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car_identified_model_12

Image Classification
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TensorBoard
Safetensors
mobilevit
Generated from Trainer
Eval Results
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metadata
license: other
base_model: apple/mobilevit-small
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - f1
  - accuracy
model-index:
  - name: car_identified_model_12
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: F1
            type: f1
            value: 0.9130434782608695
          - name: Accuracy
            type: accuracy
            value: 0.75

car_identified_model_12

This model is a fine-tuned version of apple/mobilevit-small on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5062
  • F1: 0.9130
  • Roc Auc: 0.9167
  • Accuracy: 0.75

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 300

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.2599 1.0 1 0.6888 0.4211 0.5417 0.0
0.2599 2.0 2 0.6888 0.3590 0.4792 0.0
0.2599 3.0 4 0.6878 0.4103 0.5208 0.0
0.2599 4.0 5 0.6872 0.45 0.5417 0.0
0.2599 5.0 6 0.6864 0.5116 0.5625 0.0
0.2599 6.0 8 0.6843 0.5238 0.5833 0.0833
0.2599 7.0 9 0.6828 0.5581 0.6042 0.1667
0.2599 8.0 10 0.6813 0.5238 0.5833 0.0833
0.2599 9.0 11 0.6803 0.45 0.5417 0.0833
0.2599 10.0 12 0.6794 0.4615 0.5625 0.0833
0.2599 11.0 14 0.6771 0.5128 0.6042 0.0833
0.2599 12.0 15 0.6762 0.5854 0.6458 0.0833
0.2599 13.0 16 0.6751 0.6341 0.6875 0.25
0.2599 14.0 18 0.6731 0.6667 0.7083 0.25
0.2599 15.0 19 0.6721 0.7273 0.75 0.3333
0.2599 16.0 20 0.6710 0.7273 0.75 0.3333
0.2599 17.0 21 0.6698 0.7273 0.75 0.3333
0.2599 18.0 22 0.6690 0.7273 0.75 0.3333
0.2599 19.0 24 0.6668 0.7273 0.75 0.3333
0.2599 20.0 25 0.6658 0.7273 0.75 0.3333
0.2599 21.0 26 0.6654 0.7273 0.75 0.3333
0.2599 22.0 28 0.6631 0.7273 0.75 0.3333
0.2599 23.0 29 0.6620 0.7273 0.75 0.3333
0.2599 24.0 30 0.6613 0.7273 0.75 0.3333
0.2599 25.0 31 0.6601 0.7273 0.75 0.3333
0.2599 26.0 32 0.6590 0.7273 0.75 0.3333
0.2599 27.0 34 0.6567 0.7273 0.75 0.3333
0.2599 28.0 35 0.6554 0.7273 0.75 0.3333
0.2599 29.0 36 0.6545 0.7273 0.75 0.3333
0.2599 30.0 38 0.6522 0.7273 0.75 0.3333
0.2599 31.0 39 0.6510 0.7273 0.75 0.3333
0.2599 32.0 40 0.6496 0.7273 0.75 0.3333
0.2599 33.0 41 0.6485 0.7273 0.75 0.3333
0.2599 34.0 42 0.6476 0.7273 0.75 0.3333
0.2599 35.0 44 0.6456 0.7273 0.75 0.3333
0.2599 36.0 45 0.6448 0.7556 0.7708 0.3333
0.2599 37.0 46 0.6437 0.7556 0.7708 0.3333
0.2599 38.0 48 0.6418 0.7727 0.7917 0.4167
0.2599 39.0 49 0.6410 0.7727 0.7917 0.4167
0.2599 40.0 50 0.6402 0.7727 0.7917 0.4167
0.2599 41.0 51 0.6392 0.7727 0.7917 0.4167
0.2599 42.0 52 0.6380 0.7907 0.8125 0.5
0.2599 43.0 54 0.6357 0.8182 0.8333 0.5833
0.2599 44.0 55 0.6349 0.8182 0.8333 0.5833
0.2599 45.0 56 0.6334 0.8000 0.8125 0.5
0.2599 46.0 58 0.6313 0.8000 0.8125 0.5
0.2599 47.0 59 0.6312 0.8000 0.8125 0.5
0.2599 48.0 60 0.6302 0.8182 0.8333 0.5833
0.2599 49.0 61 0.6291 0.8182 0.8333 0.5833
0.2599 50.0 62 0.6279 0.8182 0.8333 0.5833
0.2599 51.0 64 0.6254 0.8444 0.8542 0.6667
0.2599 52.0 65 0.6241 0.8696 0.875 0.6667
0.2599 53.0 66 0.6230 0.8696 0.875 0.6667
0.2599 54.0 68 0.6210 0.8936 0.8958 0.6667
0.2599 55.0 69 0.6200 0.9130 0.9167 0.75
0.2599 56.0 70 0.6192 0.9130 0.9167 0.75
0.2599 57.0 71 0.6175 0.8696 0.875 0.6667
0.2599 58.0 72 0.6171 0.9130 0.9167 0.75
0.2599 59.0 74 0.6157 0.9130 0.9167 0.75
0.2599 60.0 75 0.6147 0.9130 0.9167 0.75
0.2599 61.0 76 0.6140 0.9130 0.9167 0.75
0.2599 62.0 78 0.6120 0.9130 0.9167 0.75
0.2599 63.0 79 0.6107 0.9130 0.9167 0.75
0.2599 64.0 80 0.6088 0.8889 0.8958 0.6667
0.2599 65.0 81 0.6083 0.8889 0.8958 0.6667
0.2599 66.0 82 0.6076 0.9130 0.9167 0.75
0.2599 67.0 84 0.6058 0.9130 0.9167 0.75
0.2599 68.0 85 0.6042 0.9130 0.9167 0.75
0.2599 69.0 86 0.6034 0.9130 0.9167 0.75
0.2599 70.0 88 0.6017 0.9130 0.9167 0.75
0.2599 71.0 89 0.6001 0.9130 0.9167 0.75
0.2599 72.0 90 0.5998 0.9130 0.9167 0.75
0.2599 73.0 91 0.5993 0.9130 0.9167 0.75
0.2599 74.0 92 0.5985 0.9130 0.9167 0.75
0.2599 75.0 94 0.5949 0.9130 0.9167 0.75
0.2599 76.0 95 0.5938 0.9130 0.9167 0.75
0.2599 77.0 96 0.5941 0.9130 0.9167 0.75
0.2599 78.0 98 0.5925 0.9130 0.9167 0.75
0.2599 79.0 99 0.5922 0.9130 0.9167 0.75
0.2599 80.0 100 0.5911 0.9130 0.9167 0.75
0.2599 81.0 101 0.5905 0.9130 0.9167 0.75
0.2599 82.0 102 0.5893 0.9130 0.9167 0.75
0.2599 83.0 104 0.5872 0.9130 0.9167 0.75
0.2599 84.0 105 0.5855 0.9130 0.9167 0.75
0.2599 85.0 106 0.5852 0.9130 0.9167 0.75
0.2599 86.0 108 0.5835 0.9130 0.9167 0.75
0.2599 87.0 109 0.5835 0.9130 0.9167 0.75
0.2599 88.0 110 0.5831 0.9130 0.9167 0.75
0.2599 89.0 111 0.5822 0.9130 0.9167 0.75
0.2599 90.0 112 0.5815 0.9130 0.9167 0.75
0.2599 91.0 114 0.5803 0.9130 0.9167 0.75
0.2599 92.0 115 0.5779 0.9130 0.9167 0.75
0.2599 93.0 116 0.5770 0.9130 0.9167 0.75
0.2599 94.0 118 0.5758 0.9130 0.9167 0.75
0.2599 95.0 119 0.5748 0.9130 0.9167 0.75
0.2599 96.0 120 0.5744 0.9130 0.9167 0.75
0.2599 97.0 121 0.5739 0.9130 0.9167 0.75
0.2599 98.0 122 0.5730 0.9130 0.9167 0.75
0.2599 99.0 124 0.5710 0.9130 0.9167 0.75
0.2599 100.0 125 0.5703 0.9130 0.9167 0.75
0.2599 101.0 126 0.5687 0.9130 0.9167 0.75
0.2599 102.0 128 0.5664 0.9130 0.9167 0.75
0.2599 103.0 129 0.5657 0.9130 0.9167 0.75
0.2599 104.0 130 0.5657 0.9130 0.9167 0.75
0.2599 105.0 131 0.5639 0.9130 0.9167 0.75
0.2599 106.0 132 0.5642 0.9130 0.9167 0.75
0.2599 107.0 134 0.5617 0.9130 0.9167 0.75
0.2599 108.0 135 0.5604 0.9130 0.9167 0.75
0.2599 109.0 136 0.5598 0.9130 0.9167 0.75
0.2599 110.0 138 0.5579 0.9130 0.9167 0.75
0.2599 111.0 139 0.5566 0.8936 0.8958 0.75
0.2599 112.0 140 0.5561 0.9130 0.9167 0.75
0.2599 113.0 141 0.5567 0.8936 0.8958 0.75
0.2599 114.0 142 0.5571 0.8936 0.8958 0.75
0.2599 115.0 144 0.5536 0.8936 0.8958 0.75
0.2599 116.0 145 0.5537 0.8936 0.8958 0.75
0.2599 117.0 146 0.5510 0.8936 0.8958 0.75
0.2599 118.0 148 0.5496 0.8936 0.8958 0.75
0.2599 119.0 149 0.5492 0.9130 0.9167 0.75
0.2599 120.0 150 0.5492 0.9130 0.9167 0.75
0.2599 121.0 151 0.5490 0.9333 0.9375 0.75
0.2599 122.0 152 0.5483 0.9130 0.9167 0.75
0.2599 123.0 154 0.5464 0.9130 0.9167 0.75
0.2599 124.0 155 0.5477 0.9130 0.9167 0.75
0.2599 125.0 156 0.5469 0.9130 0.9167 0.75
0.2599 126.0 158 0.5445 0.9130 0.9167 0.75
0.2599 127.0 159 0.5450 0.9130 0.9167 0.75
0.2599 128.0 160 0.5436 0.9130 0.9167 0.75
0.2599 129.0 161 0.5433 0.9130 0.9167 0.75
0.2599 130.0 162 0.5417 0.9130 0.9167 0.75
0.2599 131.0 164 0.5416 0.9130 0.9167 0.75
0.2599 132.0 165 0.5403 0.9130 0.9167 0.75
0.2599 133.0 166 0.5404 0.9333 0.9375 0.75
0.2599 134.0 168 0.5390 0.9130 0.9167 0.75
0.2599 135.0 169 0.5389 0.9130 0.9167 0.75
0.2599 136.0 170 0.5376 0.9130 0.9167 0.75
0.2599 137.0 171 0.5374 0.9130 0.9167 0.75
0.2599 138.0 172 0.5372 0.9130 0.9167 0.75
0.2599 139.0 174 0.5348 0.9130 0.9167 0.75
0.2599 140.0 175 0.5356 0.9130 0.9167 0.75
0.2599 141.0 176 0.5355 0.9130 0.9167 0.75
0.2599 142.0 178 0.5323 0.9130 0.9167 0.75
0.2599 143.0 179 0.5323 0.9130 0.9167 0.75
0.2599 144.0 180 0.5332 0.9130 0.9167 0.75
0.2599 145.0 181 0.5320 0.9130 0.9167 0.75
0.2599 146.0 182 0.5315 0.9130 0.9167 0.75
0.2599 147.0 184 0.5301 0.9130 0.9167 0.75
0.2599 148.0 185 0.5284 0.9130 0.9167 0.75
0.2599 149.0 186 0.5296 0.9130 0.9167 0.75
0.2599 150.0 188 0.5301 0.9130 0.9167 0.75
0.2599 151.0 189 0.5282 0.9130 0.9167 0.75
0.2599 152.0 190 0.5263 0.9130 0.9167 0.75
0.2599 153.0 191 0.5263 0.9130 0.9167 0.75
0.2599 154.0 192 0.5270 0.9130 0.9167 0.75
0.2599 155.0 194 0.5274 0.9130 0.9167 0.75
0.2599 156.0 195 0.5264 0.9130 0.9167 0.75
0.2599 157.0 196 0.5281 0.9130 0.9167 0.75
0.2599 158.0 198 0.5232 0.9130 0.9167 0.75
0.2599 159.0 199 0.5218 0.9130 0.9167 0.75
0.2599 160.0 200 0.5212 0.9130 0.9167 0.75
0.2599 161.0 201 0.5214 0.9130 0.9167 0.75
0.2599 162.0 202 0.5222 0.9130 0.9167 0.75
0.2599 163.0 204 0.5210 0.9130 0.9167 0.75
0.2599 164.0 205 0.5207 0.9130 0.9167 0.75
0.2599 165.0 206 0.5210 0.9130 0.9167 0.75
0.2599 166.0 208 0.5195 0.9130 0.9167 0.75
0.2599 167.0 209 0.5217 0.9130 0.9167 0.75
0.2599 168.0 210 0.5207 0.9130 0.9167 0.75
0.2599 169.0 211 0.5190 0.9130 0.9167 0.75
0.2599 170.0 212 0.5181 0.9130 0.9167 0.75
0.2599 171.0 214 0.5183 0.9130 0.9167 0.75
0.2599 172.0 215 0.5183 0.9130 0.9167 0.75
0.2599 173.0 216 0.5202 0.9130 0.9167 0.75
0.2599 174.0 218 0.5180 0.9130 0.9167 0.75
0.2599 175.0 219 0.5189 0.9130 0.9167 0.75
0.2599 176.0 220 0.5157 0.9130 0.9167 0.75
0.2599 177.0 221 0.5168 0.9130 0.9167 0.75
0.2599 178.0 222 0.5161 0.9130 0.9167 0.75
0.2599 179.0 224 0.5160 0.9130 0.9167 0.75
0.2599 180.0 225 0.5178 0.9130 0.9167 0.75
0.2599 181.0 226 0.5166 0.9130 0.9167 0.75
0.2599 182.0 228 0.5146 0.9130 0.9167 0.75
0.2599 183.0 229 0.5143 0.9130 0.9167 0.75
0.2599 184.0 230 0.5128 0.9130 0.9167 0.75
0.2599 185.0 231 0.5111 0.9130 0.9167 0.75
0.2599 186.0 232 0.5119 0.9333 0.9375 0.75
0.2599 187.0 234 0.5116 0.9130 0.9167 0.75
0.2599 188.0 235 0.5099 0.9130 0.9167 0.75
0.2599 189.0 236 0.5108 0.9130 0.9167 0.75
0.2599 190.0 238 0.5102 0.9130 0.9167 0.75
0.2599 191.0 239 0.5103 0.9130 0.9167 0.75
0.2599 192.0 240 0.5102 0.9130 0.9167 0.75
0.2599 193.0 241 0.5102 0.9130 0.9167 0.75
0.2599 194.0 242 0.5110 0.9130 0.9167 0.75
0.2599 195.0 244 0.5093 0.9130 0.9167 0.75
0.2599 196.0 245 0.5102 0.9130 0.9167 0.75
0.2599 197.0 246 0.5094 0.9130 0.9167 0.75
0.2599 198.0 248 0.5082 0.9130 0.9167 0.75
0.2599 199.0 249 0.5069 0.9362 0.9375 0.8333
0.2599 200.0 250 0.5081 0.9130 0.9167 0.75
0.2599 201.0 251 0.5082 0.9130 0.9167 0.75
0.2599 202.0 252 0.5090 0.9130 0.9167 0.75
0.2599 203.0 254 0.5076 0.9130 0.9167 0.75
0.2599 204.0 255 0.5081 0.9130 0.9167 0.75
0.2599 205.0 256 0.5091 0.9130 0.9167 0.75
0.2599 206.0 258 0.5069 0.9130 0.9167 0.75
0.2599 207.0 259 0.5067 0.9130 0.9167 0.75
0.2599 208.0 260 0.5061 0.9362 0.9375 0.8333
0.2599 209.0 261 0.5076 0.9130 0.9167 0.75
0.2599 210.0 262 0.5082 0.9130 0.9167 0.75
0.2599 211.0 264 0.5069 0.9130 0.9167 0.75
0.2599 212.0 265 0.5074 0.9130 0.9167 0.75
0.2599 213.0 266 0.5077 0.9130 0.9167 0.75
0.2599 214.0 268 0.5058 0.9130 0.9167 0.75
0.2599 215.0 269 0.5063 0.9130 0.9167 0.75
0.2599 216.0 270 0.5059 0.9130 0.9167 0.75
0.2599 217.0 271 0.5059 0.9130 0.9167 0.75
0.2599 218.0 272 0.5050 0.9130 0.9167 0.75
0.2599 219.0 274 0.5056 0.9130 0.9167 0.75
0.2599 220.0 275 0.5053 0.9130 0.9167 0.75
0.2599 221.0 276 0.5058 0.9130 0.9167 0.75
0.2599 222.0 278 0.5051 0.9130 0.9167 0.75
0.2599 223.0 279 0.5047 0.9130 0.9167 0.75
0.2599 224.0 280 0.5041 0.9362 0.9375 0.8333
0.2599 225.0 281 0.5049 0.9130 0.9167 0.75
0.2599 226.0 282 0.5046 0.9362 0.9375 0.8333
0.2599 227.0 284 0.5078 0.9130 0.9167 0.75
0.2599 228.0 285 0.5064 0.9130 0.9167 0.75
0.2599 229.0 286 0.5065 0.9130 0.9167 0.75
0.2599 230.0 288 0.5066 0.9130 0.9167 0.75
0.2599 231.0 289 0.5058 0.9130 0.9167 0.75
0.2599 232.0 290 0.5067 0.9130 0.9167 0.75
0.2599 233.0 291 0.5079 0.9130 0.9167 0.75
0.2599 234.0 292 0.5085 0.9130 0.9167 0.75
0.2599 235.0 294 0.5073 0.9130 0.9167 0.75
0.2599 236.0 295 0.5023 0.9362 0.9375 0.8333
0.2599 237.0 296 0.5030 0.9362 0.9375 0.8333
0.2599 238.0 298 0.5044 0.9130 0.9167 0.75
0.2599 239.0 299 0.5055 0.9130 0.9167 0.75
0.2599 240.0 300 0.5062 0.9130 0.9167 0.75

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.14.1