updates model trained 5 epochs, 0.96 acc

model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b6925dcdb6fa5675ca0959c0906a1080e12595b60ddf317430a019d90ccb7b2f
+oid sha256:dbfd1293e47384cc215f3cbf0a611548589b91d5fd5e8838a03fdd485bd7151b
 size 33762565

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0d73c2d5a16bb5db09d4ca7e663c7b95f64fd39b1f9abc1dfa479b49224c598f
+oid sha256:2321555c8dae044f37b02055a11673543a7742910c760396e3813b4eba13e4cf
 size 34099540

train.py

@@ -5,7 +5,7 @@ from transformers import EfficientNetImageProcessor, EfficientNetForImageClassif
 import numpy as np
 
 print("Cuda availability:", torch.cuda.is_available())
-cuda = torch.device('cuda')     # Default HIP device
+cuda = torch.device('cuda')
 print("cuda: ", torch.cuda.get_device_name(device=cuda))
 
 dataset = load_dataset("chriamue/bird-species-dataset")
@@ -30,9 +30,9 @@ training_args = TrainingArguments(
     evaluation_strategy="epoch",
     save_strategy="epoch",
     learning_rate=5e-5,
-    per_device_train_batch_size=32,
+    per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
-    num_train_epochs=1,
+    num_train_epochs=6,
     weight_decay=0.01,
     load_best_model_at_end=True,
     metric_for_best_model="accuracy"
@@ -53,10 +53,9 @@ def transforms(examples):
     examples["pixel_values"] = pixel_values
     return examples
 
-
 image = dataset["train"][0]["image"]
 
-dataset["train"] = dataset["train"].shuffle(seed=42).select(range(1500))
+# dataset["train"] = dataset["train"].shuffle(seed=42).select(range(1500))
 # dataset["validation"] = dataset["validation"].select(range(100))
 # dataset["test"] = dataset["test"].select(range(100))
 
@@ -70,7 +69,7 @@ trainer = Trainer(
     compute_metrics=compute_metrics,
 )
 
-train_results = trainer.train(resume_from_checkpoint=True)
+train_results = trainer.train(resume_from_checkpoint=False)
 
 print(trainer.evaluate())
 

training_args.bin

@@ -1,3 +1,3 @@
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+oid sha256:817083c686e9f03595b3ef0af83f3f8fdd1a6b49d286980b33af2e23a0e330d8
 size 4600