End of training

README.md

@@ -0,0 +1,191 @@
+---
+license: apache-2.0
+base_model: google/siglip-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- stanford-dogs
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: google-siglip-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: 0.8406219630709426
+    - name: F1
+      type: f1
+      value: 0.8370399997829452
+    - name: Precision
+      type: precision
+      value: 0.8413608553082093
+    - name: Recall
+      type: recall
+      value: 0.837163245644009
+---
+
+<!-- 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-siglip-base-patch16-224-batch32-lr0.005-standford-dogs
+
+This model is a fine-tuned version of [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) on the stanford-dogs dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5144
+- Accuracy: 0.8406
+- F1: 0.8370
+- Precision: 0.8414
+- Recall: 0.8372
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 4.9299        | 0.0777 | 10   | 4.4830          | 0.0437   | 0.0194 | 0.0268    | 0.0406 |
+| 4.4701        | 0.1553 | 20   | 4.1771          | 0.0897   | 0.0695 | 0.1215    | 0.0872 |
+| 3.925         | 0.2330 | 30   | 3.1443          | 0.2087   | 0.1820 | 0.2840    | 0.2050 |
+| 3.3549        | 0.3107 | 40   | 2.5232          | 0.3494   | 0.3224 | 0.4544    | 0.3458 |
+| 2.8512        | 0.3883 | 50   | 2.1503          | 0.4332   | 0.3991 | 0.5244    | 0.4215 |
+| 2.5846        | 0.4660 | 60   | 1.7744          | 0.5180   | 0.4863 | 0.6052    | 0.5127 |
+| 2.2942        | 0.5437 | 70   | 1.5619          | 0.5437   | 0.5333 | 0.6446    | 0.5421 |
+| 2.1577        | 0.6214 | 80   | 1.5739          | 0.5622   | 0.5452 | 0.6301    | 0.5600 |
+| 1.9423        | 0.6990 | 90   | 1.2747          | 0.6198   | 0.6010 | 0.6840    | 0.6156 |
+| 1.663         | 0.7767 | 100  | 1.3143          | 0.6115   | 0.6013 | 0.7101    | 0.6084 |
+| 1.8093        | 0.8544 | 110  | 1.1125          | 0.6667   | 0.6491 | 0.7107    | 0.6618 |
+| 1.6925        | 0.9320 | 120  | 1.3373          | 0.6110   | 0.5992 | 0.6918    | 0.6074 |
+| 1.7028        | 1.0097 | 130  | 1.0352          | 0.6934   | 0.6810 | 0.7321    | 0.6878 |
+| 1.4451        | 1.0874 | 140  | 0.9891          | 0.7000   | 0.6924 | 0.7438    | 0.6931 |
+| 1.4583        | 1.1650 | 150  | 0.9574          | 0.7153   | 0.7045 | 0.7411    | 0.7120 |
+| 1.4219        | 1.2427 | 160  | 0.9801          | 0.7024   | 0.6929 | 0.7473    | 0.6991 |
+| 1.442         | 1.3204 | 170  | 1.0351          | 0.6829   | 0.6794 | 0.7294    | 0.6780 |
+| 1.3218        | 1.3981 | 180  | 0.9178          | 0.7238   | 0.7196 | 0.7632    | 0.7227 |
+| 1.2486        | 1.4757 | 190  | 0.8521          | 0.7415   | 0.7361 | 0.7669    | 0.7363 |
+| 1.2239        | 1.5534 | 200  | 0.9499          | 0.7046   | 0.6979 | 0.7582    | 0.7027 |
+| 1.2724        | 1.6311 | 210  | 0.9332          | 0.7157   | 0.7050 | 0.7604    | 0.7111 |
+| 1.205         | 1.7087 | 220  | 0.8662          | 0.7391   | 0.7287 | 0.7770    | 0.7349 |
+| 1.2263        | 1.7864 | 230  | 0.8464          | 0.7393   | 0.7302 | 0.7668    | 0.7343 |
+| 1.1301        | 1.8641 | 240  | 0.8417          | 0.7374   | 0.7302 | 0.7795    | 0.7342 |
+| 1.2035        | 1.9417 | 250  | 0.7798          | 0.7629   | 0.7553 | 0.7873    | 0.7600 |
+| 1.1138        | 2.0194 | 260  | 0.8089          | 0.7495   | 0.7368 | 0.7789    | 0.7444 |
+| 0.9266        | 2.0971 | 270  | 0.7645          | 0.7566   | 0.7539 | 0.7864    | 0.7550 |
+| 0.9438        | 2.1748 | 280  | 0.7555          | 0.7653   | 0.7575 | 0.7916    | 0.7609 |
+| 0.9776        | 2.2524 | 290  | 0.7824          | 0.7544   | 0.7494 | 0.7787    | 0.7531 |
+| 0.9083        | 2.3301 | 300  | 0.7687          | 0.7626   | 0.7543 | 0.7914    | 0.7606 |
+| 0.9157        | 2.4078 | 310  | 0.7573          | 0.7682   | 0.7634 | 0.7966    | 0.7637 |
+| 0.9962        | 2.4854 | 320  | 0.7704          | 0.7631   | 0.7539 | 0.7950    | 0.7600 |
+| 0.9313        | 2.5631 | 330  | 0.7552          | 0.7609   | 0.7560 | 0.7879    | 0.7564 |
+| 0.8893        | 2.6408 | 340  | 0.7491          | 0.7655   | 0.7568 | 0.7846    | 0.7621 |
+| 0.9724        | 2.7184 | 350  | 0.7167          | 0.7787   | 0.7721 | 0.7976    | 0.7738 |
+| 0.9045        | 2.7961 | 360  | 0.7067          | 0.7799   | 0.7728 | 0.8082    | 0.7739 |
+| 0.8922        | 2.8738 | 370  | 0.7028          | 0.7799   | 0.7696 | 0.8017    | 0.7745 |
+| 0.9082        | 2.9515 | 380  | 0.6934          | 0.7767   | 0.7716 | 0.7892    | 0.7736 |
+| 0.8438        | 3.0291 | 390  | 0.6680          | 0.7869   | 0.7795 | 0.8068    | 0.7814 |
+| 0.7603        | 3.1068 | 400  | 0.6601          | 0.7918   | 0.7837 | 0.8060    | 0.7871 |
+| 0.6695        | 3.1845 | 410  | 0.6628          | 0.7954   | 0.7881 | 0.8113    | 0.7927 |
+| 0.7315        | 3.2621 | 420  | 0.6564          | 0.7983   | 0.7908 | 0.8113    | 0.7942 |
+| 0.7155        | 3.3398 | 430  | 0.6562          | 0.7906   | 0.7876 | 0.8119    | 0.7866 |
+| 0.7216        | 3.4175 | 440  | 0.6499          | 0.7881   | 0.7828 | 0.8048    | 0.7850 |
+| 0.7167        | 3.4951 | 450  | 0.6440          | 0.7942   | 0.7890 | 0.8073    | 0.7928 |
+| 0.6772        | 3.5728 | 460  | 0.6147          | 0.8064   | 0.8014 | 0.8161    | 0.8021 |
+| 0.7298        | 3.6505 | 470  | 0.6643          | 0.7974   | 0.7942 | 0.8172    | 0.7928 |
+| 0.6712        | 3.7282 | 480  | 0.6114          | 0.8071   | 0.8032 | 0.8182    | 0.8030 |
+| 0.703         | 3.8058 | 490  | 0.6246          | 0.8069   | 0.8011 | 0.8188    | 0.8027 |
+| 0.724         | 3.8835 | 500  | 0.6386          | 0.8022   | 0.7958 | 0.8167    | 0.7980 |
+| 0.6467        | 3.9612 | 510  | 0.6490          | 0.8044   | 0.7981 | 0.8154    | 0.8002 |
+| 0.6781        | 4.0388 | 520  | 0.6296          | 0.8078   | 0.8037 | 0.8208    | 0.8055 |
+| 0.5615        | 4.1165 | 530  | 0.6108          | 0.8117   | 0.8075 | 0.8196    | 0.8091 |
+| 0.5095        | 4.1942 | 540  | 0.6272          | 0.8090   | 0.8045 | 0.8228    | 0.8042 |
+| 0.562         | 4.2718 | 550  | 0.6529          | 0.8008   | 0.7966 | 0.8197    | 0.7965 |
+| 0.5307        | 4.3495 | 560  | 0.6290          | 0.8071   | 0.8036 | 0.8164    | 0.8044 |
+| 0.5428        | 4.4272 | 570  | 0.6033          | 0.8154   | 0.8097 | 0.8221    | 0.8115 |
+| 0.5248        | 4.5049 | 580  | 0.6166          | 0.8141   | 0.8035 | 0.8228    | 0.8094 |
+| 0.5608        | 4.5825 | 590  | 0.6010          | 0.8127   | 0.8061 | 0.8243    | 0.8078 |
+| 0.5151        | 4.6602 | 600  | 0.6155          | 0.8061   | 0.8029 | 0.8231    | 0.8024 |
+| 0.5712        | 4.7379 | 610  | 0.6015          | 0.8134   | 0.8091 | 0.8208    | 0.8093 |
+| 0.564         | 4.8155 | 620  | 0.5740          | 0.8265   | 0.8217 | 0.8344    | 0.8222 |
+| 0.5198        | 4.8932 | 630  | 0.5693          | 0.8239   | 0.8165 | 0.8321    | 0.8186 |
+| 0.4851        | 4.9709 | 640  | 0.5574          | 0.8273   | 0.8225 | 0.8315    | 0.8242 |
+| 0.4428        | 5.0485 | 650  | 0.5711          | 0.8253   | 0.8187 | 0.8331    | 0.8199 |
+| 0.3877        | 5.1262 | 660  | 0.5714          | 0.8234   | 0.8198 | 0.8277    | 0.8210 |
+| 0.4377        | 5.2039 | 670  | 0.5736          | 0.8229   | 0.8168 | 0.8301    | 0.8196 |
+| 0.4056        | 5.2816 | 680  | 0.5670          | 0.8260   | 0.8228 | 0.8365    | 0.8232 |
+| 0.4679        | 5.3592 | 690  | 0.5549          | 0.8297   | 0.8247 | 0.8344    | 0.8267 |
+| 0.3742        | 5.4369 | 700  | 0.5582          | 0.8246   | 0.8188 | 0.8314    | 0.8210 |
+| 0.4215        | 5.5146 | 710  | 0.5588          | 0.8246   | 0.8211 | 0.8305    | 0.8209 |
+| 0.4136        | 5.5922 | 720  | 0.5594          | 0.8202   | 0.8153 | 0.8257    | 0.8160 |
+| 0.4464        | 5.6699 | 730  | 0.5541          | 0.8258   | 0.8216 | 0.8307    | 0.8223 |
+| 0.4684        | 5.7476 | 740  | 0.5477          | 0.8275   | 0.8241 | 0.8326    | 0.8238 |
+| 0.4094        | 5.8252 | 750  | 0.5436          | 0.8287   | 0.8247 | 0.8341    | 0.8257 |
+| 0.3757        | 5.9029 | 760  | 0.5514          | 0.8307   | 0.8258 | 0.8355    | 0.8275 |
+| 0.388         | 5.9806 | 770  | 0.5523          | 0.8285   | 0.8244 | 0.8320    | 0.8254 |
+| 0.3528        | 6.0583 | 780  | 0.5500          | 0.8246   | 0.8195 | 0.8291    | 0.8206 |
+| 0.3466        | 6.1359 | 790  | 0.5358          | 0.8311   | 0.8277 | 0.8335    | 0.8276 |
+| 0.3149        | 6.2136 | 800  | 0.5389          | 0.8326   | 0.8283 | 0.8368    | 0.8286 |
+| 0.3106        | 6.2913 | 810  | 0.5277          | 0.8379   | 0.8344 | 0.8397    | 0.8342 |
+| 0.3226        | 6.3689 | 820  | 0.5399          | 0.8304   | 0.8266 | 0.8339    | 0.8268 |
+| 0.3578        | 6.4466 | 830  | 0.5370          | 0.8328   | 0.8287 | 0.8369    | 0.8287 |
+| 0.348         | 6.5243 | 840  | 0.5371          | 0.8307   | 0.8276 | 0.8340    | 0.8275 |
+| 0.3228        | 6.6019 | 850  | 0.5319          | 0.8338   | 0.8295 | 0.8371    | 0.8306 |
+| 0.3022        | 6.6796 | 860  | 0.5332          | 0.8331   | 0.8295 | 0.8365    | 0.8295 |
+| 0.3311        | 6.7573 | 870  | 0.5314          | 0.8307   | 0.8278 | 0.8329    | 0.8273 |
+| 0.3221        | 6.8350 | 880  | 0.5267          | 0.8358   | 0.8324 | 0.8379    | 0.8322 |
+| 0.3132        | 6.9126 | 890  | 0.5293          | 0.8336   | 0.8293 | 0.8363    | 0.8295 |
+| 0.2821        | 6.9903 | 900  | 0.5279          | 0.8314   | 0.8272 | 0.8342    | 0.8277 |
+| 0.2486        | 7.0680 | 910  | 0.5286          | 0.8326   | 0.8287 | 0.8339    | 0.8291 |
+| 0.2936        | 7.1456 | 920  | 0.5250          | 0.8370   | 0.8330 | 0.8380    | 0.8336 |
+| 0.292         | 7.2233 | 930  | 0.5205          | 0.8392   | 0.8351 | 0.8408    | 0.8353 |
+| 0.2806        | 7.3010 | 940  | 0.5207          | 0.8387   | 0.8349 | 0.8403    | 0.8352 |
+| 0.2406        | 7.3786 | 950  | 0.5148          | 0.8404   | 0.8364 | 0.8406    | 0.8369 |
+| 0.2941        | 7.4563 | 960  | 0.5145          | 0.8404   | 0.8372 | 0.8419    | 0.8372 |
+| 0.2597        | 7.5340 | 970  | 0.5156          | 0.8394   | 0.8358 | 0.8401    | 0.8359 |
+| 0.2534        | 7.6117 | 980  | 0.5157          | 0.8404   | 0.8368 | 0.8410    | 0.8369 |
+| 0.2487        | 7.6893 | 990  | 0.5150          | 0.8401   | 0.8364 | 0.8408    | 0.8366 |
+| 0.2618        | 7.7670 | 1000 | 0.5144          | 0.8406   | 0.8370 | 0.8414    | 0.8372 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,261 @@
+{
+  "_name_or_path": "google/siglip-base-patch16-224",
+  "architectures": [
+    "SiglipForImageClassification"
+  ],
+  "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"
+  },
+  "initializer_factor": 1.0,
+  "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
+  },
+  "model_type": "siglip",
+  "problem_type": "single_label_classification",
+  "text_config": {
+    "model_type": "siglip_text_model"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "vision_config": {
+    "model_type": "siglip_vision_model"
+  }
+}

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:dad239e61801b79bf3b82e4ed993efc8c403be2dadcfeeef45396800c1012393
+size 371930976

preprocessor_config.json

@@ -0,0 +1,37 @@
+{
+  "_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": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24f1016240c1d1936589d86a7277d429cefa1660c376cc392f942fd63cc41c46
+size 5112