End of training

README.md

@@ -0,0 +1,191 @@
+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- stanford-dogs
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: google-vit-base-patch16-224-batch32-lr0.005-standford-dogs
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: stanford-dogs
+      type: stanford-dogs
+      config: default
+      split: full
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8797376093294461
+    - name: F1
+      type: f1
+      value: 0.8759381135610711
+    - name: Precision
+      type: precision
+      value: 0.88124155438923
+    - name: Recall
+      type: recall
+      value: 0.876557313613651
+---
+
+<!-- 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. -->
+
+# google-vit-base-patch16-224-batch32-lr0.005-standford-dogs
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4511
+- Accuracy: 0.8797
+- F1: 0.8759
+- Precision: 0.8812
+- Recall: 0.8766
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 4.8453        | 0.0777 | 10   | 4.6341          | 0.0355   | 0.0304 | 0.0311    | 0.0364 |
+| 4.5433        | 0.1553 | 20   | 4.3107          | 0.1246   | 0.0982 | 0.1263    | 0.1225 |
+| 4.2752        | 0.2330 | 30   | 3.9697          | 0.2719   | 0.2176 | 0.2518    | 0.2632 |
+| 3.9872        | 0.3107 | 40   | 3.6402          | 0.4274   | 0.3661 | 0.4264    | 0.4167 |
+| 3.7182        | 0.3883 | 50   | 3.3251          | 0.5362   | 0.4888 | 0.5817    | 0.5247 |
+| 3.473         | 0.4660 | 60   | 3.0453          | 0.6220   | 0.5815 | 0.6516    | 0.6115 |
+| 3.2252        | 0.5437 | 70   | 2.7739          | 0.6817   | 0.6506 | 0.7194    | 0.6713 |
+| 2.9976        | 0.6214 | 80   | 2.5391          | 0.7046   | 0.6756 | 0.7286    | 0.6954 |
+| 2.762         | 0.6990 | 90   | 2.2990          | 0.7505   | 0.7258 | 0.7646    | 0.7421 |
+| 2.5763        | 0.7767 | 100  | 2.1075          | 0.7646   | 0.7434 | 0.7793    | 0.7556 |
+| 2.4357        | 0.8544 | 110  | 1.9226          | 0.7850   | 0.7652 | 0.8027    | 0.7768 |
+| 2.2669        | 0.9320 | 120  | 1.7673          | 0.8008   | 0.7838 | 0.8149    | 0.7938 |
+| 2.1459        | 1.0097 | 130  | 1.6339          | 0.8175   | 0.8058 | 0.8291    | 0.8110 |
+| 1.9822        | 1.0874 | 140  | 1.5204          | 0.8214   | 0.8114 | 0.8366    | 0.8151 |
+| 1.8701        | 1.1650 | 150  | 1.4219          | 0.8173   | 0.8091 | 0.8330    | 0.8117 |
+| 1.8007        | 1.2427 | 160  | 1.3224          | 0.8292   | 0.8205 | 0.8390    | 0.8233 |
+| 1.8004        | 1.3204 | 170  | 1.2553          | 0.8324   | 0.8243 | 0.8413    | 0.8271 |
+| 1.6511        | 1.3981 | 180  | 1.1728          | 0.8372   | 0.8282 | 0.8467    | 0.8314 |
+| 1.548         | 1.4757 | 190  | 1.1091          | 0.8394   | 0.8300 | 0.8500    | 0.8340 |
+| 1.5634        | 1.5534 | 200  | 1.0561          | 0.8345   | 0.8263 | 0.8444    | 0.8287 |
+| 1.5163        | 1.6311 | 210  | 0.9983          | 0.8457   | 0.8382 | 0.8512    | 0.8409 |
+| 1.3883        | 1.7087 | 220  | 0.9574          | 0.8499   | 0.8425 | 0.8545    | 0.8452 |
+| 1.3161        | 1.7864 | 230  | 0.9129          | 0.8511   | 0.8425 | 0.8564    | 0.8457 |
+| 1.304         | 1.8641 | 240  | 0.8727          | 0.8535   | 0.8454 | 0.8570    | 0.8487 |
+| 1.3268        | 1.9417 | 250  | 0.8412          | 0.8511   | 0.8441 | 0.8572    | 0.8473 |
+| 1.2388        | 2.0194 | 260  | 0.8104          | 0.8569   | 0.8482 | 0.8608    | 0.8522 |
+| 1.1333        | 2.0971 | 270  | 0.7920          | 0.8557   | 0.8486 | 0.8596    | 0.8516 |
+| 1.1305        | 2.1748 | 280  | 0.7565          | 0.8579   | 0.8505 | 0.8630    | 0.8534 |
+| 1.1849        | 2.2524 | 290  | 0.7498          | 0.8593   | 0.8536 | 0.8646    | 0.8549 |
+| 1.1287        | 2.3301 | 300  | 0.7348          | 0.8593   | 0.8533 | 0.8653    | 0.8552 |
+| 1.0537        | 2.4078 | 310  | 0.7120          | 0.8554   | 0.8496 | 0.8586    | 0.8515 |
+| 1.1157        | 2.4854 | 320  | 0.6832          | 0.8622   | 0.8552 | 0.8662    | 0.8579 |
+| 1.1008        | 2.5631 | 330  | 0.6705          | 0.8618   | 0.8546 | 0.8640    | 0.8574 |
+| 1.0512        | 2.6408 | 340  | 0.6557          | 0.8630   | 0.8563 | 0.8636    | 0.8593 |
+| 1.0641        | 2.7184 | 350  | 0.6490          | 0.8632   | 0.8581 | 0.8691    | 0.8596 |
+| 1.0446        | 2.7961 | 360  | 0.6301          | 0.8652   | 0.8597 | 0.8692    | 0.8612 |
+| 1.0104        | 2.8738 | 370  | 0.6287          | 0.8632   | 0.8562 | 0.8668    | 0.8588 |
+| 1.0544        | 2.9515 | 380  | 0.6150          | 0.8644   | 0.8579 | 0.8657    | 0.8602 |
+| 1.0074        | 3.0291 | 390  | 0.6061          | 0.8683   | 0.8617 | 0.8712    | 0.8641 |
+| 0.9329        | 3.1068 | 400  | 0.6001          | 0.8661   | 0.8591 | 0.8750    | 0.8620 |
+| 0.9049        | 3.1845 | 410  | 0.5925          | 0.8686   | 0.8617 | 0.8731    | 0.8647 |
+| 0.9815        | 3.2621 | 420  | 0.5806          | 0.8686   | 0.8622 | 0.8717    | 0.8644 |
+| 0.9507        | 3.3398 | 430  | 0.5793          | 0.8673   | 0.8613 | 0.8691    | 0.8638 |
+| 0.9608        | 3.4175 | 440  | 0.5721          | 0.8671   | 0.8614 | 0.8683    | 0.8636 |
+| 0.9409        | 3.4951 | 450  | 0.5688          | 0.8652   | 0.8591 | 0.8658    | 0.8612 |
+| 0.8856        | 3.5728 | 460  | 0.5563          | 0.8700   | 0.8650 | 0.8714    | 0.8667 |
+| 0.9099        | 3.6505 | 470  | 0.5557          | 0.8661   | 0.8613 | 0.8681    | 0.8622 |
+| 0.9167        | 3.7282 | 480  | 0.5527          | 0.8686   | 0.8639 | 0.8701    | 0.8648 |
+| 0.9077        | 3.8058 | 490  | 0.5431          | 0.8705   | 0.8669 | 0.8722    | 0.8674 |
+| 0.9005        | 3.8835 | 500  | 0.5390          | 0.8732   | 0.8697 | 0.8749    | 0.8701 |
+| 0.8596        | 3.9612 | 510  | 0.5375          | 0.8707   | 0.8655 | 0.8732    | 0.8668 |
+| 0.8856        | 4.0388 | 520  | 0.5254          | 0.8705   | 0.8651 | 0.8741    | 0.8663 |
+| 0.8869        | 4.1165 | 530  | 0.5238          | 0.8717   | 0.8657 | 0.8731    | 0.8680 |
+| 0.8069        | 4.1942 | 540  | 0.5188          | 0.8732   | 0.8671 | 0.8744    | 0.8695 |
+| 0.8474        | 4.2718 | 550  | 0.5188          | 0.8710   | 0.8649 | 0.8729    | 0.8671 |
+| 0.8243        | 4.3495 | 560  | 0.5177          | 0.8727   | 0.8684 | 0.8756    | 0.8696 |
+| 0.8437        | 4.4272 | 570  | 0.5107          | 0.8727   | 0.8682 | 0.8742    | 0.8693 |
+| 0.7761        | 4.5049 | 580  | 0.5025          | 0.8739   | 0.8700 | 0.8751    | 0.8708 |
+| 0.784         | 4.5825 | 590  | 0.5016          | 0.8768   | 0.8717 | 0.8778    | 0.8734 |
+| 0.8055        | 4.6602 | 600  | 0.5019          | 0.8739   | 0.8701 | 0.8772    | 0.8710 |
+| 0.8109        | 4.7379 | 610  | 0.4960          | 0.8771   | 0.8724 | 0.8785    | 0.8740 |
+| 0.8697        | 4.8155 | 620  | 0.4887          | 0.8793   | 0.8749 | 0.8816    | 0.8757 |
+| 0.7996        | 4.8932 | 630  | 0.4878          | 0.8773   | 0.8719 | 0.8782    | 0.8734 |
+| 0.8002        | 4.9709 | 640  | 0.4847          | 0.8785   | 0.8738 | 0.8807    | 0.8752 |
+| 0.7404        | 5.0485 | 650  | 0.4888          | 0.8771   | 0.8726 | 0.8795    | 0.8739 |
+| 0.7326        | 5.1262 | 660  | 0.4883          | 0.8746   | 0.8701 | 0.8772    | 0.8718 |
+| 0.797         | 5.2039 | 670  | 0.4892          | 0.8729   | 0.8689 | 0.8752    | 0.8701 |
+| 0.8084        | 5.2816 | 680  | 0.4800          | 0.8793   | 0.8752 | 0.8817    | 0.8763 |
+| 0.8025        | 5.3592 | 690  | 0.4762          | 0.8768   | 0.8727 | 0.8771    | 0.8736 |
+| 0.7087        | 5.4369 | 700  | 0.4762          | 0.8783   | 0.8750 | 0.8807    | 0.8756 |
+| 0.7502        | 5.5146 | 710  | 0.4754          | 0.8785   | 0.8754 | 0.8801    | 0.8759 |
+| 0.7386        | 5.5922 | 720  | 0.4738          | 0.8793   | 0.8754 | 0.8807    | 0.8760 |
+| 0.8173        | 5.6699 | 730  | 0.4712          | 0.8793   | 0.8750 | 0.8801    | 0.8762 |
+| 0.8213        | 5.7476 | 740  | 0.4696          | 0.8790   | 0.8750 | 0.8795    | 0.8756 |
+| 0.7184        | 5.8252 | 750  | 0.4714          | 0.8805   | 0.8759 | 0.8826    | 0.8768 |
+| 0.7168        | 5.9029 | 760  | 0.4682          | 0.8749   | 0.8695 | 0.8771    | 0.8715 |
+| 0.7558        | 5.9806 | 770  | 0.4673          | 0.8761   | 0.8711 | 0.8787    | 0.8729 |
+| 0.7169        | 6.0583 | 780  | 0.4678          | 0.8783   | 0.8736 | 0.8801    | 0.8749 |
+| 0.7042        | 6.1359 | 790  | 0.4628          | 0.8759   | 0.8710 | 0.8773    | 0.8724 |
+| 0.7332        | 6.2136 | 800  | 0.4672          | 0.8766   | 0.8720 | 0.8790    | 0.8731 |
+| 0.7027        | 6.2913 | 810  | 0.4644          | 0.8785   | 0.8736 | 0.8805    | 0.8749 |
+| 0.7283        | 6.3689 | 820  | 0.4642          | 0.8776   | 0.8724 | 0.8793    | 0.8740 |
+| 0.7305        | 6.4466 | 830  | 0.4613          | 0.8780   | 0.8729 | 0.8785    | 0.8742 |
+| 0.7186        | 6.5243 | 840  | 0.4606          | 0.8768   | 0.8723 | 0.8783    | 0.8734 |
+| 0.759         | 6.6019 | 850  | 0.4592          | 0.8766   | 0.8719 | 0.8769    | 0.8730 |
+| 0.6865        | 6.6796 | 860  | 0.4580          | 0.8771   | 0.8727 | 0.8782    | 0.8737 |
+| 0.689         | 6.7573 | 870  | 0.4574          | 0.8776   | 0.8735 | 0.8788    | 0.8745 |
+| 0.6851        | 6.8350 | 880  | 0.4561          | 0.8802   | 0.8764 | 0.8815    | 0.8773 |
+| 0.7158        | 6.9126 | 890  | 0.4547          | 0.8795   | 0.8759 | 0.8808    | 0.8766 |
+| 0.6938        | 6.9903 | 900  | 0.4533          | 0.8800   | 0.8759 | 0.8810    | 0.8768 |
+| 0.6596        | 7.0680 | 910  | 0.4540          | 0.8800   | 0.8759 | 0.8808    | 0.8768 |
+| 0.7519        | 7.1456 | 920  | 0.4530          | 0.8800   | 0.8758 | 0.8809    | 0.8769 |
+| 0.6836        | 7.2233 | 930  | 0.4519          | 0.8793   | 0.8753 | 0.8806    | 0.8762 |
+| 0.7407        | 7.3010 | 940  | 0.4520          | 0.8788   | 0.8751 | 0.8807    | 0.8757 |
+| 0.6823        | 7.3786 | 950  | 0.4522          | 0.8785   | 0.8750 | 0.8802    | 0.8753 |
+| 0.7029        | 7.4563 | 960  | 0.4524          | 0.8785   | 0.8746 | 0.8802    | 0.8753 |
+| 0.6536        | 7.5340 | 970  | 0.4515          | 0.8795   | 0.8756 | 0.8812    | 0.8763 |
+| 0.6837        | 7.6117 | 980  | 0.4513          | 0.8800   | 0.8761 | 0.8815    | 0.8768 |
+| 0.6604        | 7.6893 | 990  | 0.4512          | 0.8797   | 0.8759 | 0.8812    | 0.8766 |
+| 0.683         | 7.7670 | 1000 | 0.4511          | 0.8797   | 0.8759 | 0.8812    | 0.8766 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,268 @@
+{
+  "_name_or_path": "google/vit-base-patch16-224",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Affenpinscher",
+    "1": "Afghan Hound",
+    "2": "African Hunting Dog",
+    "3": "Airedale",
+    "4": "American Staffordshire Terrier",
+    "5": "Appenzeller",
+    "6": "Australian Terrier",
+    "7": "Basenji",
+    "8": "Basset",
+    "9": "Beagle",
+    "10": "Bedlington Terrier",
+    "11": "Bernese Mountain Dog",
+    "12": "Black And Tan Coonhound",
+    "13": "Blenheim Spaniel",
+    "14": "Bloodhound",
+    "15": "Bluetick",
+    "16": "Border Collie",
+    "17": "Border Terrier",
+    "18": "Borzoi",
+    "19": "Boston Bull",
+    "20": "Bouvier Des Flandres",
+    "21": "Boxer",
+    "22": "Brabancon Griffon",
+    "23": "Briard",
+    "24": "Brittany Spaniel",
+    "25": "Bull Mastiff",
+    "26": "Cairn",
+    "27": "Cardigan",
+    "28": "Chesapeake Bay Retriever",
+    "29": "Chihuahua",
+    "30": "Chow",
+    "31": "Clumber",
+    "32": "Cocker Spaniel",
+    "33": "Collie",
+    "34": "Curly Coated Retriever",
+    "35": "Dandie Dinmont",
+    "36": "Dhole",
+    "37": "Dingo",
+    "38": "Doberman",
+    "39": "English Foxhound",
+    "40": "English Setter",
+    "41": "English Springer",
+    "42": "Entlebucher",
+    "43": "Eskimo Dog",
+    "44": "Flat Coated Retriever",
+    "45": "French Bulldog",
+    "46": "German Shepherd",
+    "47": "German Short Haired Pointer",
+    "48": "Giant Schnauzer",
+    "49": "Golden Retriever",
+    "50": "Gordon Setter",
+    "51": "Great Dane",
+    "52": "Great Pyrenees",
+    "53": "Greater Swiss Mountain Dog",
+    "54": "Groenendael",
+    "55": "Ibizan Hound",
+    "56": "Irish Setter",
+    "57": "Irish Terrier",
+    "58": "Irish Water Spaniel",
+    "59": "Irish Wolfhound",
+    "60": "Italian Greyhound",
+    "61": "Japanese Spaniel",
+    "62": "Keeshond",
+    "63": "Kelpie",
+    "64": "Kerry Blue Terrier",
+    "65": "Komondor",
+    "66": "Kuvasz",
+    "67": "Labrador Retriever",
+    "68": "Lakeland Terrier",
+    "69": "Leonberg",
+    "70": "Lhasa",
+    "71": "Malamute",
+    "72": "Malinois",
+    "73": "Maltese Dog",
+    "74": "Mexican Hairless",
+    "75": "Miniature Pinscher",
+    "76": "Miniature Poodle",
+    "77": "Miniature Schnauzer",
+    "78": "Newfoundland",
+    "79": "Norfolk Terrier",
+    "80": "Norwegian Elkhound",
+    "81": "Norwich Terrier",
+    "82": "Old English Sheepdog",
+    "83": "Otterhound",
+    "84": "Papillon",
+    "85": "Pekinese",
+    "86": "Pembroke",
+    "87": "Pomeranian",
+    "88": "Pug",
+    "89": "Redbone",
+    "90": "Rhodesian Ridgeback",
+    "91": "Rottweiler",
+    "92": "Saint Bernard",
+    "93": "Saluki",
+    "94": "Samoyed",
+    "95": "Schipperke",
+    "96": "Scotch Terrier",
+    "97": "Scottish Deerhound",
+    "98": "Sealyham Terrier",
+    "99": "Shetland Sheepdog",
+    "100": "Shih Tzu",
+    "101": "Siberian Husky",
+    "102": "Silky Terrier",
+    "103": "Soft Coated Wheaten Terrier",
+    "104": "Staffordshire Bullterrier",
+    "105": "Standard Poodle",
+    "106": "Standard Schnauzer",
+    "107": "Sussex Spaniel",
+    "108": "Tibetan Mastiff",
+    "109": "Tibetan Terrier",
+    "110": "Toy Poodle",
+    "111": "Toy Terrier",
+    "112": "Vizsla",
+    "113": "Walker Hound",
+    "114": "Weimaraner",
+    "115": "Welsh Springer Spaniel",
+    "116": "West Highland White Terrier",
+    "117": "Whippet",
+    "118": "Wire Haired Fox Terrier",
+    "119": "Yorkshire Terrier"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Affenpinscher": 0,
+    "Afghan Hound": 1,
+    "African Hunting Dog": 2,
+    "Airedale": 3,
+    "American Staffordshire Terrier": 4,
+    "Appenzeller": 5,
+    "Australian Terrier": 6,
+    "Basenji": 7,
+    "Basset": 8,
+    "Beagle": 9,
+    "Bedlington Terrier": 10,
+    "Bernese Mountain Dog": 11,
+    "Black And Tan Coonhound": 12,
+    "Blenheim Spaniel": 13,
+    "Bloodhound": 14,
+    "Bluetick": 15,
+    "Border Collie": 16,
+    "Border Terrier": 17,
+    "Borzoi": 18,
+    "Boston Bull": 19,
+    "Bouvier Des Flandres": 20,
+    "Boxer": 21,
+    "Brabancon Griffon": 22,
+    "Briard": 23,
+    "Brittany Spaniel": 24,
+    "Bull Mastiff": 25,
+    "Cairn": 26,
+    "Cardigan": 27,
+    "Chesapeake Bay Retriever": 28,
+    "Chihuahua": 29,
+    "Chow": 30,
+    "Clumber": 31,
+    "Cocker Spaniel": 32,
+    "Collie": 33,
+    "Curly Coated Retriever": 34,
+    "Dandie Dinmont": 35,
+    "Dhole": 36,
+    "Dingo": 37,
+    "Doberman": 38,
+    "English Foxhound": 39,
+    "English Setter": 40,
+    "English Springer": 41,
+    "Entlebucher": 42,
+    "Eskimo Dog": 43,
+    "Flat Coated Retriever": 44,
+    "French Bulldog": 45,
+    "German Shepherd": 46,
+    "German Short Haired Pointer": 47,
+    "Giant Schnauzer": 48,
+    "Golden Retriever": 49,
+    "Gordon Setter": 50,
+    "Great Dane": 51,
+    "Great Pyrenees": 52,
+    "Greater Swiss Mountain Dog": 53,
+    "Groenendael": 54,
+    "Ibizan Hound": 55,
+    "Irish Setter": 56,
+    "Irish Terrier": 57,
+    "Irish Water Spaniel": 58,
+    "Irish Wolfhound": 59,
+    "Italian Greyhound": 60,
+    "Japanese Spaniel": 61,
+    "Keeshond": 62,
+    "Kelpie": 63,
+    "Kerry Blue Terrier": 64,
+    "Komondor": 65,
+    "Kuvasz": 66,
+    "Labrador Retriever": 67,
+    "Lakeland Terrier": 68,
+    "Leonberg": 69,
+    "Lhasa": 70,
+    "Malamute": 71,
+    "Malinois": 72,
+    "Maltese Dog": 73,
+    "Mexican Hairless": 74,
+    "Miniature Pinscher": 75,
+    "Miniature Poodle": 76,
+    "Miniature Schnauzer": 77,
+    "Newfoundland": 78,
+    "Norfolk Terrier": 79,
+    "Norwegian Elkhound": 80,
+    "Norwich Terrier": 81,
+    "Old English Sheepdog": 82,
+    "Otterhound": 83,
+    "Papillon": 84,
+    "Pekinese": 85,
+    "Pembroke": 86,
+    "Pomeranian": 87,
+    "Pug": 88,
+    "Redbone": 89,
+    "Rhodesian Ridgeback": 90,
+    "Rottweiler": 91,
+    "Saint Bernard": 92,
+    "Saluki": 93,
+    "Samoyed": 94,
+    "Schipperke": 95,
+    "Scotch Terrier": 96,
+    "Scottish Deerhound": 97,
+    "Sealyham Terrier": 98,
+    "Shetland Sheepdog": 99,
+    "Shih Tzu": 100,
+    "Siberian Husky": 101,
+    "Silky Terrier": 102,
+    "Soft Coated Wheaten Terrier": 103,
+    "Staffordshire Bullterrier": 104,
+    "Standard Poodle": 105,
+    "Standard Schnauzer": 106,
+    "Sussex Spaniel": 107,
+    "Tibetan Mastiff": 108,
+    "Tibetan Terrier": 109,
+    "Toy Poodle": 110,
+    "Toy Terrier": 111,
+    "Vizsla": 112,
+    "Walker Hound": 113,
+    "Weimaraner": 114,
+    "Welsh Springer Spaniel": 115,
+    "West Highland White Terrier": 116,
+    "Whippet": 117,
+    "Wire Haired Fox Terrier": 118,
+    "Yorkshire Terrier": 119
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2"
+}

config.toml

@@ -0,0 +1,27 @@
+[training_args]
+output_dir="/Users/andrewmayes/Openclassroom/CanineNet/code/"
+evaluation_strategy="steps"
+save_strategy="steps"
+learning_rate=5e-5
+#per_device_train_batch_size=32 # 512
+#per_device_eval_batch_size=32 # 512
+# num_train_epochs=5,
+eval_delay=0 # 50
+eval_steps=0.01
+#eval_accumulation_steps
+gradient_accumulation_steps=4
+gradient_checkpointing=true
+optim="adafactor"
+max_steps=1000 # 100
+#logging_dir=""
+#log_level="error"
+load_best_model_at_end=true
+metric_for_best_model="f1"
+greater_is_better=true
+#use_mps_device=true
+logging_steps=0.01
+save_steps=0.01
+#auto_find_batch_size=true
+report_to="mlflow"
+save_total_limit=2
+#hub_model_id="amaye15/SwinV2-Base-Document-Classifier"

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdfbbffedbfd2101d40b2d3fc2a455d55ed8c133dbd5dd6e0fa7b523bbde9763
+size 343586952

preprocessor_config.json

@@ -0,0 +1,36 @@
+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dce16b1f9ce5f931dcd204e3af3c9d0442a9ed484f5ae2cef9bc298d0fffea0a
+size 5112