End of training

README.md

@@ -0,0 +1,191 @@
+---
+license: apache-2.0
+base_model: microsoft/resnet-50
+tags:
+- generated_from_trainer
+datasets:
+- stanford-dogs
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: microsoft-resnet-50-batch32-lr0.0005-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: 0.8386783284742468
+    - name: F1
+      type: f1
+      value: 0.8259546998355447
+    - name: Precision
+      type: precision
+      value: 0.8457483127517197
+    - name: Recall
+      type: recall
+      value: 0.8314858626273427
+---
+
+<!-- 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-resnet-50-batch32-lr0.0005-standford-dogs
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the stanford-dogs dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1545
+- Accuracy: 0.8387
+- F1: 0.8260
+- Precision: 0.8457
+- Recall: 0.8315
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 4.7829        | 0.0777 | 10   | 4.7747          | 0.2119   | 0.1874 | 0.3919    | 0.1982 |
+| 4.7714        | 0.1553 | 20   | 4.7572          | 0.2038   | 0.1842 | 0.4262    | 0.1836 |
+| 4.7606        | 0.2330 | 30   | 4.7367          | 0.3586   | 0.3433 | 0.6517    | 0.3307 |
+| 4.747         | 0.3107 | 40   | 4.7149          | 0.4303   | 0.4272 | 0.7734    | 0.4039 |
+| 4.7253        | 0.3883 | 50   | 4.6846          | 0.4361   | 0.4678 | 0.7906    | 0.4160 |
+| 4.7069        | 0.4660 | 60   | 4.6534          | 0.5330   | 0.5397 | 0.8048    | 0.5093 |
+| 4.6857        | 0.5437 | 70   | 4.6177          | 0.5500   | 0.5511 | 0.7998    | 0.5264 |
+| 4.6569        | 0.6214 | 80   | 4.5764          | 0.5739   | 0.5800 | 0.8208    | 0.5517 |
+| 4.6293        | 0.6990 | 90   | 4.5359          | 0.6142   | 0.6149 | 0.8075    | 0.5926 |
+| 4.5953        | 0.7767 | 100  | 4.4828          | 0.6207   | 0.6233 | 0.8109    | 0.6000 |
+| 4.5651        | 0.8544 | 110  | 4.4257          | 0.6591   | 0.6585 | 0.8148    | 0.6393 |
+| 4.5296        | 0.9320 | 120  | 4.3647          | 0.7063   | 0.7012 | 0.8284    | 0.6882 |
+| 4.4911        | 1.0097 | 130  | 4.2998          | 0.7089   | 0.7074 | 0.8326    | 0.6924 |
+| 4.4442        | 1.0874 | 140  | 4.2288          | 0.6939   | 0.6890 | 0.8302    | 0.6759 |
+| 4.3912        | 1.1650 | 150  | 4.1527          | 0.6873   | 0.6863 | 0.8262    | 0.6703 |
+| 4.3393        | 1.2427 | 160  | 4.0884          | 0.7250   | 0.7127 | 0.8251    | 0.7082 |
+| 4.3019        | 1.3204 | 170  | 3.9946          | 0.7262   | 0.7152 | 0.8234    | 0.7098 |
+| 4.2366        | 1.3981 | 180  | 3.9314          | 0.7301   | 0.7177 | 0.8230    | 0.7143 |
+| 4.1966        | 1.4757 | 190  | 3.8398          | 0.7325   | 0.7196 | 0.8169    | 0.7175 |
+| 4.1402        | 1.5534 | 200  | 3.7587          | 0.7381   | 0.7217 | 0.8149    | 0.7221 |
+| 4.0771        | 1.6311 | 210  | 3.6745          | 0.7310   | 0.7149 | 0.8125    | 0.7160 |
+| 4.0436        | 1.7087 | 220  | 3.5729          | 0.7364   | 0.7189 | 0.8121    | 0.7214 |
+| 3.9697        | 1.7864 | 230  | 3.5030          | 0.7490   | 0.7339 | 0.8172    | 0.7358 |
+| 3.9181        | 1.8641 | 240  | 3.4505          | 0.7541   | 0.7379 | 0.8123    | 0.7408 |
+| 3.8573        | 1.9417 | 250  | 3.3529          | 0.7646   | 0.7453 | 0.8136    | 0.7521 |
+| 3.8077        | 2.0194 | 260  | 3.2566          | 0.7660   | 0.7482 | 0.8093    | 0.7540 |
+| 3.7449        | 2.0971 | 270  | 3.1869          | 0.7709   | 0.7510 | 0.8144    | 0.7588 |
+| 3.682         | 2.1748 | 280  | 3.0898          | 0.7668   | 0.7440 | 0.8097    | 0.7548 |
+| 3.6461        | 2.2524 | 290  | 3.0377          | 0.7641   | 0.7381 | 0.8100    | 0.7511 |
+| 3.6004        | 2.3301 | 300  | 2.9001          | 0.7648   | 0.7384 | 0.8061    | 0.7522 |
+| 3.5478        | 2.4078 | 310  | 2.8623          | 0.7653   | 0.7410 | 0.8060    | 0.7529 |
+| 3.4971        | 2.4854 | 320  | 2.7961          | 0.7675   | 0.7447 | 0.8068    | 0.7558 |
+| 3.4446        | 2.5631 | 330  | 2.6960          | 0.7690   | 0.7486 | 0.8128    | 0.7582 |
+| 3.4093        | 2.6408 | 340  | 2.6480          | 0.7821   | 0.7652 | 0.8151    | 0.7718 |
+| 3.3994        | 2.7184 | 350  | 2.5330          | 0.7847   | 0.7676 | 0.8156    | 0.7742 |
+| 3.2963        | 2.7961 | 360  | 2.4866          | 0.7855   | 0.7681 | 0.8154    | 0.7752 |
+| 3.2615        | 2.8738 | 370  | 2.4344          | 0.7891   | 0.7740 | 0.8172    | 0.7792 |
+| 3.2024        | 2.9515 | 380  | 2.4011          | 0.7794   | 0.7638 | 0.8126    | 0.7694 |
+| 3.1641        | 3.0291 | 390  | 2.3039          | 0.7835   | 0.7659 | 0.8100    | 0.7736 |
+| 3.0719        | 3.1068 | 400  | 2.2471          | 0.7796   | 0.7608 | 0.8072    | 0.7691 |
+| 3.0808        | 3.1845 | 410  | 2.2130          | 0.7896   | 0.7717 | 0.8137    | 0.7795 |
+| 2.9916        | 3.2621 | 420  | 2.1387          | 0.7823   | 0.7652 | 0.8104    | 0.7718 |
+| 2.9898        | 3.3398 | 430  | 2.0905          | 0.7981   | 0.7821 | 0.8250    | 0.7886 |
+| 2.9597        | 3.4175 | 440  | 2.0260          | 0.7923   | 0.7769 | 0.8192    | 0.7826 |
+| 2.9068        | 3.4951 | 450  | 1.9944          | 0.7976   | 0.7816 | 0.8233    | 0.7877 |
+| 2.8423        | 3.5728 | 460  | 1.9643          | 0.7976   | 0.7805 | 0.8185    | 0.7876 |
+| 2.8323        | 3.6505 | 470  | 1.8926          | 0.7935   | 0.7754 | 0.8136    | 0.7837 |
+| 2.7814        | 3.7282 | 480  | 1.8676          | 0.8017   | 0.7856 | 0.8208    | 0.7917 |
+| 2.7337        | 3.8058 | 490  | 1.8320          | 0.8052   | 0.7905 | 0.8246    | 0.7957 |
+| 2.7215        | 3.8835 | 500  | 1.8003          | 0.7986   | 0.7834 | 0.8208    | 0.7890 |
+| 2.6456        | 3.9612 | 510  | 1.7754          | 0.8005   | 0.7848 | 0.8230    | 0.7914 |
+| 2.6494        | 4.0388 | 520  | 1.7083          | 0.8054   | 0.7895 | 0.8252    | 0.7967 |
+| 2.5878        | 4.1165 | 530  | 1.6836          | 0.8054   | 0.7878 | 0.8239    | 0.7967 |
+| 2.592         | 4.1942 | 540  | 1.6770          | 0.8005   | 0.7826 | 0.8220    | 0.7912 |
+| 2.5698        | 4.2718 | 550  | 1.6184          | 0.8056   | 0.7881 | 0.8268    | 0.7970 |
+| 2.52          | 4.3495 | 560  | 1.6368          | 0.8064   | 0.7898 | 0.8267    | 0.7975 |
+| 2.5317        | 4.4272 | 570  | 1.5952          | 0.8059   | 0.7891 | 0.8289    | 0.7972 |
+| 2.4199        | 4.5049 | 580  | 1.5518          | 0.8163   | 0.8002 | 0.8337    | 0.8082 |
+| 2.4357        | 4.5825 | 590  | 1.5375          | 0.8095   | 0.7933 | 0.8263    | 0.8012 |
+| 2.4217        | 4.6602 | 600  | 1.4994          | 0.8127   | 0.7964 | 0.8297    | 0.8042 |
+| 2.428         | 4.7379 | 610  | 1.4671          | 0.8156   | 0.8003 | 0.8309    | 0.8074 |
+| 2.3725        | 4.8155 | 620  | 1.4402          | 0.8141   | 0.7973 | 0.8295    | 0.8054 |
+| 2.3594        | 4.8932 | 630  | 1.4566          | 0.8134   | 0.7976 | 0.8287    | 0.8049 |
+| 2.3279        | 4.9709 | 640  | 1.4359          | 0.8183   | 0.8034 | 0.8314    | 0.8100 |
+| 2.3166        | 5.0485 | 650  | 1.4067          | 0.8226   | 0.8086 | 0.8343    | 0.8149 |
+| 2.3062        | 5.1262 | 660  | 1.3913          | 0.8212   | 0.8072 | 0.8340    | 0.8131 |
+| 2.3096        | 5.2039 | 670  | 1.3577          | 0.8241   | 0.8107 | 0.8373    | 0.8159 |
+| 2.2514        | 5.2816 | 680  | 1.3574          | 0.8270   | 0.8136 | 0.8371    | 0.8193 |
+| 2.2053        | 5.3592 | 690  | 1.3450          | 0.8239   | 0.8101 | 0.8370    | 0.8164 |
+| 2.2347        | 5.4369 | 700  | 1.3331          | 0.8270   | 0.8137 | 0.8388    | 0.8194 |
+| 2.215         | 5.5146 | 710  | 1.2902          | 0.8294   | 0.8154 | 0.8419    | 0.8219 |
+| 2.175         | 5.5922 | 720  | 1.2861          | 0.8256   | 0.8114 | 0.8388    | 0.8181 |
+| 2.2212        | 5.6699 | 730  | 1.2637          | 0.8321   | 0.8180 | 0.8440    | 0.8241 |
+| 2.1459        | 5.7476 | 740  | 1.2827          | 0.8302   | 0.8166 | 0.8396    | 0.8227 |
+| 2.1615        | 5.8252 | 750  | 1.2800          | 0.8311   | 0.8184 | 0.8496    | 0.8239 |
+| 2.0966        | 5.9029 | 760  | 1.2742          | 0.8326   | 0.8195 | 0.8418    | 0.8251 |
+| 2.1314        | 5.9806 | 770  | 1.2464          | 0.8316   | 0.8184 | 0.8407    | 0.8238 |
+| 2.0846        | 6.0583 | 780  | 1.2409          | 0.8326   | 0.8189 | 0.8414    | 0.8250 |
+| 2.0522        | 6.1359 | 790  | 1.2023          | 0.8365   | 0.8233 | 0.8455    | 0.8292 |
+| 2.0724        | 6.2136 | 800  | 1.2252          | 0.8309   | 0.8174 | 0.8396    | 0.8235 |
+| 2.0848        | 6.2913 | 810  | 1.2025          | 0.8321   | 0.8186 | 0.8424    | 0.8248 |
+| 2.0402        | 6.3689 | 820  | 1.2130          | 0.8333   | 0.8189 | 0.8428    | 0.8255 |
+| 2.0778        | 6.4466 | 830  | 1.1809          | 0.8375   | 0.8249 | 0.8532    | 0.8302 |
+| 2.0963        | 6.5243 | 840  | 1.1696          | 0.8365   | 0.8231 | 0.8527    | 0.8289 |
+| 2.0576        | 6.6019 | 850  | 1.1866          | 0.8321   | 0.8181 | 0.8411    | 0.8245 |
+| 2.0386        | 6.6796 | 860  | 1.1882          | 0.8302   | 0.8160 | 0.8389    | 0.8227 |
+| 2.0084        | 6.7573 | 870  | 1.1696          | 0.8372   | 0.8244 | 0.8446    | 0.8301 |
+| 2.0571        | 6.8350 | 880  | 1.1622          | 0.8353   | 0.8217 | 0.8437    | 0.8280 |
+| 2.0264        | 6.9126 | 890  | 1.1640          | 0.8336   | 0.8204 | 0.8429    | 0.8263 |
+| 2.0077        | 6.9903 | 900  | 1.1673          | 0.8367   | 0.8241 | 0.8447    | 0.8295 |
+| 2.0492        | 7.0680 | 910  | 1.1455          | 0.8404   | 0.8269 | 0.8462    | 0.8330 |
+| 1.9973        | 7.1456 | 920  | 1.1538          | 0.8379   | 0.8250 | 0.8455    | 0.8307 |
+| 1.9961        | 7.2233 | 930  | 1.1502          | 0.8367   | 0.8236 | 0.8415    | 0.8295 |
+| 1.9681        | 7.3010 | 940  | 1.1657          | 0.8384   | 0.8254 | 0.8463    | 0.8311 |
+| 2.0188        | 7.3786 | 950  | 1.1309          | 0.8379   | 0.8252 | 0.8445    | 0.8310 |
+| 2.0225        | 7.4563 | 960  | 1.1547          | 0.8367   | 0.8231 | 0.8446    | 0.8294 |
+| 1.9562        | 7.5340 | 970  | 1.1474          | 0.8377   | 0.8243 | 0.8457    | 0.8305 |
+| 2.0247        | 7.6117 | 980  | 1.1251          | 0.8365   | 0.8241 | 0.8449    | 0.8294 |
+| 1.9355        | 7.6893 | 990  | 1.1349          | 0.8397   | 0.8276 | 0.8532    | 0.8329 |
+| 1.9804        | 7.7670 | 1000 | 1.1545          | 0.8387   | 0.8260 | 0.8457    | 0.8315 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,285 @@
+{
+  "_name_or_path": "microsoft/resnet-50",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsample_in_bottleneck": false,
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "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"
+  },
+  "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_type": "bottleneck",
+  "model_type": "resnet",
+  "num_channels": 3,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "problem_type": "single_label_classification",
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "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=false#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:ac8d97ad215b4ae316fdf257126ee3fd0726b047c176129bcc2d16eecd327bb2
+size 95270232

preprocessor_config.json

@@ -0,0 +1,37 @@
+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "crop_pct",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "crop_pct": 0.875,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ConvNextImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

train.ipynb

@@ -0,0 +1,926 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: uv in /Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages (0.1.42)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install uv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2mAudited \u001b[1m12 packages\u001b[0m in 8ms\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!uv pip install dagshub setuptools accelerate toml torch torchvision transformers mlflow datasets ipywidgets python-dotenv evaluate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Initialized MLflow to track repo <span style=\"color: #008000; text-decoration-color: #008000\">\"amaye15/CanineNet\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Initialized MLflow to track repo \u001b[32m\"amaye15/CanineNet\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Repository amaye15/CanineNet initialized!\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Repository amaye15/CanineNet initialized!\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import toml\n",
+    "import torch\n",
+    "import mlflow\n",
+    "import dagshub\n",
+    "import datasets\n",
+    "import evaluate\n",
+    "from dotenv import load_dotenv\n",
+    "from torchvision.transforms import v2\n",
+    "from transformers import AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer\n",
+    "\n",
+    "ENV_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/.env\"\n",
+    "CONFIG_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/code/config.toml\"\n",
+    "CONFIG = toml.load(CONFIG_PATH)\n",
+    "\n",
+    "load_dotenv(ENV_PATH)\n",
+    "\n",
+    "dagshub.init(repo_name=os.environ['MLFLOW_TRACKING_PROJECTNAME'], repo_owner=os.environ['MLFLOW_TRACKING_USERNAME'], mlflow=True, dvc=True)\n",
+    "\n",
+    "os.environ['MLFLOW_TRACKING_USERNAME'] = \"amaye15\"\n",
+    "\n",
+    "mlflow.set_tracking_uri(f'https://dagshub.com/' + os.environ['MLFLOW_TRACKING_USERNAME']\n",
+    "                         + '/' + os.environ['MLFLOW_TRACKING_PROJECTNAME'] + '.mlflow')\n",
+    "\n",
+    "CREATE_DATASET = True\n",
+    "ORIGINAL_DATASET = \"Alanox/stanford-dogs\"\n",
+    "MODIFIED_DATASET = \"amaye15/stanford-dogs\"\n",
+    "REMOVE_COLUMNS = [\"name\", \"annotations\"]\n",
+    "RENAME_COLUMNS = {\"image\":\"pixel_values\", \"target\":\"label\"}\n",
+    "SPLIT = 0.2\n",
+    "\n",
+    "METRICS = [\"accuracy\", \"f1\", \"precision\", \"recall\"]\n",
+    "# MODELS = 'google/vit-base-patch16-224'\n",
+    "# MODELS = \"google/siglip-base-patch16-224\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Affenpinscher: 0\n",
+      "Afghan Hound: 1\n",
+      "African Hunting Dog: 2\n",
+      "Airedale: 3\n",
+      "American Staffordshire Terrier: 4\n",
+      "Appenzeller: 5\n",
+      "Australian Terrier: 6\n",
+      "Basenji: 7\n",
+      "Basset: 8\n",
+      "Beagle: 9\n",
+      "Bedlington Terrier: 10\n",
+      "Bernese Mountain Dog: 11\n",
+      "Black And Tan Coonhound: 12\n",
+      "Blenheim Spaniel: 13\n",
+      "Bloodhound: 14\n",
+      "Bluetick: 15\n",
+      "Border Collie: 16\n",
+      "Border Terrier: 17\n",
+      "Borzoi: 18\n",
+      "Boston Bull: 19\n",
+      "Bouvier Des Flandres: 20\n",
+      "Boxer: 21\n",
+      "Brabancon Griffon: 22\n",
+      "Briard: 23\n",
+      "Brittany Spaniel: 24\n",
+      "Bull Mastiff: 25\n",
+      "Cairn: 26\n",
+      "Cardigan: 27\n",
+      "Chesapeake Bay Retriever: 28\n",
+      "Chihuahua: 29\n",
+      "Chow: 30\n",
+      "Clumber: 31\n",
+      "Cocker Spaniel: 32\n",
+      "Collie: 33\n",
+      "Curly Coated Retriever: 34\n",
+      "Dandie Dinmont: 35\n",
+      "Dhole: 36\n",
+      "Dingo: 37\n",
+      "Doberman: 38\n",
+      "English Foxhound: 39\n",
+      "English Setter: 40\n",
+      "English Springer: 41\n",
+      "Entlebucher: 42\n",
+      "Eskimo Dog: 43\n",
+      "Flat Coated Retriever: 44\n",
+      "French Bulldog: 45\n",
+      "German Shepherd: 46\n",
+      "German Short Haired Pointer: 47\n",
+      "Giant Schnauzer: 48\n",
+      "Golden Retriever: 49\n",
+      "Gordon Setter: 50\n",
+      "Great Dane: 51\n",
+      "Great Pyrenees: 52\n",
+      "Greater Swiss Mountain Dog: 53\n",
+      "Groenendael: 54\n",
+      "Ibizan Hound: 55\n",
+      "Irish Setter: 56\n",
+      "Irish Terrier: 57\n",
+      "Irish Water Spaniel: 58\n",
+      "Irish Wolfhound: 59\n",
+      "Italian Greyhound: 60\n",
+      "Japanese Spaniel: 61\n",
+      "Keeshond: 62\n",
+      "Kelpie: 63\n",
+      "Kerry Blue Terrier: 64\n",
+      "Komondor: 65\n",
+      "Kuvasz: 66\n",
+      "Labrador Retriever: 67\n",
+      "Lakeland Terrier: 68\n",
+      "Leonberg: 69\n",
+      "Lhasa: 70\n",
+      "Malamute: 71\n",
+      "Malinois: 72\n",
+      "Maltese Dog: 73\n",
+      "Mexican Hairless: 74\n",
+      "Miniature Pinscher: 75\n",
+      "Miniature Poodle: 76\n",
+      "Miniature Schnauzer: 77\n",
+      "Newfoundland: 78\n",
+      "Norfolk Terrier: 79\n",
+      "Norwegian Elkhound: 80\n",
+      "Norwich Terrier: 81\n",
+      "Old English Sheepdog: 82\n",
+      "Otterhound: 83\n",
+      "Papillon: 84\n",
+      "Pekinese: 85\n",
+      "Pembroke: 86\n",
+      "Pomeranian: 87\n",
+      "Pug: 88\n",
+      "Redbone: 89\n",
+      "Rhodesian Ridgeback: 90\n",
+      "Rottweiler: 91\n",
+      "Saint Bernard: 92\n",
+      "Saluki: 93\n",
+      "Samoyed: 94\n",
+      "Schipperke: 95\n",
+      "Scotch Terrier: 96\n",
+      "Scottish Deerhound: 97\n",
+      "Sealyham Terrier: 98\n",
+      "Shetland Sheepdog: 99\n",
+      "Shih Tzu: 100\n",
+      "Siberian Husky: 101\n",
+      "Silky Terrier: 102\n",
+      "Soft Coated Wheaten Terrier: 103\n",
+      "Staffordshire Bullterrier: 104\n",
+      "Standard Poodle: 105\n",
+      "Standard Schnauzer: 106\n",
+      "Sussex Spaniel: 107\n",
+      "Tibetan Mastiff: 108\n",
+      "Tibetan Terrier: 109\n",
+      "Toy Poodle: 110\n",
+      "Toy Terrier: 111\n",
+      "Vizsla: 112\n",
+      "Walker Hound: 113\n",
+      "Weimaraner: 114\n",
+      "Welsh Springer Spaniel: 115\n",
+      "West Highland White Terrier: 116\n",
+      "Whippet: 117\n",
+      "Wire Haired Fox Terrier: 118\n",
+      "Yorkshire Terrier: 119\n"
+     ]
+    }
+   ],
+   "source": [
+    "if CREATE_DATASET:\n",
+    "    ds = datasets.load_dataset(ORIGINAL_DATASET, token=os.getenv(\"HF_TOKEN\"), split=\"full\", trust_remote_code=True)\n",
+    "    ds = ds.remove_columns(REMOVE_COLUMNS).rename_columns(RENAME_COLUMNS)\n",
+    "\n",
+    "    labels = ds.select_columns(\"label\").to_pandas().sort_values(\"label\").get(\"label\").unique().tolist()\n",
+    "    numbers = range(len(labels))\n",
+    "    label2int = dict(zip(labels, numbers))\n",
+    "    int2label = dict(zip(numbers, labels))\n",
+    "\n",
+    "    for key, val in label2int.items():\n",
+    "        print(f\"{key}: {val}\")\n",
+    "\n",
+    "    ds = ds.class_encode_column(\"label\")\n",
+    "    ds = ds.align_labels_with_mapping(label2int, \"label\")\n",
+    "\n",
+    "    ds = ds.train_test_split(test_size=SPLIT, stratify_by_column = \"label\")\n",
+    "    #ds.push_to_hub(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"))\n",
+    "\n",
+    "    CONFIG[\"label2int\"] = str(label2int)\n",
+    "    CONFIG[\"int2label\"] = str(int2label)\n",
+    "\n",
+    "    # with open(\"output.toml\", \"w\") as toml_file:\n",
+    "    #     toml.dump(toml.dumps(CONFIG), toml_file)\n",
+    "\n",
+    "    #ds = datasets.load_dataset(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"), trust_remote_code=True, streaming=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration. Please open a PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of `feature_extractor_type`. This warning will be removed in v4.40.\n",
+      "Some weights of ResNetForImageClassification were not initialized from the model checkpoint at microsoft/resnet-50 and are newly initialized because the shapes did not match:\n",
+      "- classifier.1.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([120]) in the model instantiated\n",
+      "- classifier.1.weight: found shape torch.Size([1000, 2048]) in the checkpoint and torch.Size([120, 2048]) in the model instantiated\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "max_steps is given, it will override any value given in num_train_epochs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "037a86c2839440679fcff5595079beac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 4.7829, 'grad_norm': 0.6043907999992371, 'learning_rate': 4.9500000000000004e-05, 'epoch': 0.08}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1f85d03fadd4763a741fec5e5285d2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.77471923828125, 'eval_accuracy': 0.2118561710398445, 'eval_f1': 0.187375517726323, 'eval_precision': 0.3919036860239945, 'eval_recall': 0.19824327355121704, 'eval_runtime': 33.4309, 'eval_samples_per_second': 123.12, 'eval_steps_per_second': 3.859, 'epoch': 0.08}\n",
+      "{'loss': 4.7714, 'grad_norm': 0.6754865050315857, 'learning_rate': 4.9e-05, 'epoch': 0.16}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "268749f0d84a4fde98822b2ce1eead4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.757228851318359, 'eval_accuracy': 0.20383867832847424, 'eval_f1': 0.18416981866925827, 'eval_precision': 0.42618136770448983, 'eval_recall': 0.18363998800713158, 'eval_runtime': 30.7622, 'eval_samples_per_second': 133.801, 'eval_steps_per_second': 4.193, 'epoch': 0.16}\n",
+      "{'loss': 4.7606, 'grad_norm': 0.6417286992073059, 'learning_rate': 4.85e-05, 'epoch': 0.23}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "907e888ac52d4fe18fb87cc1cfe2b22f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.736657619476318, 'eval_accuracy': 0.358600583090379, 'eval_f1': 0.3433113409864575, 'eval_precision': 0.6517178219942168, 'eval_recall': 0.3306848427836897, 'eval_runtime': 29.5337, 'eval_samples_per_second': 139.366, 'eval_steps_per_second': 4.368, 'epoch': 0.23}\n",
+      "{'loss': 4.747, 'grad_norm': 0.6243997812271118, 'learning_rate': 4.8e-05, 'epoch': 0.31}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2c7266f55814c86badb55ec9f3c12cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.714941501617432, 'eval_accuracy': 0.4302721088435374, 'eval_f1': 0.42721541071711805, 'eval_precision': 0.773414620851018, 'eval_recall': 0.40385862239180403, 'eval_runtime': 31.0076, 'eval_samples_per_second': 132.742, 'eval_steps_per_second': 4.16, 'epoch': 0.31}\n",
+      "{'loss': 4.7253, 'grad_norm': 0.6433669328689575, 'learning_rate': 4.75e-05, 'epoch': 0.39}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a6ab27c180d4d68925da71fb6d734d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.684640884399414, 'eval_accuracy': 0.4361030126336249, 'eval_f1': 0.4677602303574034, 'eval_precision': 0.7906333558807621, 'eval_recall': 0.4160270614713831, 'eval_runtime': 31.2777, 'eval_samples_per_second': 131.595, 'eval_steps_per_second': 4.124, 'epoch': 0.39}\n",
+      "{'loss': 4.7069, 'grad_norm': 0.7207397818565369, 'learning_rate': 4.7e-05, 'epoch': 0.47}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff7ef69b883549f988fda71b35cb9ef1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.653403282165527, 'eval_accuracy': 0.533041788143829, 'eval_f1': 0.5396864056951644, 'eval_precision': 0.804847761263092, 'eval_recall': 0.5092981237432466, 'eval_runtime': 28.6761, 'eval_samples_per_second': 143.534, 'eval_steps_per_second': 4.499, 'epoch': 0.47}\n",
+      "{'loss': 4.6857, 'grad_norm': 0.7303667068481445, 'learning_rate': 4.6500000000000005e-05, 'epoch': 0.54}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c293ebbb0aec4666b6ad0d17517e2a29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.617745399475098, 'eval_accuracy': 0.5500485908649174, 'eval_f1': 0.5511369002526866, 'eval_precision': 0.7998093864476505, 'eval_recall': 0.5263615811202424, 'eval_runtime': 28.5304, 'eval_samples_per_second': 144.267, 'eval_steps_per_second': 4.521, 'epoch': 0.54}\n",
+      "{'loss': 4.6569, 'grad_norm': 0.744701623916626, 'learning_rate': 4.600000000000001e-05, 'epoch': 0.62}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53ce3795c47f4dd18f0f926d1532fb9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.576382160186768, 'eval_accuracy': 0.5738581146744413, 'eval_f1': 0.5800354551041117, 'eval_precision': 0.8207891649420048, 'eval_recall': 0.5516830965289926, 'eval_runtime': 28.9367, 'eval_samples_per_second': 142.241, 'eval_steps_per_second': 4.458, 'epoch': 0.62}\n",
+      "{'loss': 4.6293, 'grad_norm': 0.8225492238998413, 'learning_rate': 4.55e-05, 'epoch': 0.7}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "388b626051df4cde8bfacb4f10f53706",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.535852432250977, 'eval_accuracy': 0.6141885325558795, 'eval_f1': 0.6148517759248673, 'eval_precision': 0.807489842077252, 'eval_recall': 0.5926437581767611, 'eval_runtime': 29.8128, 'eval_samples_per_second': 138.062, 'eval_steps_per_second': 4.327, 'epoch': 0.7}\n",
+      "{'loss': 4.5953, 'grad_norm': 0.835442066192627, 'learning_rate': 4.5e-05, 'epoch': 0.78}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0969f9351eae4472814238ff34b3a129",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.482782363891602, 'eval_accuracy': 0.6207482993197279, 'eval_f1': 0.6233347480319061, 'eval_precision': 0.8108960881073339, 'eval_recall': 0.5999664720807305, 'eval_runtime': 30.0674, 'eval_samples_per_second': 136.893, 'eval_steps_per_second': 4.29, 'epoch': 0.78}\n",
+      "{'loss': 4.5651, 'grad_norm': 0.8578382134437561, 'learning_rate': 4.4500000000000004e-05, 'epoch': 0.85}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30b40f923e894e87b98cd86c2fe885e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.425670146942139, 'eval_accuracy': 0.6591350826044704, 'eval_f1': 0.6584699253003153, 'eval_precision': 0.8147592711787498, 'eval_recall': 0.6393439002306762, 'eval_runtime': 28.4304, 'eval_samples_per_second': 144.775, 'eval_steps_per_second': 4.537, 'epoch': 0.85}\n",
+      "{'loss': 4.5296, 'grad_norm': 0.9620392322540283, 'learning_rate': 4.4000000000000006e-05, 'epoch': 0.93}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a2e4094efc9460e8b0720746df15da2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.364680290222168, 'eval_accuracy': 0.706268221574344, 'eval_f1': 0.7012054635300039, 'eval_precision': 0.8284350125904834, 'eval_recall': 0.688199507444556, 'eval_runtime': 28.5471, 'eval_samples_per_second': 144.183, 'eval_steps_per_second': 4.519, 'epoch': 0.93}\n",
+      "{'loss': 4.4911, 'grad_norm': 0.9173192977905273, 'learning_rate': 4.35e-05, 'epoch': 1.01}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18ea4e46851043e9bd175bd132b7ed0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.299846649169922, 'eval_accuracy': 0.7089407191448007, 'eval_f1': 0.7073568856764126, 'eval_precision': 0.8325596625698185, 'eval_recall': 0.6924090542708233, 'eval_runtime': 28.5965, 'eval_samples_per_second': 143.934, 'eval_steps_per_second': 4.511, 'epoch': 1.01}\n",
+      "{'loss': 4.4442, 'grad_norm': 0.9183776378631592, 'learning_rate': 4.3e-05, 'epoch': 1.09}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f77e01f5212d46f49e8970bae34705be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.228794574737549, 'eval_accuracy': 0.6938775510204082, 'eval_f1': 0.6890499178440211, 'eval_precision': 0.8302365826885487, 'eval_recall': 0.6758939664483897, 'eval_runtime': 28.7618, 'eval_samples_per_second': 143.106, 'eval_steps_per_second': 4.485, 'epoch': 1.09}\n",
+      "{'loss': 4.3912, 'grad_norm': 1.0323781967163086, 'learning_rate': 4.25e-05, 'epoch': 1.17}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12d209218a594953b4abfaf14de05b8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/129 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.152723789215088, 'eval_accuracy': 0.6873177842565598, 'eval_f1': 0.6863011851876918, 'eval_precision': 0.8261897457310591, 'eval_recall': 0.6702606718880093, 'eval_runtime': 29.7578, 'eval_samples_per_second': 138.317, 'eval_steps_per_second': 4.335, 'epoch': 1.17}\n"
+     ]
+    }
+   ],
+   "source": [
+    "metrics = {metric: evaluate.load(metric) for metric in METRICS}\n",
+    "\n",
+    "\n",
+    "# for lr in [5e-3, 5e-4, 5e-5]: # 5e-5\n",
+    "#     for batch in [64]: # 32\n",
+    "#         for model_name in [\"google/vit-base-patch16-224\", \"microsoft/swinv2-base-patch4-window16-256\", \"google/siglip-base-patch16-224\"]: # \"facebook/dinov2-base\"\n",
+    "\n",
+    "lr = 5e-4\n",
+    "batch = 32\n",
+    "model_name = \"microsoft/resnet-50\"\n",
+    "\n",
+    "image_processor = AutoImageProcessor.from_pretrained(model_name)\n",
+    "model = AutoModelForImageClassification.from_pretrained(\n",
+    "model_name,\n",
+    "num_labels=len(label2int),\n",
+    "id2label=int2label,\n",
+    "label2id=label2int,\n",
+    "ignore_mismatched_sizes=True,\n",
+    ")\n",
+    "\n",
+    "# Then, in your transformations:\n",
+    "def train_transform(examples, num_ops=10, magnitude=9, num_magnitude_bins=31):\n",
+    "\n",
+    "    transformation = v2.Compose(\n",
+    "        [\n",
+    "            v2.RandAugment(\n",
+    "                num_ops=num_ops,\n",
+    "                magnitude=magnitude,\n",
+    "                num_magnitude_bins=num_magnitude_bins,\n",
+    "            )\n",
+    "        ]\n",
+    "    )\n",
+    "    # Ensure each image has three dimensions (in this case, ensure it's RGB)\n",
+    "    examples[\"pixel_values\"] = [\n",
+    "        image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
+    "    ]\n",
+    "    # Apply transformations\n",
+    "    examples[\"pixel_values\"] = [\n",
+    "        image_processor(transformation(image), return_tensors=\"pt\")[\n",
+    "            \"pixel_values\"\n",
+    "        ].squeeze()\n",
+    "        for image in examples[\"pixel_values\"]\n",
+    "    ]\n",
+    "    return examples\n",
+    "\n",
+    "\n",
+    "def test_transform(examples):\n",
+    "    # Ensure each image is RGB\n",
+    "    examples[\"pixel_values\"] = [\n",
+    "        image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
+    "    ]\n",
+    "    # Apply processing\n",
+    "    examples[\"pixel_values\"] = [\n",
+    "        image_processor(image, return_tensors=\"pt\")[\"pixel_values\"].squeeze()\n",
+    "        for image in examples[\"pixel_values\"]\n",
+    "    ]\n",
+    "    return examples\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    predictions, labels = eval_pred\n",
+    "    # predictions = np.argmax(logits, axis=-1)\n",
+    "    results = {}\n",
+    "    for key, val in metrics.items():\n",
+    "        if \"accuracy\" == key:\n",
+    "            result = next(\n",
+    "                iter(val.compute(predictions=predictions, references=labels).items())\n",
+    "            )\n",
+    "        if \"accuracy\" != key:\n",
+    "            result = next(\n",
+    "                iter(\n",
+    "                    val.compute(\n",
+    "                        predictions=predictions, references=labels, average=\"macro\"\n",
+    "                    ).items()\n",
+    "                )\n",
+    "            )\n",
+    "        results[result[0]] = result[1]\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def collate_fn(examples):\n",
+    "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
+    "    labels = torch.tensor([example[\"label\"] for example in examples])\n",
+    "    return {\"pixel_values\": pixel_values, \"labels\": labels}\n",
+    "\n",
+    "\n",
+    "def preprocess_logits_for_metrics(logits, labels):\n",
+    "    \"\"\"\n",
+    "    Original Trainer may have a memory leak.\n",
+    "    This is a workaround to avoid storing too many tensors that are not needed.\n",
+    "    \"\"\"\n",
+    "    pred_ids = torch.argmax(logits, dim=-1)\n",
+    "    return pred_ids\n",
+    "\n",
+    "ds[\"train\"].set_transform(train_transform)\n",
+    "ds[\"test\"].set_transform(test_transform)\n",
+    "\n",
+    "training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
+    "training_args.per_device_train_batch_size = batch\n",
+    "training_args.per_device_eval_batch_size = batch\n",
+    "training_args.hub_model_id = f\"amaye15/{model_name.replace('/','-')}-batch{batch}-lr{lr}-standford-dogs\"\n",
+    "\n",
+    "mlflow.start_run(run_name=f\"{model_name.replace('/','-')}-batch{batch}-lr{lr}\")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    "    tokenizer=image_processor,\n",
+    "    data_collator=collate_fn,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    # callbacks=[early_stopping_callback],\n",
+    "    preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n",
+    ")\n",
+    "\n",
+    "# Train the model\n",
+    "trainer.train()\n",
+    "\n",
+    "trainer.push_to_hub()\n",
+    "\n",
+    "mlflow.end_run()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training_args.bin

@@ -0,0 +1,3 @@
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+oid sha256:34687c9f8264a2f41d3953440fb5f2f2b6c7f894111aeed6b3a7f30826b01fb2
+size 5112