End of training

README.md

@@ -1,13 +1,94 @@
 ---
-license: mit
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
 datasets:
-- Ghiffari4869/pipefail
-language:
-- en
+- imagefolder
 metrics:
 - accuracy
-library_name: transformers
-pipeline_tag: image-classification
-tags:
-- not-for-all-audiences
----
+model-index:
+- name: pipe-failure_classification
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 1.0
+---
+
+<!-- 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. -->
+
+# pipe-failure_classification
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0255
+- Accuracy: 1.0
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 15   | 0.0516          | 0.9867   |
+| No log        | 2.0   | 30   | 0.0441          | 0.9867   |
+| No log        | 3.0   | 45   | 0.0497          | 0.9733   |
+| No log        | 4.0   | 60   | 0.0464          | 0.9867   |
+| No log        | 5.0   | 75   | 0.0677          | 0.9867   |
+| No log        | 6.0   | 90   | 0.0208          | 1.0      |
+| No log        | 7.0   | 105  | 0.0183          | 1.0      |
+| No log        | 8.0   | 120  | 0.0943          | 0.9733   |
+| No log        | 9.0   | 135  | 0.0216          | 1.0      |
+| No log        | 10.0  | 150  | 0.0148          | 1.0      |
+| No log        | 11.0  | 165  | 0.0144          | 1.0      |
+| No log        | 12.0  | 180  | 0.0188          | 1.0      |
+| No log        | 13.0  | 195  | 0.0602          | 0.9867   |
+| No log        | 14.0  | 210  | 0.0882          | 0.9733   |
+| No log        | 15.0  | 225  | 0.0314          | 0.9867   |
+| No log        | 16.0  | 240  | 0.0127          | 1.0      |
+| No log        | 17.0  | 255  | 0.0119          | 1.0      |
+| No log        | 18.0  | 270  | 0.0117          | 1.0      |
+| No log        | 19.0  | 285  | 0.0114          | 1.0      |
+| No log        | 20.0  | 300  | 0.0131          | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

@@ -10,14 +10,16 @@
   "hidden_size": 768,
   "id2label": {
     "0": "burst",
-    "1": "welding flaws"
+    "1": "rust",
+    "2": "weld defect"
   },
   "image_size": 224,
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
     "burst": "0",
-    "welding flaws": "1"
+    "rust": "1",
+    "weld defect": "2"
   },
   "layer_norm_eps": 1e-12,
   "model_type": "vit",

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