End of training

README.md

@@ -0,0 +1,58 @@
+---
+license: apache-2.0
+base_model: microsoft/resnet-152
+tags:
+- generated_from_trainer
+model-index:
+- name: SkinCancerClassifier_Plain-V0
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jhoppanne-myself/finalProject/runs/yef3opr5)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jhoppanne-myself/finalProject/runs/yef3opr5)
+# SkinCancerClassifier_Plain-V0
+
+This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the None dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 3.2075
+- eval_accuracy: 0.6083
+- eval_runtime: 1.756
+- eval_samples_per_second: 136.671
+- eval_steps_per_second: 4.556
+- epoch: 250.6
+- step: 7518
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 500
+
+### Framework versions
+
+- Transformers 4.42.2
+- Pytorch 2.3.0
+- Datasets 2.15.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "microsoft/resnet-152",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    8,
+    36,
+    3
+  ],
+  "downsample_in_bottleneck": false,
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
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