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CLIPForImageClassification-v1

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metadata
base_model: openai/clip-vit-base-patch32
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: outputs
    results: []
license: apache-2.0
datasets:
  - Andron00e/CIFAR10-custom
language:
  - en
library_name: transformers

outputs

This model is a fine-tuned version of openai/clip-vit-base-patch32 on an CIFAR10 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8115
  • Accuracy: 0.8255

Model description

Training and evaluation data

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • train_batch_size: 10
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.7258 0.02 100 1.6999 0.8048
1.669 0.04 200 1.6798 0.8055
1.6704 0.06 300 1.6599 0.8053
1.6655 0.08 400 1.6407 0.8047
1.5754 0.1 500 1.6223 0.809
1.6159 0.12 600 1.6040 0.8068
1.5663 0.15 700 1.5858 0.8073
1.5426 0.17 800 1.5677 0.8095
1.5794 0.19 900 1.5506 0.808
1.5504 0.21 1000 1.5342 0.8035
1.554 0.23 1100 1.5179 0.802
1.4831 0.25 1200 1.5022 0.7972
1.4718 0.27 1300 1.4867 0.7955
1.5206 0.29 1400 1.4716 0.796
1.4534 0.31 1500 1.4567 0.7963
1.3932 0.33 1600 1.4427 0.7875
1.4635 0.35 1700 1.4289 0.789
1.4339 0.38 1800 1.4151 0.793
1.4492 0.4 1900 1.4016 0.7973
1.4369 0.42 2000 1.3881 0.8018
1.4007 0.44 2100 1.3754 0.801
1.3697 0.46 2200 1.3627 0.8025
1.3298 0.48 2300 1.3505 0.8048
1.2809 0.5 2400 1.3386 0.8068
1.2989 0.52 2500 1.3272 0.8067
1.2958 0.54 2600 1.3159 0.81
1.3072 0.56 2700 1.3048 0.8097
1.2545 0.58 2800 1.2943 0.809
1.2722 0.6 2900 1.2834 0.8112
1.2628 0.62 3000 1.2732 0.8102
1.2357 0.65 3100 1.2632 0.8105
1.3189 0.67 3200 1.2532 0.8093
1.2465 0.69 3300 1.2436 0.8097
1.2579 0.71 3400 1.2342 0.8087
1.1963 0.73 3500 1.2249 0.8085
1.1701 0.75 3600 1.2159 0.8092
1.2117 0.77 3700 1.2069 0.8113
1.1907 0.79 3800 1.1984 0.8112
1.1903 0.81 3900 1.1902 0.8115
1.2357 0.83 4000 1.1821 0.8115
1.1924 0.85 4100 1.1738 0.8117
1.1914 0.88 4200 1.1657 0.8133
1.1536 0.9 4300 1.1580 0.8148
1.1893 0.92 4400 1.1505 0.8158
1.1811 0.94 4500 1.1433 0.8158
1.0182 0.96 4600 1.1358 0.8165
1.0396 0.98 4700 1.1287 0.8158
1.1502 1.0 4800 1.1217 0.816
1.1764 1.02 4900 1.1147 0.8158
1.1508 1.04 5000 1.1080 0.8152
1.0518 1.06 5100 1.1015 0.8155
1.0648 1.08 5200 1.0952 0.816
1.1631 1.1 5300 1.0889 0.8153
1.0629 1.12 5400 1.0826 0.8152
1.1151 1.15 5500 1.0771 0.815
1.1377 1.17 5600 1.0711 0.8145
1.0353 1.19 5700 1.0652 0.8158
1.068 1.21 5800 1.0594 0.815
1.0834 1.23 5900 1.0538 0.8162
1.0002 1.25 6000 1.0483 0.8165
1.0024 1.27 6100 1.0428 0.817
1.0609 1.29 6200 1.0376 0.817
1.0901 1.31 6300 1.0324 0.816
1.0772 1.33 6400 1.0275 0.8173
0.9434 1.35 6500 1.0226 0.817
0.9692 1.38 6600 1.0178 0.8157
1.0461 1.4 6700 1.0131 0.8155
1.0583 1.42 6800 1.0086 0.8143
0.9369 1.44 6900 1.0042 0.8157
1.0685 1.46 7000 0.9998 0.8152
1.062 1.48 7100 0.9955 0.8153
1.0394 1.5 7200 0.9912 0.8142
1.031 1.52 7300 0.9870 0.8157
0.9556 1.54 7400 0.9829 0.8155
0.9846 1.56 7500 0.9789 0.8152
0.9995 1.58 7600 0.9750 0.8158
1.0273 1.6 7700 0.9711 0.8163
0.9383 1.62 7800 0.9674 0.817
0.951 1.65 7900 0.9634 0.8163
0.9457 1.67 8000 0.9598 0.8167
1.012 1.69 8100 0.9563 0.816
0.9683 1.71 8200 0.9529 0.8158
0.9582 1.73 8300 0.9495 0.8157
0.9005 1.75 8400 0.9461 0.8162
0.888 1.77 8500 0.9428 0.8175
0.9267 1.79 8600 0.9396 0.8168
0.9298 1.81 8700 0.9364 0.8168
1.0072 1.83 8800 0.9334 0.8167
0.9425 1.85 8900 0.9303 0.8158
0.9729 1.88 9000 0.9273 0.8168
0.9104 1.9 9100 0.9244 0.8175
0.9153 1.92 9200 0.9216 0.817
0.9115 1.94 9300 0.9188 0.8165
0.9079 1.96 9400 0.9161 0.8168
0.8453 1.98 9500 0.9133 0.8175
0.8323 2.0 9600 0.9107 0.817
0.9071 2.02 9700 0.9080 0.8183
0.9331 2.04 9800 0.9054 0.8185
0.886 2.06 9900 0.9029 0.8193
0.8562 2.08 10000 0.9006 0.8183
0.8904 2.1 10100 0.8980 0.8193
0.8247 2.12 10200 0.8956 0.8188
0.8114 2.15 10300 0.8934 0.8202
0.96 2.17 10400 0.8912 0.8198
0.9326 2.19 10500 0.8889 0.8198
0.8057 2.21 10600 0.8867 0.8195
0.8266 2.23 10700 0.8846 0.8188
0.7909 2.25 10800 0.8823 0.82
0.886 2.27 10900 0.8803 0.8192
0.8691 2.29 11000 0.8783 0.8193
0.8676 2.31 11100 0.8763 0.8187
0.8147 2.33 11200 0.8744 0.819
0.7723 2.35 11300 0.8725 0.8195
0.9222 2.38 11400 0.8705 0.8188
0.9692 2.4 11500 0.8687 0.8195
0.8792 2.42 11600 0.8669 0.8188
0.939 2.44 11700 0.8650 0.8193
0.9093 2.46 11800 0.8633 0.8188
0.7794 2.48 11900 0.8616 0.8182
0.8572 2.5 12000 0.8599 0.8182
0.9035 2.52 12100 0.8582 0.8185
0.8063 2.54 12200 0.8566 0.8193
0.8935 2.56 12300 0.8550 0.8195
0.7991 2.58 12400 0.8535 0.8192
0.856 2.6 12500 0.8520 0.8195
0.8374 2.62 12600 0.8505 0.8197
0.8418 2.65 12700 0.8490 0.8203
0.9232 2.67 12800 0.8475 0.8208
0.8335 2.69 12900 0.8462 0.8207
0.8659 2.71 13000 0.8449 0.8205
0.9798 2.73 13100 0.8435 0.8205
0.7288 2.75 13200 0.8423 0.8205
0.9086 2.77 13300 0.8411 0.821
0.7912 2.79 13400 0.8398 0.8205
0.8675 2.81 13500 0.8386 0.8202
0.8045 2.83 13600 0.8374 0.8198
0.8421 2.85 13700 0.8362 0.8202
0.7453 2.88 13800 0.8350 0.8202
0.7348 2.9 13900 0.8339 0.8203
0.8977 2.92 14000 0.8328 0.8205
0.859 2.94 14100 0.8318 0.821
0.8571 2.96 14200 0.8307 0.8212
0.8158 2.98 14300 0.8297 0.8215
0.8635 3.0 14400 0.8287 0.8215
0.9095 3.02 14500 0.8277 0.8215
0.8491 3.04 14600 0.8268 0.8217
0.9136 3.06 14700 0.8259 0.8223
0.8652 3.08 14800 0.8250 0.8218
0.9299 3.1 14900 0.8242 0.8215
0.8259 3.12 15000 0.8233 0.8215
0.775 3.15 15100 0.8225 0.8222
0.801 3.17 15200 0.8217 0.8217
0.8535 3.19 15300 0.8209 0.8215
0.7973 3.21 15400 0.8202 0.8217
0.8937 3.23 15500 0.8195 0.8213
0.7632 3.25 15600 0.8188 0.821
0.8117 3.27 15700 0.8181 0.8212
0.8941 3.29 15800 0.8174 0.8217
0.802 3.31 15900 0.8168 0.8225
0.8303 3.33 16000 0.8161 0.8217
0.8264 3.35 16100 0.8155 0.8218
0.8411 3.38 16200 0.8149 0.8213
0.9378 3.4 16300 0.8143 0.8218
0.8514 3.42 16400 0.8138 0.8217
0.7313 3.44 16500 0.8133 0.8222
0.8238 3.46 16600 0.8128 0.8218
0.7876 3.48 16700 0.8123 0.8222
0.8364 3.5 16800 0.8118 0.8222
0.7049 3.52 16900 0.8114 0.8222
0.9101 3.54 17000 0.8109 0.8218
0.7984 3.56 17100 0.8105 0.822
0.85 3.58 17200 0.8101 0.8218
0.8677 3.6 17300 0.8098 0.822
0.8797 3.62 17400 0.8094 0.8218
0.7847 3.65 17500 0.8091 0.8222
0.8415 3.67 17600 0.8088 0.8218
0.8702 3.69 17700 0.8085 0.8222
0.8979 3.71 17800 0.8082 0.8222
0.8387 3.73 17900 0.8080 0.8222
0.8467 3.75 18000 0.8077 0.822
0.8729 3.77 18100 0.8075 0.822
0.8291 3.79 18200 0.8073 0.8222
0.7897 3.81 18300 0.8072 0.8222
0.8039 3.83 18400 0.8070 0.822
0.771 3.85 18500 0.8069 0.8223
0.7704 3.88 18600 0.8067 0.8223
0.7695 3.9 18700 0.8066 0.8223
0.8958 3.92 18800 0.8066 0.8223
0.8342 3.94 18900 0.8065 0.8223
0.8725 3.96 19000 0.8064 0.8225
0.8657 3.98 19100 0.8064 0.8225
0.779 4.0 19200 0.8064 0.8225

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0