update model card README.md

README.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 tags:
 - generated_from_trainer
 datasets:
@@ -21,7 +20,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.903
+      value: 0.933
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,118 +28,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # VIT-food101-image-classifier
 
-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.
+This model was trained from scratch on the food101 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6312
-- Accuracy: 0.903
+- Loss: 0.5661
+- Accuracy: 0.933
 
 ## Model description
 
-Model trained on the food classification dataset trained on labels: 
-
-0 apple_pie
-1 baby_back_ribs
-2 baklava
-3 beef_carpaccio
-4 beef_tartare
-5 beet_salad
-6 beignets
-7 bibimbap
-8 bread_pudding
-9 breakfast_burrito
-10 bruschetta
-11 caesar_salad
-12 cannoli
-13 caprese_salad
-14 carrot_cake
-15 ceviche
-16 cheesecake
-17 cheese_plate
-18 chicken_curry
-19 chicken_quesadilla
-20 chicken_wings
-21 chocolate_cake
-22 chocolate_mousse
-23 churros
-24 clam_chowder
-25 club_sandwich
-26 crab_cakes
-27 creme_brulee
-28 croque_madame
-29 cup_cakes
-30 deviled_eggs
-31 donuts
-32 dumplings
-33 edamame
-34 eggs_benedict
-35 escargots
-36 falafel
-37 filet_mignon
-38 fish_and_chips
-39 foie_gras
-40 french_fries
-41 french_onion_soup
-42 french_toast
-43 fried_calamari
-44 fried_rice
-45 frozen_yogurt
-46 garlic_bread
-47 gnocchi
-48 greek_salad
-49 grilled_cheese_sandwich
-50 grilled_salmon
-51 guacamole
-52 gyoza
-53 hamburger
-54 hot_and_sour_soup
-55 hot_dog
-56 huevos_rancheros
-57 hummus
-58 ice_cream
-59 lasagna
-60 lobster_bisque
-61 lobster_roll_sandwich
-62 macaroni_and_cheese
-63 macarons
-64 miso_soup
-65 mussels
-66 nachos
-67 omelette
-68 onion_rings
-69 oysters
-70 pad_thai
-71 paella
-72 pancakes
-73 panna_cotta
-74 peking_duck
-75 pho
-76 pizza
-77 pork_chop
-78 poutine
-79 prime_rib
-80 pulled_pork_sandwich
-81 ramen
-82 ravioli
-83 red_velvet_cake
-84 risotto
-85 samosa
-86 sashimi
-87 scallops
-88 seaweed_salad
-89 shrimp_and_grits
-90 spaghetti_bolognese
-91 spaghetti_carbonara
-92 spring_rolls
-93 steak
-94 strawberry_shortcake
-95 sushi
-96 tacos
-97 takoyaki
-98 tiramisu
-99 tuna_tartare
-100 waffles
-
-The model is then used to detect new images and produce a classification based on what it thinks is in the model. 
+More information needed
 
 ## Intended uses & limitations
 
@@ -164,15 +59,17 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.6787        | 0.99  | 62   | 2.5274          | 0.847    |
-| 1.8204        | 1.99  | 124  | 1.8050          | 0.883    |
-| 1.5946        | 2.99  | 186  | 1.6312          | 0.903    |
+| 1.1716        | 0.99  | 62   | 1.2149          | 0.896    |
+| 0.7758        | 1.99  | 124  | 0.8727          | 0.906    |
+| 0.6269        | 2.99  | 186  | 0.6833          | 0.928    |
+| 0.5495        | 3.99  | 248  | 0.6041          | 0.931    |
+| 0.4973        | 4.99  | 310  | 0.5661          | 0.933    |
 
 
 ### Framework versions