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finetuned-arsenic

Image Classification
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vit
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  1. README.md +26 -27
  2. model.safetensors +1 -1
  3. runs/Oct07_17-33-22_a656dd3e0de6/events.out.tfevents.1728322409.a656dd3e0de6.478.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -3,7 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  datasets:
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  - imagefolder
@@ -16,7 +15,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: arsenic_images
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  type: imagefolder
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  config: default
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  split: train
@@ -24,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9993451211525868
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # finetuned-arsenic
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- 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 arsenic_images dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0033
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- - Accuracy: 0.9993
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  ## Model description
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@@ -67,27 +66,27 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | 0.2254 | 0.1848 | 100 | 0.1266 | 0.9633 |
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- | 0.1941 | 0.3697 | 200 | 0.0928 | 0.9699 |
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- | 0.2312 | 0.5545 | 300 | 0.1787 | 0.9345 |
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- | 0.1052 | 0.7394 | 400 | 0.1246 | 0.9640 |
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- | 0.1738 | 0.9242 | 500 | 0.0384 | 0.9902 |
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- | 0.115 | 1.1091 | 600 | 0.1406 | 0.9535 |
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- | 0.0549 | 1.2939 | 700 | 0.0358 | 0.9882 |
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- | 0.0313 | 1.4787 | 800 | 0.0465 | 0.9862 |
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- | 0.0476 | 1.6636 | 900 | 0.0200 | 0.9941 |
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- | 0.0331 | 1.8484 | 1000 | 0.0423 | 0.9862 |
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- | 0.101 | 2.0333 | 1100 | 0.0146 | 0.9954 |
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- | 0.0736 | 2.2181 | 1200 | 0.0189 | 0.9954 |
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- | 0.0274 | 2.4030 | 1300 | 0.0381 | 0.9856 |
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- | 0.0106 | 2.5878 | 1400 | 0.0120 | 0.9980 |
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- | 0.0014 | 2.7726 | 1500 | 0.0263 | 0.9941 |
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- | 0.0056 | 2.9575 | 1600 | 0.0033 | 0.9993 |
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- | 0.0221 | 3.1423 | 1700 | 0.0100 | 0.9954 |
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- | 0.0011 | 3.3272 | 1800 | 0.0073 | 0.9980 |
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- | 0.0013 | 3.5120 | 1900 | 0.0107 | 0.9974 |
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- | 0.0052 | 3.6969 | 2000 | 0.0124 | 0.9967 |
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- | 0.0104 | 3.8817 | 2100 | 0.0055 | 0.9987 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
 
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
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  name: Image Classification
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  type: image-classification
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  dataset:
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+ name: imagefolder
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  type: imagefolder
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  config: default
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  split: train
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9986902423051736
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # finetuned-arsenic
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+ 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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0053
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+ - Accuracy: 0.9987
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.2214 | 0.1848 | 100 | 0.1243 | 0.9607 |
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+ | 0.1213 | 0.3697 | 200 | 0.1763 | 0.9339 |
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+ | 0.1201 | 0.5545 | 300 | 0.1018 | 0.9607 |
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+ | 0.0991 | 0.7394 | 400 | 0.2071 | 0.9417 |
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+ | 0.1127 | 0.9242 | 500 | 0.0886 | 0.9666 |
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+ | 0.0314 | 1.1091 | 600 | 0.0333 | 0.9908 |
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+ | 0.0252 | 1.2939 | 700 | 0.0110 | 0.9974 |
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+ | 0.0582 | 1.4787 | 800 | 0.0104 | 0.9987 |
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+ | 0.0455 | 1.6636 | 900 | 0.0198 | 0.9954 |
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+ | 0.0569 | 1.8484 | 1000 | 0.0180 | 0.9961 |
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+ | 0.0627 | 2.0333 | 1100 | 0.0244 | 0.9948 |
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+ | 0.0328 | 2.2181 | 1200 | 0.0054 | 0.9987 |
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+ | 0.0156 | 2.4030 | 1300 | 0.0193 | 0.9948 |
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+ | 0.0016 | 2.5878 | 1400 | 0.0074 | 0.9974 |
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+ | 0.0032 | 2.7726 | 1500 | 0.0045 | 0.9980 |
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+ | 0.0233 | 2.9575 | 1600 | 0.0029 | 0.9993 |
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+ | 0.0434 | 3.1423 | 1700 | 0.0026 | 0.9993 |
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+ | 0.0079 | 3.3272 | 1800 | 0.0095 | 0.9980 |
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+ | 0.0175 | 3.5120 | 1900 | 0.0111 | 0.9974 |
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+ | 0.0013 | 3.6969 | 2000 | 0.0109 | 0.9974 |
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+ | 0.0008 | 3.8817 | 2100 | 0.0053 | 0.9987 |
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  ### Framework versions
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