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crasyangel
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my_awesome_food_model

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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crasyangel commited on
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End of training

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  1. README.md +11 -11
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -4,7 +4,7 @@ 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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- - food101
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  metrics:
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  - accuracy
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  model-index:
@@ -14,11 +14,11 @@ 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: food101
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- type: food101
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- config: default
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- split: train[:5000]
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- args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
@@ -30,9 +30,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # my_awesome_food_model
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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 food101 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9566
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  - Accuracy: 0.921
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  ## Model description
@@ -67,9 +67,9 @@ 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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- | 1.3043 | 0.99 | 62 | 1.2840 | 0.91 |
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- | 1.0293 | 2.0 | 125 | 1.0594 | 0.902 |
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- | 0.9053 | 2.98 | 186 | 0.9566 | 0.921 |
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  datasets:
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+ - cifar10
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  metrics:
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  - accuracy
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  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: cifar10
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+ type: cifar10
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+ config: plain_text
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+ split: train[:50000]
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+ args: plain_text
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  metrics:
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  - name: Accuracy
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  type: accuracy
 
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  # my_awesome_food_model
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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 cifar10 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2768
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  - Accuracy: 0.921
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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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+ | 0.4414 | 1.0 | 625 | 0.4034 | 0.9011 |
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+ | 0.2976 | 2.0 | 1250 | 0.3157 | 0.9102 |
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+ | 0.2345 | 3.0 | 1875 | 0.2768 | 0.921 |
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  ### Framework versions
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