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979b286 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | # TinyCLIP-ViT Inference
## Download checkpoints
Download a checkpoint from [Model Zoo](../README.md#model-zoo).
## Zero-shot inference on ImageNet-1k
Please change the paths to `imagenet-val` and `resume`.
### For manual weight inference checkpoint:
<details>
<summary>Evaluate TinyCLIP ViT-39M/16 + Text-19M (YFCC-15M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ViT-39M-16-Text-19M \
--eval \
--resume ./checkpoints/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-8M/16 + Text-3M (YFCC-15M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ViT-8M-16-Text-3M \
--eval \
--resume ./checkpoints/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ResNet-30M + Text-29M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ResNet-30M-Text-29M \
--eval \
--resume ./checkpoints/TinyCLIP-ResNet-30M-Text-29M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ResNet-19M + Text-19M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ResNet-19M-Text-19M \
--eval \
--resume ./checkpoints/TinyCLIP-ResNet-19M-Text-19M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-61M/32 + Text-29M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ViT-61M-32-Text-29M \
--eval \
--resume ./checkpoints/TinyCLIP-ViT-61M-32-Text-29M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-40M/32 + Text-19M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model TinyCLIP-ViT-40M-32-Text-19M \
--eval \
--resume ./checkpoints/TinyCLIP-ViT-40M-32-Text-19M-LAION400M.pt
</code></pre>
</details>
### For auto weight inference checkpoint:
<details>
<summary>Evaluate TinyCLIP ViT-63M/32 + Text-31M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model ViT-B-32 \
--prune-image \
--prune-text \
--eval \
--resume ./checkpoints/TinyCLIP-auto-ViT-63M-32-Text-31M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-45M/32 + Text-18M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model ViT-B-32 \
--prune-image \
--prune-text \
--eval \
--resume ./checkpoints/TinyCLIP-auto-ViT-45M-32-Text-18M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-22M/32 + Text-10M (LAION-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model ViT-B-32 \
--prune-image \
--prune-text \
--eval \
--resume ./checkpoints/TinyCLIP-auto-ViT-22M-32-Text-10M-LAION400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-63M/32 + Text-31M (LAION+YFCC-400M) </summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model ViT-B-32 \
--prune-image \
--prune-text \
--eval \
--resume ./checkpoints/TinyCLIP-auto-ViT-63M-32-Text-31M-LAIONYFCC400M.pt
</code></pre>
</details>
<details>
<summary>Evaluate TinyCLIP ViT-45M/32 + Text-18M (LAION+YFCC-400M)
</summary>
<pre><code>python -m torch.distributed.launch --use_env --nproc_per_node 8 src/training/main_for_test.py \
--imagenet-val ./ImageNet \
--model ViT-B-32 \
--prune-image \
--prune-text \
--eval \
--resume ./checkpoints/TinyCLIP-auto-ViT-45M-32-Text-18M-LAIONYFCC400M.pt
</code></pre>
</details>
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