Model save

README.md

@@ -3,28 +3,12 @@ library_name: transformers
 license: apache-2.0
 base_model: google/vit-base-patch16-224-in21k
 tags:
-- image-classification
 - generated_from_trainer
-datasets:
-- imagefolder
 metrics:
 - accuracy
 model-index:
 - name: finetuned-fake-food
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: indian_food_images
-      type: imagefolder
-      config: default
-      split: train
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.8828996282527881
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # finetuned-fake-food
 
-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 indian_food_images dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3411
-- Accuracy: 0.8829
+- Loss: 0.4941
+- Accuracy: 0.8387
 
 ## Model description
 
@@ -54,29 +38,34 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 3e-05
 - train_batch_size: 4
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
-- num_epochs: 10
+- num_epochs: 15
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.5083        | 1.0   | 761  | 0.4226          | 0.8234   |
-| 0.5171        | 2.0   | 1522 | 0.4868          | 0.7825   |
-| 0.5042        | 3.0   | 2283 | 0.4948          | 0.7862   |
-| 0.282         | 4.0   | 3044 | 0.4702          | 0.8067   |
-| 0.8396        | 5.0   | 3805 | 0.4037          | 0.8290   |
-| 0.2213        | 6.0   | 4566 | 0.5371          | 0.7918   |
-| 0.636         | 7.0   | 5327 | 0.4830          | 0.8234   |
-| 0.5217        | 8.0   | 6088 | 0.3411          | 0.8829   |
-| 0.3796        | 9.0   | 6849 | 0.3674          | 0.8680   |
-| 0.286         | 10.0  | 7610 | 0.3989          | 0.8494   |
+| 0.6061        | 1.0   | 176  | 0.5937          | 0.6855   |
+| 0.481         | 2.0   | 352  | 0.5138          | 0.8226   |
+| 0.5522        | 3.0   | 528  | 0.4973          | 0.8065   |
+| 0.4092        | 4.0   | 704  | 0.5557          | 0.7903   |
+| 0.4882        | 5.0   | 880  | 0.4998          | 0.7984   |
+| 0.4442        | 6.0   | 1056 | 0.4647          | 0.8387   |
+| 0.5749        | 7.0   | 1232 | 0.4464          | 0.8306   |
+| 0.4529        | 8.0   | 1408 | 0.5366          | 0.8065   |
+| 0.5287        | 9.0   | 1584 | 0.4633          | 0.8387   |
+| 0.3821        | 10.0  | 1760 | 0.4983          | 0.8387   |
+| 0.2409        | 11.0  | 1936 | 0.4855          | 0.8548   |
+| 0.2025        | 12.0  | 2112 | 0.5102          | 0.8387   |
+| 0.2045        | 13.0  | 2288 | 0.4942          | 0.8387   |
+| 0.4097        | 14.0  | 2464 | 0.4954          | 0.8387   |
+| 0.5798        | 15.0  | 2640 | 0.4941          | 0.8387   |
 
 
 ### Framework versions

config.json

@@ -8,9 +8,17 @@
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.0,
   "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1
+  },
   "image_size": 224,
   "initializer_range": 0.02,
   "intermediate_size": 3072,
+  "label2id": {
+    "0": "0",
+    "1": "1"
+  },
   "layer_norm_eps": 1e-12,
   "model_type": "vit",
   "num_attention_heads": 12,

model.safetensors

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runs/Oct02_17-11-29_593ca4d065ee/events.out.tfevents.1727889099.593ca4d065ee.197.0

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training_args.bin

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