End of training

README.md

@@ -0,0 +1,191 @@
+---
+license: apache-2.0
+base_model: microsoft/swinv2-base-patch4-window16-256
+tags:
+- generated_from_trainer
+datasets:
+- stanford-dogs
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: microsoft-swinv2-base-patch4-window16-256-batch32-lr5e-05-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: 0.9467930029154519
+    - name: F1
+      type: f1
+      value: 0.9450299849824627
+    - name: Precision
+      type: precision
+      value: 0.9479779072439513
+    - name: Recall
+      type: recall
+      value: 0.9453246844288115
+---
+
+<!-- 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. -->
+
+# microsoft-swinv2-base-patch4-window16-256-batch32-lr5e-05-standford-dogs
+
+This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1856
+- Accuracy: 0.9468
+- F1: 0.9450
+- Precision: 0.9480
+- Recall: 0.9453
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 4.7437        | 0.0777 | 10   | 4.6395          | 0.0862   | 0.0519 | 0.0499    | 0.0821 |
+| 4.5551        | 0.1553 | 20   | 4.3696          | 0.1713   | 0.1162 | 0.1608    | 0.1573 |
+| 4.2151        | 0.2330 | 30   | 3.8252          | 0.3188   | 0.2681 | 0.4133    | 0.3021 |
+| 3.5619        | 0.3107 | 40   | 2.8929          | 0.6368   | 0.5785 | 0.6552    | 0.6211 |
+| 2.6253        | 0.3883 | 50   | 1.8693          | 0.7850   | 0.7538 | 0.7906    | 0.7733 |
+| 1.8818        | 0.4660 | 60   | 1.1203          | 0.8542   | 0.8406 | 0.8667    | 0.8468 |
+| 1.3652        | 0.5437 | 70   | 0.7330          | 0.8880   | 0.8780 | 0.9039    | 0.8850 |
+| 1.0456        | 0.6214 | 80   | 0.5269          | 0.9084   | 0.9015 | 0.9101    | 0.9050 |
+| 0.9039        | 0.6990 | 90   | 0.4139          | 0.9181   | 0.9093 | 0.9213    | 0.9150 |
+| 0.7965        | 0.7767 | 100  | 0.3441          | 0.9249   | 0.9181 | 0.9315    | 0.9221 |
+| 0.7053        | 0.8544 | 110  | 0.3184          | 0.9225   | 0.9163 | 0.9320    | 0.9208 |
+| 0.6907        | 0.9320 | 120  | 0.2870          | 0.9283   | 0.9261 | 0.9324    | 0.9270 |
+| 0.6293        | 1.0097 | 130  | 0.2760          | 0.9276   | 0.9245 | 0.9329    | 0.9260 |
+| 0.5564        | 1.0874 | 140  | 0.2517          | 0.9339   | 0.9308 | 0.9362    | 0.9320 |
+| 0.5902        | 1.1650 | 150  | 0.2500          | 0.9351   | 0.9308 | 0.9371    | 0.9328 |
+| 0.5269        | 1.2427 | 160  | 0.2429          | 0.9334   | 0.9307 | 0.9370    | 0.9317 |
+| 0.5148        | 1.3204 | 170  | 0.2358          | 0.9393   | 0.9368 | 0.9407    | 0.9373 |
+| 0.4998        | 1.3981 | 180  | 0.2451          | 0.9310   | 0.9270 | 0.9357    | 0.9283 |
+| 0.4797        | 1.4757 | 190  | 0.2425          | 0.9325   | 0.9287 | 0.9377    | 0.9315 |
+| 0.4933        | 1.5534 | 200  | 0.2360          | 0.9281   | 0.9257 | 0.9333    | 0.9266 |
+| 0.4414        | 1.6311 | 210  | 0.2201          | 0.9371   | 0.9343 | 0.9398    | 0.9351 |
+| 0.4401        | 1.7087 | 220  | 0.2248          | 0.9346   | 0.9327 | 0.9375    | 0.9337 |
+| 0.4023        | 1.7864 | 230  | 0.2199          | 0.9344   | 0.9282 | 0.9381    | 0.9317 |
+| 0.4723        | 1.8641 | 240  | 0.2071          | 0.9419   | 0.9389 | 0.9437    | 0.9401 |
+| 0.4593        | 1.9417 | 250  | 0.2123          | 0.9402   | 0.9371 | 0.9421    | 0.9382 |
+| 0.4544        | 2.0194 | 260  | 0.2191          | 0.9385   | 0.9347 | 0.9396    | 0.9371 |
+| 0.3871        | 2.0971 | 270  | 0.2158          | 0.9395   | 0.9372 | 0.9401    | 0.9378 |
+| 0.4162        | 2.1748 | 280  | 0.2073          | 0.9385   | 0.9353 | 0.9396    | 0.9364 |
+| 0.3774        | 2.2524 | 290  | 0.1981          | 0.9397   | 0.9387 | 0.9422    | 0.9387 |
+| 0.3895        | 2.3301 | 300  | 0.2008          | 0.9400   | 0.9361 | 0.9395    | 0.9376 |
+| 0.3804        | 2.4078 | 310  | 0.2018          | 0.9431   | 0.9396 | 0.9443    | 0.9412 |
+| 0.3783        | 2.4854 | 320  | 0.2038          | 0.9422   | 0.9384 | 0.9439    | 0.9403 |
+| 0.4376        | 2.5631 | 330  | 0.1968          | 0.9419   | 0.9404 | 0.9459    | 0.9414 |
+| 0.3696        | 2.6408 | 340  | 0.2011          | 0.9441   | 0.9422 | 0.9464    | 0.9430 |
+| 0.3954        | 2.7184 | 350  | 0.1997          | 0.9417   | 0.9379 | 0.9430    | 0.9399 |
+| 0.3651        | 2.7961 | 360  | 0.1952          | 0.9434   | 0.9392 | 0.9407    | 0.9415 |
+| 0.3646        | 2.8738 | 370  | 0.2045          | 0.9429   | 0.9391 | 0.9459    | 0.9413 |
+| 0.3532        | 2.9515 | 380  | 0.1991          | 0.9427   | 0.9394 | 0.9455    | 0.9413 |
+| 0.342         | 3.0291 | 390  | 0.1958          | 0.9410   | 0.9399 | 0.9441    | 0.9404 |
+| 0.3706        | 3.1068 | 400  | 0.2010          | 0.9419   | 0.9401 | 0.9442    | 0.9406 |
+| 0.3031        | 3.1845 | 410  | 0.2013          | 0.9424   | 0.9407 | 0.9449    | 0.9410 |
+| 0.3345        | 3.2621 | 420  | 0.2022          | 0.9414   | 0.9399 | 0.9438    | 0.9406 |
+| 0.3356        | 3.3398 | 430  | 0.1927          | 0.9470   | 0.9451 | 0.9500    | 0.9453 |
+| 0.3538        | 3.4175 | 440  | 0.1927          | 0.9446   | 0.9422 | 0.9472    | 0.9430 |
+| 0.3505        | 3.4951 | 450  | 0.1909          | 0.9480   | 0.9461 | 0.9498    | 0.9466 |
+| 0.3398        | 3.5728 | 460  | 0.1917          | 0.9453   | 0.9419 | 0.9475    | 0.9436 |
+| 0.3303        | 3.6505 | 470  | 0.1895          | 0.9483   | 0.9453 | 0.9506    | 0.9464 |
+| 0.3685        | 3.7282 | 480  | 0.1883          | 0.9458   | 0.9442 | 0.9468    | 0.9445 |
+| 0.3125        | 3.8058 | 490  | 0.1926          | 0.9441   | 0.9422 | 0.9462    | 0.9426 |
+| 0.3857        | 3.8835 | 500  | 0.1911          | 0.9446   | 0.9426 | 0.9473    | 0.9430 |
+| 0.3407        | 3.9612 | 510  | 0.1825          | 0.9470   | 0.9454 | 0.9486    | 0.9459 |
+| 0.3545        | 4.0388 | 520  | 0.1919          | 0.9444   | 0.9428 | 0.9448    | 0.9432 |
+| 0.306         | 4.1165 | 530  | 0.1901          | 0.9466   | 0.9437 | 0.9471    | 0.9450 |
+| 0.2511        | 4.1942 | 540  | 0.2026          | 0.9431   | 0.9388 | 0.9448    | 0.9410 |
+| 0.3233        | 4.2718 | 550  | 0.1950          | 0.9453   | 0.9433 | 0.9470    | 0.9438 |
+| 0.2793        | 4.3495 | 560  | 0.1973          | 0.9453   | 0.9437 | 0.9466    | 0.9444 |
+| 0.3035        | 4.4272 | 570  | 0.1944          | 0.9470   | 0.9454 | 0.9491    | 0.9459 |
+| 0.2776        | 4.5049 | 580  | 0.2030          | 0.9412   | 0.9393 | 0.9445    | 0.9398 |
+| 0.3204        | 4.5825 | 590  | 0.1959          | 0.9441   | 0.9417 | 0.9468    | 0.9428 |
+| 0.2868        | 4.6602 | 600  | 0.1959          | 0.9429   | 0.9413 | 0.9437    | 0.9414 |
+| 0.3325        | 4.7379 | 610  | 0.1991          | 0.9414   | 0.9389 | 0.9435    | 0.9401 |
+| 0.3255        | 4.8155 | 620  | 0.1894          | 0.9441   | 0.9425 | 0.9448    | 0.9431 |
+| 0.2744        | 4.8932 | 630  | 0.1915          | 0.9434   | 0.9411 | 0.9434    | 0.9421 |
+| 0.2945        | 4.9709 | 640  | 0.1932          | 0.9453   | 0.9415 | 0.9468    | 0.9436 |
+| 0.253         | 5.0485 | 650  | 0.1928          | 0.9448   | 0.9423 | 0.9465    | 0.9435 |
+| 0.2614        | 5.1262 | 660  | 0.1942          | 0.9451   | 0.9441 | 0.9478    | 0.9444 |
+| 0.2699        | 5.2039 | 670  | 0.1924          | 0.9468   | 0.9433 | 0.9479    | 0.9451 |
+| 0.2839        | 5.2816 | 680  | 0.1894          | 0.9461   | 0.9442 | 0.9475    | 0.9447 |
+| 0.2353        | 5.3592 | 690  | 0.1947          | 0.9427   | 0.9407 | 0.9435    | 0.9410 |
+| 0.2627        | 5.4369 | 700  | 0.1964          | 0.9419   | 0.9405 | 0.9440    | 0.9409 |
+| 0.2592        | 5.5146 | 710  | 0.1893          | 0.9456   | 0.9440 | 0.9468    | 0.9441 |
+| 0.2634        | 5.5922 | 720  | 0.1918          | 0.9458   | 0.9431 | 0.9473    | 0.9443 |
+| 0.294         | 5.6699 | 730  | 0.1922          | 0.9446   | 0.9417 | 0.9457    | 0.9428 |
+| 0.2565        | 5.7476 | 740  | 0.1907          | 0.9456   | 0.9432 | 0.9469    | 0.9439 |
+| 0.2657        | 5.8252 | 750  | 0.1902          | 0.9453   | 0.9415 | 0.9464    | 0.9434 |
+| 0.2945        | 5.9029 | 760  | 0.1872          | 0.9453   | 0.9427 | 0.9457    | 0.9439 |
+| 0.2758        | 5.9806 | 770  | 0.1855          | 0.9444   | 0.9432 | 0.9460    | 0.9430 |
+| 0.226         | 6.0583 | 780  | 0.1867          | 0.9470   | 0.9456 | 0.9488    | 0.9457 |
+| 0.2105        | 6.1359 | 790  | 0.1866          | 0.9470   | 0.9446 | 0.9482    | 0.9451 |
+| 0.2524        | 6.2136 | 800  | 0.1891          | 0.9456   | 0.9441 | 0.9470    | 0.9441 |
+| 0.2987        | 6.2913 | 810  | 0.1879          | 0.9463   | 0.9442 | 0.9472    | 0.9447 |
+| 0.2393        | 6.3689 | 820  | 0.1876          | 0.9456   | 0.9439 | 0.9467    | 0.9442 |
+| 0.2779        | 6.4466 | 830  | 0.1870          | 0.9473   | 0.9460 | 0.9486    | 0.9463 |
+| 0.3117        | 6.5243 | 840  | 0.1866          | 0.9470   | 0.9450 | 0.9483    | 0.9455 |
+| 0.2574        | 6.6019 | 850  | 0.1853          | 0.9468   | 0.9449 | 0.9481    | 0.9454 |
+| 0.2307        | 6.6796 | 860  | 0.1886          | 0.9463   | 0.9441 | 0.9475    | 0.9447 |
+| 0.2771        | 6.7573 | 870  | 0.1878          | 0.9456   | 0.9437 | 0.9464    | 0.9440 |
+| 0.2575        | 6.8350 | 880  | 0.1868          | 0.9458   | 0.9440 | 0.9465    | 0.9443 |
+| 0.2422        | 6.9126 | 890  | 0.1857          | 0.9463   | 0.9447 | 0.9466    | 0.9448 |
+| 0.2564        | 6.9903 | 900  | 0.1861          | 0.9451   | 0.9434 | 0.9458    | 0.9437 |
+| 0.222         | 7.0680 | 910  | 0.1866          | 0.9461   | 0.9442 | 0.9471    | 0.9445 |
+| 0.2467        | 7.1456 | 920  | 0.1862          | 0.9456   | 0.9438 | 0.9464    | 0.9441 |
+| 0.2412        | 7.2233 | 930  | 0.1860          | 0.9463   | 0.9449 | 0.9474    | 0.9451 |
+| 0.2518        | 7.3010 | 940  | 0.1857          | 0.9458   | 0.9442 | 0.9466    | 0.9445 |
+| 0.2811        | 7.3786 | 950  | 0.1857          | 0.9463   | 0.9446 | 0.9472    | 0.9448 |
+| 0.2255        | 7.4563 | 960  | 0.1856          | 0.9468   | 0.9451 | 0.9477    | 0.9453 |
+| 0.2425        | 7.5340 | 970  | 0.1857          | 0.9466   | 0.9449 | 0.9478    | 0.9451 |
+| 0.2352        | 7.6117 | 980  | 0.1856          | 0.9468   | 0.9450 | 0.9480    | 0.9453 |
+| 0.2328        | 7.6893 | 990  | 0.1855          | 0.9468   | 0.9450 | 0.9480    | 0.9453 |
+| 0.2353        | 7.7670 | 1000 | 0.1856          | 0.9468   | 0.9450 | 0.9480    | 0.9453 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,303 @@
+{
+  "_name_or_path": "microsoft/swinv2-base-patch4-window16-256",
+  "architectures": [
+    "Swinv2ForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    18,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 128,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "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": 256,
+  "initializer_range": 0.02,
+  "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,
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+    "Norwich Terrier": 81,
+    "Old English Sheepdog": 82,
+    "Otterhound": 83,
+    "Papillon": 84,
+    "Pekinese": 85,
+    "Pembroke": 86,
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+    "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,
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+    "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-05,
+  "mlp_ratio": 4.0,
+  "model_type": "swinv2",
+  "num_channels": 3,
+  "num_heads": [
+    4,
+    8,
+    16,
+    32
+  ],
+  "num_layers": 4,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "patch_size": 4,
+  "path_norm": true,
+  "pretrained_window_sizes": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "use_absolute_embeddings": false,
+  "window_size": 16
+}

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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e18f50b9ae839ca8b94bea38205322291e4b0362b4df1f378fee5b9e1e22a2f9
+size 348129336

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": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 256,
+    "width": 256
+  }
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:adbf3f8ee263e567b265649efdbd281e949370bde35f578c80c1251f0e7d969d
+size 5112