoch Time: 17.34s
+
+Epoch 8:
+  Train Loss: 0.109680
+  Train Accuracy: 0.967185
+  Val Accuracy: 0.941778
+  Learning Rate: 0.00010000
+  Epoch Time: 17.32s
+
+Epoch 9:
+  Train Loss: 0.101151
+  Train Accuracy: 0.968296
+  Val Accuracy: 0.941111
+  Learning Rate: 0.00010000
+  Epoch Time: 17.26s
+
+Epoch 10:
+  Train Loss: 0.092170
+  Train Accuracy: 0.970963
+  Val Accuracy: 0.937778
+  Learning Rate: 0.00010000
+  Epoch Time: 17.19s
+
+Epoch 11:
+  Train Loss: 0.070614
+  Train Accuracy: 0.975926
+  Val Accuracy: 0.944444
+  Learning Rate: 0.00010000
+  Epoch Time: 17.26s
+
+Epoch 12:
+  Train Loss: 0.067904
+  Train Accuracy: 0.978296
+  Val Accuracy: 0.939556
+  Learning Rate: 0.00010000
+  Epoch Time: 17.99s
+
+Epoch 13:
+  Train Loss: 0.065085
+  Train Accuracy: 0.979333
+  Val Accuracy: 0.939556
+  Learning Rate: 0.00010000
+  Epoch Time: 18.03s
+
+Epoch 14:
+  Train Loss: 0.063790
+  Train Accuracy: 0.979259
+  Val Accuracy: 0.940444
+  Learning Rate: 0.00010000
+  Epoch Time: 17.87s
+
+Epoch 15:
+  Train Loss: 0.054066
+  Train Accuracy: 0.983037
+  Val Accuracy: 0.939556
+  Learning Rate: 0.00010000
+  Epoch Time: 17.66s
+
+Epoch 16:
+  Train Loss: 0.054058
+  Train Accuracy: 0.981852
+  Val Accuracy: 0.942667
+  Learning Rate: 0.00010000
+  Epoch Time: 18.07s
+
+Epoch 17:
+  Train Loss: 0.050998
+  Train Accuracy: 0.984444
+  Val Accuracy: 0.942222
+  Learning Rate: 0.00010000
+  Epoch Time: 17.81s
+
+Epoch 18:
+  Train Loss: 0.041915
+  Train Accuracy: 0.987185
+  Val Accuracy: 0.948667
+  Learning Rate: 0.00005000
+  Epoch Time: 17.68s
+
+Epoch 19:
+  Train Loss: 0.034541
+  Train Accuracy: 0.989407
+  Val Accuracy: 0.945111
+  Learning Rate: 0.00005000
+  Epoch Time: 17.52s
+
+Epoch 20:
+  Train Loss: 0.031690
+  Train Accuracy: 0.990296
+  Val Accuracy: 0.946000
+  Learning Rate: 0.00005000
+  Epoch Time: 17.24s
+
+Epoch 21:
+  Train Loss: 0.031530
+  Train Accuracy: 0.990815
+  Val Accuracy: 0.949778
+  Learning Rate: 0.00005000
+  Epoch Time: 17.25s
+
+Epoch 22:
+  Train Loss: 0.032882
+  Train Accuracy: 0.990000
+  Val Accuracy: 0.946000
+  Learning Rate: 0.00005000
+  Epoch Time: 17.20s
+
+Epoch 23:
+  Train Loss: 0.024585
+  Train Accuracy: 0.992370
+  Val Accuracy: 0.946000
+  Learning Rate: 0.00005000
+  Epoch Time: 17.27s
+
+Epoch 24:
+  Train Loss: 0.027643
+  Train Accuracy: 0.991556
+  Val Accuracy: 0.946667
+  Learning Rate: 0.00005000
+  Epoch Time: 17.26s
+
+Epoch 25:
+  Train Loss: 0.025976
+  Train Accuracy: 0.991926
+  Val Accuracy: 0.947778
+  Learning Rate: 0.00005000
+  Epoch Time: 17.28s
+
+Epoch 26:
+  Train Loss: 0.025604
+  Train Accuracy: 0.992222
+  Val Accuracy: 0.947333
+  Learning Rate: 0.00005000
+  Epoch Time: 17.25s
+
+Epoch 27:
+  Train Loss: 0.025203
+  Train Accuracy: 0.992593
+  Val Accuracy: 0.947111
+  Learning Rate: 0.00005000
+  Epoch Time: 17.43s
+
+Epoch 28:
+  Train Loss: 0.023580
+  Train Accuracy: 0.992741
+  Val Accuracy: 0.950667
+  Learning Rate: 0.00002500
+  Epoch Time: 17.35s
+
+Epoch 29:
+  Train Loss: 0.019039
+  Train Accuracy: 0.994000
+  Val Accuracy: 0.948000
+  Learning Rate: 0.00002500
+  Epoch Time: 17.31s
+
+Epoch 30:
+  Train Loss: 0.019318
+  Train Accuracy: 0.993926
+  Val Accuracy: 0.948222
+  Learning Rate: 0.00002500
+  Epoch Time: 17.44s
+
+Epoch 31:
+  Train Loss: 0.019344
+  Train Accuracy: 0.994074
+  Val Accuracy: 0.949778
+  Learning Rate: 0.00002500
+  Epoch Time: 17.29s
+
+Epoch 32:
+  Train Loss: 0.023005
+  Train Accuracy: 0.992444
+  Val Accuracy: 0.948222
+  Learning Rate: 0.00002500
+  Epoch Time: 17.39s
+
+Epoch 33:
+  Train Loss: 0.018618
+  Train Accuracy: 0.994222
+  Val Accuracy: 0.948667
+  Learning Rate: 0.00002500
+  Epoch Time: 17.16s
+
+Epoch 34:
+  Train Loss: 0.019426
+  Train Accuracy: 0.993778
+  Val Accuracy: 0.949778
+  Learning Rate: 0.00002500
+  Epoch Time: 17.40s
+
+Epoch 35:
+  Train Loss: 0.017998
+  Train Accuracy: 0.994667
+  Val Accuracy: 0.949556
+  Learning Rate: 0.00001250
+  Epoch Time: 17.29s
+
+Epoch 36:
+  Train Loss: 0.017031
+  Train Accuracy: 0.995037
+  Val Accuracy: 0.950000
+  Learning Rate: 0.00001250
+  Epoch Time: 17.15s
+
+Epoch 37:
+  Train Loss: 0.017270
+  Train Accuracy: 0.994667
+  Val Accuracy: 0.950444
+  Learning Rate: 0.00001250
+  Epoch Time: 17.32s
+
+Epoch 38:
+  Train Loss: 0.017385
+  Train Accuracy: 0.994148
+  Val Accuracy: 0.950444
+  Learning Rate: 0.00001250
+  Epoch Time: 17.23s
+