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# 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

affenpinscher afghan hound african hunting dog airedale american staffordshire terrier appenzeller australian terrier basenji basset beagle bedlington terrier bernese mountain dog black and tan coonhound blenheim spaniel bloodhound bluetick border collie border terrier borzoi boston bull bouvier des flandres boxer brabancon griffon briard brittany spaniel bull mastiff cairn cardigan chesapeake bay retriever chihuahua chow clumber cocker spaniel collie curly coated retriever dandie dinmont dhole dingo doberman english foxhound english setter english springer entlebucher eskimo dog flat coated retriever french bulldog german shepherd german short haired pointer giant schnauzer golden retriever gordon setter great dane great pyrenees greater swiss mountain dog groenendael ibizan hound irish setter irish terrier irish water spaniel irish wolfhound italian greyhound japanese spaniel keeshond kelpie kerry blue terrier komondor kuvasz labrador retriever lakeland terrier leonberg lhasa malamute malinois maltese dog mexican hairless miniature pinscher miniature poodle miniature schnauzer newfoundland norfolk terrier norwegian elkhound norwich terrier old english sheepdog otterhound papillon pekinese pembroke pomeranian pug redbone rhodesian ridgeback rottweiler saint bernard saluki samoyed schipperke scotch terrier scottish deerhound sealyham terrier shetland sheepdog shih tzu siberian husky silky terrier soft coated wheaten terrier staffordshire bullterrier standard poodle standard schnauzer sussex spaniel tibetan mastiff tibetan terrier toy poodle toy terrier vizsla walker hound weimaraner welsh springer spaniel west highland white terrier whippet wire haired fox terrier yorkshire terrier