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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_1-Node_mGyj2hi6
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_XydcPvT5
|
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[
"label_0",
"label_1",
"label_2",
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"label_4",
"label_5",
"label_6",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_3LwPsiwV
|
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[
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_ZemwcrUG
|
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[
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"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
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"label_13",
"label_14",
"label_15"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_MEY7Trs5
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_yjivEgny
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_jEgCc229
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_trNku9wa
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_7hXxcKTc
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_1-Node_LqzWTHtd
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_EZ6KezwF
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_62JB6Tw6
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_PDCcfhoH
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_XfBLFtro
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_1-Node_oRu8YWaE
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_ixB9y6eu
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_YYY3hYbS
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_wRJEzqKW
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_0-Depth_2-Node_MNwpKQQL
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_78rUJxRS
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_ZQejZsZn
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"label_12",
"label_13",
"label_14",
"label_15"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_C4tTrq6o
|
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[
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"label_1",
"label_2",
"label_3",
"label_4",
"label_5"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_Hihw9muQ
|
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[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_63g6pEt9
|
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[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_CJU9ykrK
|
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|
[
"label_0",
"label_1",
"label_2",
"label_3"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_2jDdcs5Y
|
# Model Card for Model ID
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|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
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"label_10",
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"label_52",
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"label_54",
"label_55",
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"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
"label_92",
"label_93",
"label_94",
"label_95",
"label_96",
"label_97",
"label_98",
"label_99"
] |
ahmedesmail16/Train-Augmentation-V2-swinv2-base
|
<!-- 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. -->
# Train-Augmentation-V2-swinv2-base
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9822
- Accuracy: 0.8459
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5894 | 0.99 | 109 | 0.7123 | 0.7481 |
| 0.2772 | 2.0 | 219 | 0.6394 | 0.7970 |
| 0.1863 | 3.0 | 329 | 0.7819 | 0.7669 |
| 0.0925 | 4.0 | 439 | 0.7062 | 0.8083 |
| 0.0461 | 4.99 | 548 | 0.8637 | 0.8120 |
| 0.0427 | 6.0 | 658 | 0.9080 | 0.7970 |
| 0.043 | 7.0 | 768 | 1.0747 | 0.8045 |
| 0.0074 | 8.0 | 878 | 0.9019 | 0.8421 |
| 0.0169 | 8.99 | 987 | 0.9099 | 0.8459 |
| 0.015 | 10.0 | 1097 | 0.9512 | 0.8647 |
| 0.0022 | 11.0 | 1207 | 1.0051 | 0.8609 |
| 0.0081 | 12.0 | 1317 | 1.0061 | 0.8308 |
| 0.0013 | 12.99 | 1426 | 0.9844 | 0.8534 |
| 0.0037 | 14.0 | 1536 | 0.9864 | 0.8459 |
| 0.0002 | 14.9 | 1635 | 0.9822 | 0.8459 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.15.2
|
[
"erythrodermic",
"guttate",
"inverse",
"nail",
"normal",
"not define",
"palm soles",
"plaque",
"psoriatic arthritis",
"pustular",
"scalp",
"upnormal"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_sYrWUxjG
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_y6qpb4MQ
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_9w6g388s
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_N9NGjrzj
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_nxYM5ppP
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] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_YEFPBgYj
|
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[
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_1-Node_tQ5snMkx
|
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[
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_d8YhPn7N
|
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[
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_qyLV4o3Q
|
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"label_99"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_1-Depth_2-Node_YAXn6cPa
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"label_42",
"label_43",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_TSmWB7Ck
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_YjeJvMd4
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_tqxvVZeD
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_5LTfgyBw
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_1-Node_Wxu3bzKe
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_b5eViYep
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_c9K8GNmz
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_FzdCErZN
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_psSHZ8Rz
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_1-Node_rVz2hcDW
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[
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"label_2",
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] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_CDBnGFvW
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_SEEVHMjS
|
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[
"label_0",
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"label_2",
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"label_5",
"label_6",
"label_7",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_uW5P2d4L
|
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[
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_6jjrVrbh
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_1-Node_4GE54fBw
|
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[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_orgdoU4g
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_pXcKSLSH
|
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[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_ge3wsm7v
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_2-Depth_2-Node_CGVgaCAU
|
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"label_2",
"label_3",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_iR7cjYx9
|
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[
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"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
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] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_8wr8xj4H
|
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[
"label_0",
"label_1",
"label_2",
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"label_6",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_QPPtXj29
|
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[
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"label_2",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Gidbp5bi
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[
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"label_8"
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_1-Node_QVQtLHre
|
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"label_95",
"label_96",
"label_97",
"label_98",
"label_99"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Lz898uTP
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_N9XUxrSc
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_oCrjLjak
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_cuwcx9uf
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_1-Node_5gZucSFK
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_dP52EnQd
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[
"label_0",
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"label_6",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_poyiVesc
|
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_pgX4s4Ji
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"label_99"
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_1-Node_UA6rbsJi
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_rxjL5iGb
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_5b3biBdQ
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[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Wxr8na32
|
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"label_2",
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"label_7"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_3-Depth_2-Node_Ch5K29UH
|
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] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_tGeUP4bP
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_m29nu62T
|
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[
"label_0",
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"label_2",
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"label_101"
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MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_Yz2jCf4t
|
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17"
] |
MoTHer-VTHR/VTHR-LoRA-F-ModelTree_4-Depth_2-Node_HVUUpUar
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9"
] |
hchcsuim/batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation
|
<!-- 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. -->
# batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0442
- Accuracy: 0.9837
- Precision: 0.9831
- Recall: 0.9964
- F1: 0.9897
- Roc Auc: 0.9991
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0483 | 1.0 | 1377 | 0.0442 | 0.9837 | 0.9831 | 0.9964 | 0.9897 | 0.9991 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.3.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"fake",
"real"
] |
vuongnhathien/test-wrong-label
|
<!-- 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. -->
# test-wrong-label
This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on an unknown dataset.
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.9315 | 0.7625 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"aechmea_fasciata",
"agave_americana",
"agave_attenuata",
"agave_tequilana",
"aglaonema_commutatum",
"albuca_spiralis",
"allium_cepa",
"allium_sativum"
] |
mjun/tinyvit-musinsa-fashion-classification
|
<!-- 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. -->
# mjun/tinyvit-musinsa-fashion-classification
This model is a fine-tuned version of [timm/tiny_vit_5m_224.dist_in22k_ft_in1k](https://huggingface.co/timm/tiny_vit_5m_224.dist_in22k_ft_in1k) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5282
- Accuracy: 0.7588
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 269 | 0.7828 | 0.6390 |
| 0.881 | 2.0 | 538 | 0.7235 | 0.6702 |
| 0.881 | 3.0 | 807 | 0.5733 | 0.7353 |
| 0.5813 | 4.0 | 1076 | 0.5362 | 0.7519 |
| 0.5813 | 5.0 | 1345 | 0.5282 | 0.7588 |
### Framework versions
- Transformers 4.8.1
- Pytorch 1.13.1+cu117
- Datasets 2.7.1
- Tokenizers 0.10.3
|
[
"label_0",
"label_1",
"label_2",
"label_3"
] |
Mullerjo/food-101-finetuned-model
|
<!-- 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. -->
# food-101-finetuned-model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5578
- Accuracy: 0.8447
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8013 | 1.0 | 9469 | 0.5578 | 0.8447 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"6",
"79",
"81",
"53",
"10",
"20",
"77",
"48",
"86",
"84",
"76",
"34",
"51",
"21",
"64",
"0",
"43",
"44",
"73",
"57",
"14",
"5",
"46",
"55",
"93",
"98",
"38",
"11",
"99",
"72",
"22",
"59",
"70",
"16",
"2",
"58",
"83",
"96",
"39",
"49",
"45",
"88",
"9",
"26",
"94",
"4",
"65",
"32",
"27",
"36",
"87",
"69",
"85",
"25",
"40",
"19",
"35",
"56",
"42",
"60",
"68",
"100",
"41",
"92",
"24",
"3",
"89",
"75",
"17",
"97",
"61",
"33",
"80",
"30",
"8",
"74",
"66",
"31",
"18",
"67",
"37",
"13",
"63",
"28",
"47",
"52",
"54",
"1",
"82",
"91",
"95",
"7",
"29",
"78",
"15",
"23",
"12",
"62",
"50",
"71",
"90"
] |
th041/vit-weldclassify
|
<!-- 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. -->
# vit-weldclassify
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0639
- Accuracy: 0.8174
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8311 | 0.8130 | 100 | 0.9623 | 0.4886 |
| 0.6016 | 1.6260 | 200 | 0.5911 | 0.7215 |
| 0.2602 | 2.4390 | 300 | 1.0585 | 0.6393 |
| 0.1643 | 3.2520 | 400 | 0.9470 | 0.7169 |
| 0.3754 | 4.0650 | 500 | 0.6054 | 0.8082 |
| 0.1446 | 4.8780 | 600 | 0.6845 | 0.7854 |
| 0.138 | 5.6911 | 700 | 0.9011 | 0.7534 |
| 0.033 | 6.5041 | 800 | 0.8366 | 0.8128 |
| 0.0538 | 7.3171 | 900 | 0.9102 | 0.7854 |
| 0.0144 | 8.1301 | 1000 | 0.8510 | 0.8128 |
| 0.0459 | 8.9431 | 1100 | 0.8610 | 0.8219 |
| 0.0022 | 9.7561 | 1200 | 0.9398 | 0.8082 |
| 0.0019 | 10.5691 | 1300 | 0.8714 | 0.8356 |
| 0.0015 | 11.3821 | 1400 | 1.0001 | 0.8128 |
| 0.0013 | 12.1951 | 1500 | 0.9926 | 0.8219 |
| 0.0012 | 13.0081 | 1600 | 1.0175 | 0.8219 |
| 0.0011 | 13.8211 | 1700 | 1.0323 | 0.8219 |
| 0.001 | 14.6341 | 1800 | 1.0453 | 0.8174 |
| 0.0009 | 15.4472 | 1900 | 1.0518 | 0.8174 |
| 0.0009 | 16.2602 | 2000 | 1.0585 | 0.8174 |
| 0.0009 | 17.0732 | 2100 | 1.0623 | 0.8174 |
| 0.0009 | 17.8862 | 2200 | 1.0639 | 0.8174 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"ab",
"c",
"d"
] |
ahmedesmail16/Train-Test-Augmentation-V2-beit-large
|
<!-- 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. -->
# Train-Test-Augmentation-V2-beit-large
This model is a fine-tuned version of [ahmedesmail16/Train-Test-Augmentation-V2-beit-large](https://huggingface.co/ahmedesmail16/Train-Test-Augmentation-V2-beit-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7562
- Accuracy: 0.8519
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0753 | 1.0 | 55 | 0.5558 | 0.8519 |
| 0.0191 | 2.0 | 110 | 0.6652 | 0.8447 |
| 0.0062 | 3.0 | 165 | 0.5826 | 0.8582 |
| 0.0018 | 4.0 | 220 | 0.7664 | 0.8408 |
| 0.001 | 5.0 | 275 | 0.7562 | 0.8519 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.15.2
|
[
"erythrodermic",
"guttate",
"inverse",
"nail",
"normal",
"not define",
"palm soles",
"plaque",
"psoriatic arthritis",
"pustular",
"scalp",
"upnormal"
] |
th041/vit-weld-classify
|
<!-- 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. -->
# vit-weld-classify
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7966
- Accuracy: 0.6895
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8686 | 0.8130 | 100 | 0.7966 | 0.6895 |
| 0.6935 | 1.6260 | 200 | 1.2217 | 0.5068 |
| 0.4225 | 2.4390 | 300 | 0.9592 | 0.6210 |
| 0.2586 | 3.2520 | 400 | 1.3123 | 0.5936 |
| 0.237 | 4.0650 | 500 | 0.8075 | 0.6986 |
| 0.2658 | 4.8780 | 600 | 1.0878 | 0.6210 |
| 0.1904 | 5.6911 | 700 | 1.1048 | 0.7169 |
| 0.0964 | 6.5041 | 800 | 1.3602 | 0.6849 |
| 0.0474 | 7.3171 | 900 | 1.1331 | 0.7671 |
| 0.1179 | 8.1301 | 1000 | 1.1228 | 0.7306 |
| 0.0447 | 8.9431 | 1100 | 1.2609 | 0.7397 |
| 0.0043 | 9.7561 | 1200 | 1.1746 | 0.7763 |
| 0.1059 | 10.5691 | 1300 | 1.1867 | 0.7763 |
| 0.0026 | 11.3821 | 1400 | 1.2890 | 0.7534 |
| 0.0039 | 12.1951 | 1500 | 1.3283 | 0.7580 |
| 0.002 | 13.0081 | 1600 | 1.1871 | 0.7671 |
| 0.0019 | 13.8211 | 1700 | 1.1643 | 0.7900 |
| 0.0264 | 14.6341 | 1800 | 1.1537 | 0.7900 |
| 0.0015 | 15.4472 | 1900 | 1.1821 | 0.7945 |
| 0.0015 | 16.2602 | 2000 | 1.1962 | 0.7900 |
| 0.0014 | 17.0732 | 2100 | 1.2036 | 0.7900 |
| 0.0014 | 17.8862 | 2200 | 1.2067 | 0.7900 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"ab",
"c",
"d"
] |
BehradG/resnet-18-MRI-Brain
|
# Model Card for Model ID
## Training Details
### Training Data
https://huggingface.co/datasets/tanzuhuggingface/brainmri
### Training Procedure
The restnet18 model was fin-tuned with P100 GPU for 200 epochs. Both calibration and validation losses decined constantly during the fine-tuning showing no sign of overfitting.
The final accuracy was 97.9%.
|
[
"no",
"yes"
] |
c14kevincardenas/beit-large-patch16-384-limb-person-crop-8_5e-5_1e-4_0.05
|
<!-- 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. -->
# beit-large-patch16-384-limb-person-crop-8_5e-5_1e-4_0.05
This model is a fine-tuned version of [microsoft/beit-large-patch16-384](https://huggingface.co/microsoft/beit-large-patch16-384) on the c14kevincardenas/beta_caller_284_person_crop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7131
- Accuracy: 0.7678
## 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: 2014
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.05
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3649 | 1.0 | 214 | 1.5215 | 0.3640 |
| 1.1297 | 2.0 | 428 | 1.0014 | 0.6003 |
| 1.0881 | 3.0 | 642 | 0.9018 | 0.6559 |
| 1.0065 | 4.0 | 856 | 0.9688 | 0.5995 |
| 1.0028 | 5.0 | 1070 | 0.8240 | 0.7015 |
| 0.9225 | 6.0 | 1284 | 0.7355 | 0.7521 |
| 0.8522 | 7.0 | 1498 | 0.7693 | 0.7463 |
| 0.821 | 8.0 | 1712 | 0.7131 | 0.7678 |
| 0.735 | 9.0 | 1926 | 0.7316 | 0.7761 |
| 0.7123 | 10.0 | 2140 | 0.7301 | 0.7778 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"left_foot",
"left_hand",
"right_foot",
"right_hand"
] |
vsing8298/vit-base-patch16-224-finetuned-flower
|
<!-- 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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.3.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
amaye15/microsoft-resnet-50-batch32-lr0.0005-standford-dogs
|
<!-- 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
|
[
"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"
] |
Zweehn/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0729
- Accuracy: 0.9763
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2331 | 1.0 | 190 | 0.1341 | 0.9526 |
| 0.1949 | 2.0 | 380 | 0.0718 | 0.9763 |
| 0.1231 | 3.0 | 570 | 0.0729 | 0.9763 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
vuongnhathien/convnext-tiny-upgrade-384-batch-32
|
<!-- 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. -->
# convnext-tiny-upgrade-384-batch-32
This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2521
- Accuracy: 0.9298
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9343 | 1.0 | 550 | 0.5732 | 0.8410 |
| 0.6456 | 2.0 | 1100 | 0.4130 | 0.8843 |
| 0.5478 | 3.0 | 1650 | 0.3537 | 0.9026 |
| 0.466 | 4.0 | 2200 | 0.3012 | 0.9181 |
| 0.4619 | 5.0 | 2750 | 0.3031 | 0.9141 |
| 0.4046 | 6.0 | 3300 | 0.2971 | 0.9157 |
| 0.3852 | 7.0 | 3850 | 0.2763 | 0.9205 |
| 0.3346 | 8.0 | 4400 | 0.2712 | 0.9225 |
| 0.3386 | 9.0 | 4950 | 0.2672 | 0.9221 |
| 0.3462 | 10.0 | 5500 | 0.2655 | 0.9245 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"banh beo",
"banh bot loc",
"banh can",
"banh canh",
"banh chung",
"banh cuon",
"banh duc",
"banh gio",
"banh khot",
"banh mi",
"banh pia",
"banh tet",
"banh trang nuong",
"banh xeo",
"bun bo hue",
"bun dau mam tom",
"bun mam",
"bun rieu",
"bun thit nuong",
"ca kho to",
"canh chua",
"cao lau",
"chao long",
"com tam",
"goi cuon",
"hu tieu",
"mi quang",
"nem chua",
"pho",
"xoi xeo"
] |
vuongnhathien/convnext-tiny-upgrade-384-batch-16
|
<!-- 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. -->
# convnext-tiny-upgrade-384-batch-16
This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2389
- Accuracy: 0.9369
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.855 | 1.0 | 1099 | 0.4926 | 0.8577 |
| 0.6243 | 2.0 | 2198 | 0.3875 | 0.8911 |
| 0.4776 | 3.0 | 3297 | 0.3230 | 0.9125 |
| 0.4535 | 4.0 | 4396 | 0.2854 | 0.9205 |
| 0.4204 | 5.0 | 5495 | 0.2915 | 0.9169 |
| 0.3756 | 6.0 | 6594 | 0.2914 | 0.9193 |
| 0.3603 | 7.0 | 7693 | 0.2645 | 0.9272 |
| 0.2885 | 8.0 | 8792 | 0.2599 | 0.9280 |
| 0.2753 | 9.0 | 9891 | 0.2565 | 0.9292 |
| 0.2902 | 10.0 | 10990 | 0.2526 | 0.9292 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"banh beo",
"banh bot loc",
"banh can",
"banh canh",
"banh chung",
"banh cuon",
"banh duc",
"banh gio",
"banh khot",
"banh mi",
"banh pia",
"banh tet",
"banh trang nuong",
"banh xeo",
"bun bo hue",
"bun dau mam tom",
"bun mam",
"bun rieu",
"bun thit nuong",
"ca kho to",
"canh chua",
"cao lau",
"chao long",
"com tam",
"goi cuon",
"hu tieu",
"mi quang",
"nem chua",
"pho",
"xoi xeo"
] |
vuongnhathien/convnext-tiny-upgrade-1k-224-batch-32
|
<!-- 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. -->
# convnext-tiny-upgrade-1k-224-batch-32
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4027
- Accuracy: 0.8887
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5523 | 1.0 | 550 | 1.2083 | 0.7010 |
| 1.0852 | 2.0 | 1100 | 0.7955 | 0.7960 |
| 0.9179 | 3.0 | 1650 | 0.6425 | 0.8258 |
| 0.7621 | 4.0 | 2200 | 0.5426 | 0.8549 |
| 0.7506 | 5.0 | 2750 | 0.5018 | 0.8624 |
| 0.6774 | 6.0 | 3300 | 0.4792 | 0.8684 |
| 0.6364 | 7.0 | 3850 | 0.4526 | 0.8744 |
| 0.5961 | 8.0 | 4400 | 0.4362 | 0.8799 |
| 0.602 | 9.0 | 4950 | 0.4316 | 0.8827 |
| 0.5896 | 10.0 | 5500 | 0.4287 | 0.8851 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"banh beo",
"banh bot loc",
"banh can",
"banh canh",
"banh chung",
"banh cuon",
"banh duc",
"banh gio",
"banh khot",
"banh mi",
"banh pia",
"banh tet",
"banh trang nuong",
"banh xeo",
"bun bo hue",
"bun dau mam tom",
"bun mam",
"bun rieu",
"bun thit nuong",
"ca kho to",
"canh chua",
"cao lau",
"chao long",
"com tam",
"goi cuon",
"hu tieu",
"mi quang",
"nem chua",
"pho",
"xoi xeo"
] |
amaye15/microsoft-resnet-50-batch32-lr0.005-standford-dogs
|
<!-- 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-lr0.005-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: 1.1192
- Accuracy: 0.8256
- F1: 0.8098
- Precision: 0.8426
- Recall: 0.8178
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 4.7839 | 0.0777 | 10 | 4.7747 | 0.2556 | 0.2410 | 0.4479 | 0.2436 |
| 4.7731 | 0.1553 | 20 | 4.7576 | 0.3511 | 0.3282 | 0.6032 | 0.3338 |
| 4.7617 | 0.2330 | 30 | 4.7363 | 0.4184 | 0.3974 | 0.6668 | 0.3947 |
| 4.7445 | 0.3107 | 40 | 4.7115 | 0.5265 | 0.4927 | 0.7032 | 0.4993 |
| 4.7266 | 0.3883 | 50 | 4.6846 | 0.5561 | 0.5413 | 0.7422 | 0.5333 |
| 4.7081 | 0.4660 | 60 | 4.6547 | 0.6062 | 0.5767 | 0.7392 | 0.5828 |
| 4.6807 | 0.5437 | 70 | 4.6161 | 0.5909 | 0.5750 | 0.7740 | 0.5673 |
| 4.6572 | 0.6214 | 80 | 4.5761 | 0.6324 | 0.6162 | 0.8021 | 0.6102 |
| 4.6286 | 0.6990 | 90 | 4.5274 | 0.6297 | 0.6241 | 0.8188 | 0.6080 |
| 4.598 | 0.7767 | 100 | 4.4746 | 0.6569 | 0.6609 | 0.8380 | 0.6370 |
| 4.5578 | 0.8544 | 110 | 4.4193 | 0.6674 | 0.6713 | 0.8301 | 0.6486 |
| 4.521 | 0.9320 | 120 | 4.3553 | 0.6914 | 0.6868 | 0.8215 | 0.6729 |
| 4.4888 | 1.0097 | 130 | 4.2924 | 0.7082 | 0.7064 | 0.8415 | 0.6904 |
| 4.4312 | 1.0874 | 140 | 4.2125 | 0.7155 | 0.7076 | 0.8381 | 0.6980 |
| 4.3865 | 1.1650 | 150 | 4.1433 | 0.7145 | 0.7115 | 0.8315 | 0.6984 |
| 4.336 | 1.2427 | 160 | 4.0630 | 0.7082 | 0.7010 | 0.8353 | 0.6930 |
| 4.2903 | 1.3204 | 170 | 3.9781 | 0.7148 | 0.7024 | 0.8109 | 0.6982 |
| 4.2465 | 1.3981 | 180 | 3.8896 | 0.7376 | 0.7234 | 0.8328 | 0.7217 |
| 4.1924 | 1.4757 | 190 | 3.8117 | 0.7476 | 0.7310 | 0.8161 | 0.7322 |
| 4.1217 | 1.5534 | 200 | 3.7499 | 0.7510 | 0.7344 | 0.8105 | 0.7372 |
| 4.068 | 1.6311 | 210 | 3.6340 | 0.7551 | 0.7355 | 0.8183 | 0.7409 |
| 4.0148 | 1.7087 | 220 | 3.5678 | 0.7546 | 0.7358 | 0.8066 | 0.7413 |
| 3.9682 | 1.7864 | 230 | 3.4852 | 0.7663 | 0.7477 | 0.8145 | 0.7530 |
| 3.9196 | 1.8641 | 240 | 3.3841 | 0.7648 | 0.7464 | 0.8075 | 0.7520 |
| 3.8481 | 1.9417 | 250 | 3.3003 | 0.7626 | 0.7421 | 0.8056 | 0.7495 |
| 3.8017 | 2.0194 | 260 | 3.2395 | 0.7578 | 0.7370 | 0.8045 | 0.7461 |
| 3.7528 | 2.0971 | 270 | 3.1183 | 0.7578 | 0.7349 | 0.8007 | 0.7457 |
| 3.6614 | 2.1748 | 280 | 3.0364 | 0.7655 | 0.7435 | 0.8011 | 0.7531 |
| 3.6522 | 2.2524 | 290 | 2.9775 | 0.7629 | 0.7415 | 0.7990 | 0.7507 |
| 3.5922 | 2.3301 | 300 | 2.8995 | 0.7665 | 0.7466 | 0.8090 | 0.7551 |
| 3.519 | 2.4078 | 310 | 2.8049 | 0.7680 | 0.7488 | 0.8129 | 0.7566 |
| 3.4724 | 2.4854 | 320 | 2.7425 | 0.7704 | 0.7528 | 0.8170 | 0.7601 |
| 3.4333 | 2.5631 | 330 | 2.6444 | 0.7755 | 0.7560 | 0.8236 | 0.7648 |
| 3.4303 | 2.6408 | 340 | 2.5672 | 0.7687 | 0.7473 | 0.8178 | 0.7585 |
| 3.3287 | 2.7184 | 350 | 2.5194 | 0.7806 | 0.7599 | 0.8229 | 0.7712 |
| 3.2916 | 2.7961 | 360 | 2.4733 | 0.7796 | 0.7575 | 0.8223 | 0.7698 |
| 3.1999 | 2.8738 | 370 | 2.4098 | 0.7792 | 0.7565 | 0.8158 | 0.7692 |
| 3.211 | 2.9515 | 380 | 2.3081 | 0.7796 | 0.7571 | 0.8284 | 0.7692 |
| 3.1437 | 3.0291 | 390 | 2.2523 | 0.7830 | 0.7600 | 0.8212 | 0.7730 |
| 3.1036 | 3.1068 | 400 | 2.2000 | 0.7847 | 0.7619 | 0.8210 | 0.7740 |
| 3.0345 | 3.1845 | 410 | 2.1385 | 0.7833 | 0.7606 | 0.8261 | 0.7726 |
| 2.99 | 3.2621 | 420 | 2.1079 | 0.7799 | 0.7560 | 0.8199 | 0.7698 |
| 2.9386 | 3.3398 | 430 | 2.0585 | 0.7821 | 0.7584 | 0.8232 | 0.7716 |
| 2.9093 | 3.4175 | 440 | 2.0176 | 0.7823 | 0.7586 | 0.8225 | 0.7721 |
| 2.8868 | 3.4951 | 450 | 1.9702 | 0.7818 | 0.7585 | 0.8183 | 0.7720 |
| 2.8603 | 3.5728 | 460 | 1.8973 | 0.7864 | 0.7645 | 0.8241 | 0.7767 |
| 2.8232 | 3.6505 | 470 | 1.8814 | 0.7855 | 0.7616 | 0.8128 | 0.7758 |
| 2.7889 | 3.7282 | 480 | 1.8170 | 0.7886 | 0.7676 | 0.8214 | 0.7792 |
| 2.7561 | 3.8058 | 490 | 1.7750 | 0.7920 | 0.7721 | 0.8364 | 0.7828 |
| 2.7243 | 3.8835 | 500 | 1.7369 | 0.7906 | 0.7695 | 0.8295 | 0.7813 |
| 2.6619 | 3.9612 | 510 | 1.7225 | 0.7971 | 0.7766 | 0.8292 | 0.7884 |
| 2.7054 | 4.0388 | 520 | 1.6453 | 0.7983 | 0.7788 | 0.8346 | 0.7894 |
| 2.6069 | 4.1165 | 530 | 1.6340 | 0.8000 | 0.7807 | 0.8347 | 0.7910 |
| 2.5627 | 4.1942 | 540 | 1.6538 | 0.7971 | 0.7760 | 0.8337 | 0.7878 |
| 2.5555 | 4.2718 | 550 | 1.5779 | 0.7998 | 0.7785 | 0.8324 | 0.7906 |
| 2.5541 | 4.3495 | 560 | 1.5960 | 0.7945 | 0.7736 | 0.8329 | 0.7850 |
| 2.513 | 4.4272 | 570 | 1.5537 | 0.8025 | 0.7841 | 0.8368 | 0.7941 |
| 2.442 | 4.5049 | 580 | 1.5196 | 0.8034 | 0.7858 | 0.8380 | 0.7954 |
| 2.4763 | 4.5825 | 590 | 1.5009 | 0.8052 | 0.7870 | 0.8345 | 0.7965 |
| 2.4412 | 4.6602 | 600 | 1.4760 | 0.8098 | 0.7924 | 0.8391 | 0.8015 |
| 2.383 | 4.7379 | 610 | 1.4403 | 0.8088 | 0.7920 | 0.8395 | 0.8007 |
| 2.3731 | 4.8155 | 620 | 1.4123 | 0.8120 | 0.7956 | 0.8401 | 0.8039 |
| 2.3616 | 4.8932 | 630 | 1.4193 | 0.8105 | 0.7940 | 0.8369 | 0.8021 |
| 2.3311 | 4.9709 | 640 | 1.4220 | 0.8098 | 0.7934 | 0.8370 | 0.8016 |
| 2.3373 | 5.0485 | 650 | 1.3956 | 0.8081 | 0.7907 | 0.8367 | 0.7996 |
| 2.2879 | 5.1262 | 660 | 1.3375 | 0.8144 | 0.7976 | 0.8410 | 0.8062 |
| 2.299 | 5.2039 | 670 | 1.3431 | 0.8146 | 0.7967 | 0.8371 | 0.8061 |
| 2.2471 | 5.2816 | 680 | 1.3360 | 0.8151 | 0.7985 | 0.8389 | 0.8070 |
| 2.2419 | 5.3592 | 690 | 1.3139 | 0.8139 | 0.7977 | 0.8377 | 0.8058 |
| 2.2195 | 5.4369 | 700 | 1.3225 | 0.8151 | 0.7974 | 0.8395 | 0.8062 |
| 2.1901 | 5.5146 | 710 | 1.2797 | 0.8173 | 0.8001 | 0.8397 | 0.8087 |
| 2.1931 | 5.5922 | 720 | 1.2543 | 0.8192 | 0.8032 | 0.8423 | 0.8109 |
| 2.195 | 5.6699 | 730 | 1.2767 | 0.8209 | 0.8039 | 0.8405 | 0.8125 |
| 2.1413 | 5.7476 | 740 | 1.2735 | 0.8212 | 0.8053 | 0.8416 | 0.8132 |
| 2.1696 | 5.8252 | 750 | 1.2694 | 0.8149 | 0.7983 | 0.8358 | 0.8069 |
| 2.1387 | 5.9029 | 760 | 1.2532 | 0.8217 | 0.8062 | 0.8422 | 0.8136 |
| 2.1811 | 5.9806 | 770 | 1.2426 | 0.8197 | 0.8034 | 0.8417 | 0.8116 |
| 2.077 | 6.0583 | 780 | 1.2101 | 0.8243 | 0.8078 | 0.8464 | 0.8159 |
| 2.1099 | 6.1359 | 790 | 1.1947 | 0.8265 | 0.8108 | 0.8455 | 0.8186 |
| 2.0825 | 6.2136 | 800 | 1.1826 | 0.8241 | 0.8080 | 0.8455 | 0.8161 |
| 2.0933 | 6.2913 | 810 | 1.1934 | 0.8282 | 0.8128 | 0.8474 | 0.8207 |
| 2.0857 | 6.3689 | 820 | 1.1897 | 0.8258 | 0.8099 | 0.8465 | 0.8181 |
| 2.0881 | 6.4466 | 830 | 1.1666 | 0.8277 | 0.8124 | 0.8477 | 0.8199 |
| 2.074 | 6.5243 | 840 | 1.1815 | 0.8248 | 0.8081 | 0.8433 | 0.8167 |
| 2.0145 | 6.6019 | 850 | 1.1680 | 0.8292 | 0.8130 | 0.8473 | 0.8209 |
| 2.0778 | 6.6796 | 860 | 1.1565 | 0.8260 | 0.8094 | 0.8348 | 0.8178 |
| 1.9784 | 6.7573 | 870 | 1.1571 | 0.8345 | 0.8201 | 0.8529 | 0.8269 |
| 2.0595 | 6.8350 | 880 | 1.1554 | 0.8309 | 0.8165 | 0.8475 | 0.8234 |
| 2.0252 | 6.9126 | 890 | 1.1444 | 0.8282 | 0.8140 | 0.8476 | 0.8209 |
| 1.9708 | 6.9903 | 900 | 1.1478 | 0.8302 | 0.8158 | 0.8472 | 0.8224 |
| 2.0656 | 7.0680 | 910 | 1.1285 | 0.8324 | 0.8169 | 0.8485 | 0.8245 |
| 2.0086 | 7.1456 | 920 | 1.1289 | 0.8290 | 0.8148 | 0.8444 | 0.8219 |
| 2.0056 | 7.2233 | 930 | 1.1268 | 0.8280 | 0.8130 | 0.8470 | 0.8208 |
| 1.9498 | 7.3010 | 940 | 1.1246 | 0.8311 | 0.8158 | 0.8497 | 0.8234 |
| 2.0067 | 7.3786 | 950 | 1.1495 | 0.8285 | 0.8132 | 0.8440 | 0.8207 |
| 2.0171 | 7.4563 | 960 | 1.1168 | 0.8285 | 0.8138 | 0.8501 | 0.8209 |
| 1.9683 | 7.5340 | 970 | 1.1290 | 0.8314 | 0.8165 | 0.8500 | 0.8235 |
| 1.9771 | 7.6117 | 980 | 1.0982 | 0.8314 | 0.8153 | 0.8454 | 0.8233 |
| 2.0086 | 7.6893 | 990 | 1.1275 | 0.8294 | 0.8151 | 0.8491 | 0.8218 |
| 1.9854 | 7.7670 | 1000 | 1.1192 | 0.8256 | 0.8098 | 0.8426 | 0.8178 |
### 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"
] |
Heem2/Facemask-detection
|
<!-- 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. -->
# Face-Mask-Detection
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0239
- Accuracy: 0.9953
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1218 | 1.0 | 147 | 0.0251 | 0.9953 |
| 0.0186 | 1.99 | 294 | 0.0239 | 0.9953 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"withmask",
"withoutmask"
] |
Heem2/brain-tumor-classification
|
<!-- 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. -->
# Brain-Tumor-Classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0872
- Accuracy: 0.9758
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2074 | 1.0 | 44 | 0.8060 | 0.8128 |
| 0.4897 | 2.0 | 88 | 0.3008 | 0.9274 |
| 0.2462 | 3.0 | 132 | 0.2464 | 0.9331 |
| 0.1937 | 4.0 | 176 | 0.1918 | 0.9502 |
| 0.1523 | 5.0 | 220 | 0.1699 | 0.9502 |
| 0.1371 | 6.0 | 264 | 0.1372 | 0.9644 |
| 0.1104 | 7.0 | 308 | 0.1121 | 0.9708 |
| 0.1097 | 8.0 | 352 | 0.1220 | 0.9651 |
| 0.1015 | 9.0 | 396 | 0.1053 | 0.9737 |
| 0.0841 | 10.0 | 440 | 0.1142 | 0.9708 |
| 0.0839 | 11.0 | 484 | 0.1073 | 0.9708 |
| 0.0771 | 12.0 | 528 | 0.1156 | 0.9665 |
| 0.074 | 13.0 | 572 | 0.1203 | 0.9644 |
| 0.0652 | 14.0 | 616 | 0.0706 | 0.9858 |
| 0.0694 | 15.0 | 660 | 0.0984 | 0.9744 |
| 0.0596 | 16.0 | 704 | 0.0872 | 0.9758 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
Heem2/wound-image-classification
|
<!-- 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. -->
# Wound-Image-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1209
- Accuracy: 0.965
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0919 | 1.0 | 200 | 0.7780 | 0.76 |
| 0.6157 | 2.0 | 400 | 0.5695 | 0.7925 |
| 0.4894 | 3.0 | 600 | 0.3667 | 0.8775 |
| 0.3786 | 4.0 | 800 | 0.4436 | 0.8625 |
| 0.3142 | 5.0 | 1000 | 0.4412 | 0.8625 |
| 0.2636 | 6.0 | 1200 | 0.4430 | 0.86 |
| 0.198 | 7.0 | 1400 | 0.2760 | 0.9175 |
| 0.1456 | 8.0 | 1600 | 0.2211 | 0.93 |
| 0.1586 | 9.0 | 1800 | 0.3520 | 0.905 |
| 0.1307 | 10.0 | 2000 | 0.3188 | 0.9175 |
| 0.106 | 11.0 | 2200 | 0.3167 | 0.925 |
| 0.0975 | 12.0 | 2400 | 0.2633 | 0.92 |
| 0.0734 | 13.0 | 2600 | 0.1813 | 0.9525 |
| 0.0994 | 14.0 | 2800 | 0.2150 | 0.945 |
| 0.0622 | 15.0 | 3000 | 0.1757 | 0.955 |
| 0.0609 | 16.0 | 3200 | 0.1209 | 0.965 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"abrasions",
"bruises",
"burns",
"cut",
"diabetic wounds",
"laseration",
"normal",
"pressure wounds",
"surgical wounds",
"venous wounds"
] |
Heem2/New-plant-disease-classification
|
<!-- 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. -->
# New-plant-diseases-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0548
- Accuracy: 0.995
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5635 | 1.0 | 137 | 1.0712 | 0.9918 |
| 0.5929 | 2.0 | 275 | 0.3213 | 0.9923 |
| 0.2239 | 3.0 | 412 | 0.1482 | 0.9955 |
| 0.1256 | 4.0 | 550 | 0.1175 | 0.9882 |
| 0.0807 | 5.0 | 687 | 0.0648 | 0.9955 |
| 0.0561 | 6.0 | 825 | 0.0548 | 0.995 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"apple___apple_scab",
"apple___black_rot",
"corn_(maize)___healthy",
"grape___black_rot",
"grape___esca_(black_measles)",
"grape___leaf_blight_(isariopsis_leaf_spot)",
"grape___healthy",
"orange___haunglongbing_(citrus_greening)",
"peach___bacterial_spot",
"peach___healthy",
"pepper,_bell___bacterial_spot",
"pepper,_bell___healthy",
"apple___cedar_apple_rust",
"potato___early_blight",
"potato___late_blight",
"potato___healthy",
"raspberry___healthy",
"soybean___healthy",
"squash___powdery_mildew",
"strawberry___leaf_scorch",
"strawberry___healthy",
"tomato___bacterial_spot",
"tomato___early_blight",
"apple___healthy",
"tomato___late_blight",
"tomato___leaf_mold",
"tomato___septoria_leaf_spot",
"tomato___spider_mites two-spotted_spider_mite",
"tomato___target_spot",
"tomato___tomato_yellow_leaf_curl_virus",
"tomato___tomato_mosaic_virus",
"tomato___healthy",
"blueberry___healthy",
"cherry_(including_sour)___powdery_mildew",
"cherry_(including_sour)___healthy",
"corn_(maize)___cercospora_leaf_spot gray_leaf_spot",
"corn_(maize)___common_rust_",
"corn_(maize)___northern_leaf_blight"
] |
Heem2/sign-language-classification
|
<!-- 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. -->
# Indian-sign-language-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0327
- Accuracy: 0.9905
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2504 | 1.0 | 2137 | 0.0731 | 0.9800 |
| 0.0519 | 2.0 | 4274 | 0.0327 | 0.9905 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"1",
"2",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"3",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"4",
"v",
"w",
"x",
"y",
"z",
"5",
"6",
"7",
"8",
"9",
"a"
] |
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